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\ No newline at end of file diff --git a/metadata.csv b/metadata.csv new file mode 100644 index 0000000..f85a48f --- /dev/null +++ b/metadata.csv @@ -0,0 +1 @@ +cel,ShortName,ShortNameOld,Well,Plate,Timepoint,Treatment,CB315vUNT,METvUNT,CUB2vUNT,VANvUNT,GENTvUNT,CB315vMET,CB315vCUB2,CB315vVAN,CB315time,METtime,CUB2time,VANtime,gDNAship,gDNAlab,PCRyield,PCRperf CB315D0M1,CB315D0M1,CB-B-M 1,A1,1,1.Baseline,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,NA,NA,1200,99.87,,fail CB315D0M2,CB315D0M2,CB-B-M 2,B1,1,1.Baseline,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,NA,NA,302.4,23.89,, CB315D0M3,CB315D0M3,CB-B-M 3,C1,1,1.Baseline,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,NA,NA,391.2,24.14,, CB315D0M4,CB315D0M4,CB-B-M 4,D1,1,1.Baseline,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,NA,NA,256.8,24.44,, CB315D0M5,CB315D0M5,CB-B-M 5,E1,1,1.Baseline,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,NA,NA,350.4,37.33,, CB315D5M1,CB315D5M1,CB-D 5 -M 1,F1,1,2.Day5,CB183315,CB183315,NA,NA,NA,NA,CB183315,CB183315,CB183315,2.Day5,NA,NA,NA,261.6,28.19,, CB315D5M2,CB315D5M2,CB-D 5 -M 2,G1,1,2.Day5,CB183315,CB183315,NA,NA,NA,NA,CB183315,CB183315,CB183315,2.Day5,NA,NA,NA,376.8,26.78,, CB315D5M3,CB315D5M3,CB-D 5 -M 3,H1,1,2.Day5,CB183315,CB183315,NA,NA,NA,NA,CB183315,CB183315,CB183315,2.Day5,NA,NA,NA,804,81.77,, CB315D5M4,CB315D5M4,CB-D 5 -M 4,A2,1,2.Day5,CB183315,CB183315,NA,NA,NA,NA,CB183315,CB183315,CB183315,2.Day5,NA,NA,NA,277.2,21.68,, CB315D5M5,CB315D5M5,CB-D 5 -M 5,B2,1,2.Day5,CB183315,CB183315,NA,NA,NA,NA,CB183315,CB183315,CB183315,2.Day5,NA,NA,NA,499.2,53.41,, CB315D10M1,CB315D10M1,CB-D 10 -M 1,C2,1,3.Day10,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,498,76.86,, CB315D10M2,CB315D10M2,CB-D 10 -M 2,D2,1,3.Day10,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,439.2,75.26,, CB315D10M3,CB315D10M3,CB-D 10 -M 3,E2,1,3.Day10,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,382.8,38.74,, CB315D10M4,CB315D10M4,CB-D 10 -M 4,F2,1,3.Day10,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,482.4,103.06,, CB315D10M5,CB315D10M5,CB-D 10 -M 5,G2,1,3.Day10,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,255.6,31.69,, CB315D15M1,CB315D15M1,CB-D 15-M 1,H2,1,4.Day15,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,NA,NA,258,20.46,, CB315D15M2,CB315D15M2,CB-D 15-M 2,A3,1,4.Day15,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,NA,NA,474,54.7,, CB315D15M3,CB315D15M3,CB-D 15-M 3,B3,1,4.Day15,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,NA,NA,242.4,22.48,, CB315D15M4,CB315D15M4,CB-D 15-M 4,C3,1,4.Day15,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,NA,NA,360,53.29,, CB315D15M5,CB315D15M5,CB-D 15-M 5,D3,1,4.Day15,CB183315,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,NA,NA,842.4,116.87,, METD0M11,METD0M11,MET -B-M 11,E3,1,1.Baseline,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,NA,319.2,21.31,, METD0M12,METD0M12,MET -B-M 12,F3,1,1.Baseline,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,NA,477.6,45.13,, METD0M13,METD0M13,MET -B-M 13,G3,1,1.Baseline,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,NA,333.6,35.18,, METD0M14,METD0M14,MET -B-M 14,H3,1,1.Baseline,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,NA,765.6,60.29,, METD5M11,METD5M11,MET -D 5-M 11,A4,1,2.Day5,METRONIDAZOLE,NA,METRONIDAZOLE,NA,NA,NA,METRONIDAZOLE,NA,NA,NA,2.Day5,NA,NA,570,108.16,, METD5M12,METD5M12,MET -D 5-M 12,B4,1,2.Day5,METRONIDAZOLE,NA,METRONIDAZOLE,NA,NA,NA,METRONIDAZOLE,NA,NA,NA,2.Day5,NA,NA,384,31.5,,fail METD5M13,METD5M13,MET -D 5-M 13,C4,1,2.Day5,METRONIDAZOLE,NA,METRONIDAZOLE,NA,NA,NA,METRONIDAZOLE,NA,NA,NA,2.Day5,NA,NA,286.4,37.64,, METD5M14,METD5M14,MET -D 5-M 14,D4,1,2.Day5,METRONIDAZOLE,NA,METRONIDAZOLE,NA,NA,NA,METRONIDAZOLE,NA,NA,NA,2.Day5,NA,NA,768,62.74,,fail METD5M15,METD5M15,MET -D 5-M 15,E4,1,2.Day5,METRONIDAZOLE,NA,METRONIDAZOLE,NA,NA,NA,METRONIDAZOLE,NA,NA,NA,2.Day5,NA,NA,454.8,60.04,, METD10M11,METD10M11,MET -D 10-M 11,F4,1,3.Day10,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,816,140.38,, METD10M12,METD10M12,MET -D 10-M 12,G4,1,3.Day10,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,265.5,16.4,, METD10M13,METD10M13,MET -D 10-M 13,H4,1,3.Day10,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,265.5,21.07,,fail METD10M14,METD10M14,MET -D 10-M 14,A5,1,3.Day10,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,522,86.61,, METD10M15,METD10M15,MET -D 10-M 15,B5,1,3.Day10,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,844.8,112.33,, METD15M11,METD15M11,MET -D 15-M 11,C5,1,4.Day15,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,NA,655.2,75.08,, METD15M12,METD15M12,MET -D 15-M 12,D5,1,4.Day15,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,NA,292.8,20.95,, METD15M13,METD15M13,MET -D 15-M 13,E5,1,4.Day15,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,NA,268.8,22.66,, METD15M14,METD15M14,MET -D 15-M 14,F5,1,4.Day15,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,NA,396,32.3,, METD15M15,METD15M15,MET -D 15-M 15,G5,1,4.Day15,METRONIDAZOLE,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,NA,256.8,20.39,, CUB2D0M6,CUB2D0M6,CUB2 -B-M 6,H5,1,1.Baseline,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,297.6,43.04,,fail CUB2D0M7,CUB2D0M7,CUB2 -B-M 7,A6,1,1.Baseline,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,261.6,110.3,, CUB2D0M8,CUB2D0M8,CUB2 -B-M 8,B6,1,1.Baseline,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,544.8,43.59,, CUB2D0M9,CUB2D0M9,CUB2 -B-M 9,C6,1,1.Baseline,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,262.8,120.19,, CUB2D0M10,CUB2D0M10,CUB2 -B-M 10,D6,1,1.Baseline,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,NA,607.2,107.48,, CUB2D5M6,CUB2D5M6,CUB2 -D 5-M 6,E6,1,2.Day5,CUB2,NA,NA,CUB2,NA,NA,NA,CUB2,NA,NA,NA,2.Day5,NA,572.4,74.4,, CUB2D5M7,CUB2D5M7,CUB2 -D 5-M 7,F6,1,2.Day5,CUB2,NA,NA,CUB2,NA,NA,NA,CUB2,NA,NA,NA,2.Day5,NA,518.4,38.68,, CUB2D5M8,CUB2D5M8,CUB2 -D 5-M 8,G6,1,2.Day5,CUB2,NA,NA,CUB2,NA,NA,NA,CUB2,NA,NA,NA,2.Day5,NA,890.4,78.82,, CUB2D5M9,CUB2D5M9,CUB2 -D 5-M 9,H6,1,2.Day5,CUB2,NA,NA,CUB2,NA,NA,NA,CUB2,NA,NA,NA,2.Day5,NA,415.2,58.44,,fail CUB2D5M10,CUB2D5M10,CUB2 -D 5-M 10,A7,1,2.Day5,CUB2,NA,NA,CUB2,NA,NA,NA,CUB2,NA,NA,NA,2.Day5,NA,261.6,42.98,, CUB2D10M6,CUB2D10M6,CUB2 -D 10-M 6,B7,1,3.Day10,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,373.2,40.03,, CUB2D10M7,CUB2D10M7,CUB2 -D 10-M 7,C7,1,3.Day10,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,562.8,84.71,, CUB2D10M8,CUB2D10M8,CUB2 -D 10-M 8,D7,1,3.Day10,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,547.2,80.29,, CUB2D10M9,CUB2D10M9,CUB2 -D 10-M 9,E7,1,3.Day10,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,302.4,43.9,, CUB2D10M10,CUB2D10M10,CUB2 -D 10-M 10,F7,1,3.Day10,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,547.2,103.12,, CUB2D15M6,CUB2D15M6,CUB2 -D 15-M 6,G7,1,4.Day15,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,376.8,52.98,, CUB2D15M7,CUB2D15M7,CUB2 -D 15-M 7,H7,1,4.Day15,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,684,41.51,,fail CUB2D15M8,CUB2D15M8,CUB2 -D 15-M 8,A8,1,4.Day15,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,513.6,74.83,, CUB2D15M9,CUB2D15M9,CUB2 -D 15-M 9,B8,1,4.Day15,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,586.8,104.52,, CUB2D15M10,CUB2D15M10,CUB2 -D 15-M 10,C8,1,4.Day15,CUB2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,NA,556.8,86.96,,fail VAND0M16,VAND0M16,VAN -B-M 16,D8,1,1.Baseline,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,638.4,57.72,, VAND0M17,VAND0M17,VAN -B-M 17,E8,1,1.Baseline,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,279.6,28.36,, VAND0M18,VAND0M18,VAN -B-M 18,F8,1,1.Baseline,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,252,18.02,, VAND0M19,VAND0M19,VAN -B-M 19,G8,1,1.Baseline,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,470.4,39.88,, VAND0M20,VAND0M20,VAN -B-M 20,H8,1,1.Baseline,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1.Baseline,355.2,27.52,, VAND5M16,VAND5M16,VAN -D 5-M 16,A9,1,2.Day5,VANCOMYCIN,NA,NA,NA,VANCOMYCIN,NA,NA,NA,VANCOMYCIN,NA,NA,NA,2.Day5,254.4,18.74,, VAND5M17,VAND5M17,VAN -D 5-M 17,B9,1,2.Day5,VANCOMYCIN,NA,NA,NA,VANCOMYCIN,NA,NA,NA,VANCOMYCIN,NA,NA,NA,2.Day5,434.4,49.44,, VAND5M18,VAND5M18,VAN -D 5-M 18,C9,1,2.Day5,VANCOMYCIN,NA,NA,NA,VANCOMYCIN,NA,NA,NA,VANCOMYCIN,NA,NA,NA,2.Day5,324,47.71,, VAND5M19,VAND5M19,VAN -D 5-M 19,D9,1,2.Day5,VANCOMYCIN,NA,NA,NA,VANCOMYCIN,NA,NA,NA,VANCOMYCIN,NA,NA,NA,2.Day5,324,66.27,, VAND5M20,VAND5M20,VAN -D 5-M 20,E9,1,2.Day5,VANCOMYCIN,NA,NA,NA,VANCOMYCIN,NA,NA,NA,VANCOMYCIN,NA,NA,NA,2.Day5,450,60.4,, VAND10M16,VAND10M16,VAN -D 10-M 16,F9,1,3.Day10,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,336,38.54,, VAND10M17,VAND10M17,VAN -D 10-M 17,G9,1,3.Day10,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,360,44.24,, VAND10M18,VAND10M18,VAN -D 10-M 18,H9,1,3.Day10,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,529.2,101.05,, VAND10M19,VAND10M19,VAN -D 10-M 19,A10,1,3.Day10,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,302.4,27.58,, VAND10M20,VAND10M20,VAN -D 10-M 20,B10,1,3.Day10,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,214.5,13.1,, VAND15M16,VAND15M16,VAN -D 15-M 16,C10,1,4.Day15,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,372,41.67,, VAND15M17,VAND15M17,VAN -D 15-M 17,D10,1,4.Day15,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,385.2,58.16,, VAND15M18,VAND15M18,VAN -D 15-M 18,E10,1,4.Day15,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,295.2,7.17,, VAND15M19,VAND15M19,VAN -D 15-M 19,F10,1,4.Day15,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,124.1,57.32,, VAND15M20,VAND15M20,VAN -D 15-M 20,G10,1,4.Day15,VANCOMYCIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.Day15,648,51.62,, GENTD0M21,GENTD0M21,GEN -B-M 21,H10,1,1.Baseline,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,411.6,43.07,, GENTD0M22,GENTD0M22,GEN -B-M 22,A11,1,1.Baseline,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,426,43.23,, GENTD0M23,GENTD0M23,GEN -B-M 23,B11,1,1.Baseline,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,250.8,20.48,, GENTD0M24,GENTD0M24,GEN -B-M 24,C11,1,1.Baseline,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,336,32,, GENTD0M25,GENTD0M25,GEN -B-M 25,D11,1,1.Baseline,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,343.2,33.45,, GENTD5M22,GENTD5M22,GEN -D 5-M 22,E11,1,2.Day5,GENTAMICIN,NA,NA,NA,NA,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,448.8,44.24,, GENTD5M23,GENTD5M23,GEN -D 5-M 23,F11,1,2.Day5,GENTAMICIN,NA,NA,NA,NA,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,474,94.68,, GENTD5M24,GENTD5M24,GEN -D 5-M 24,G11,1,2.Day5,GENTAMICIN,NA,NA,NA,NA,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,436.8,61.13,, GENTD5M25,GENTD5M25,GEN -D 5-M 25,H11,1,2.Day5,GENTAMICIN,NA,NA,NA,NA,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,482.4,82.32,, GENTD10M23,GENTD10M23,GEN -D 10-M 23,A12,1,3.Day10,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,466.8,55.14,, GENTD10M24,GENTD10M24,GEN -D 10-M 24,B12,1,3.Day10,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,328.8,28.36,,fail GENTD10M25,GENTD10M25,GEN -D 10-M 25,C12,1,3.Day10,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,367.2,65.88,, GENTD15M23,GENTD15M23,GEN -D 15-M 23,D12,1,4.Day15,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,537.6,48.55,, GENTD15M24,GENTD15M24,GEN -D 15-M 24,E12,1,4.Day15,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,508.8,53.75,, GENTD15M25,GENTD15M25,GEN -D 15-M 25,F12,1,4.Day15,GENTAMICIN,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,378,41.78,, UNTD0M26,UNTD0M26,C -B-M 26,A1,2,1.Baseline,Untreated,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,468,39.1,, UNTD0M27,UNTD0M27,C -B-M 27,B1,2,1.Baseline,Untreated,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,332.4,33.84,, UNTD0M28,UNTD0M28,C -B-M 28,C1,2,1.Baseline,Untreated,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,375.6,50.28,, UNTD0M29,UNTD0M29,C -B-M 29,D1,2,1.Baseline,Untreated,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,284.4,33.28,, UNTD0M30,UNTD0M30,C -B-M 30,E1,2,1.Baseline,Untreated,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,441.6,89.81,, UNTD5M26,UNTD5M26,C -D 5-M 26,F1,2,2.Day5,Untreated,Untreated,Untreated,Untreated,Untreated,Untreated,NA,NA,NA,NA,NA,NA,NA,376.8,28.08,, UNTD5M27,UNTD5M27,C -D 5-M 27,G1,2,2.Day5,Untreated,Untreated,Untreated,Untreated,Untreated,Untreated,NA,NA,NA,NA,NA,NA,NA,351.6,40.72,, UNTD5M28,UNTD5M28,C -D 5-M 28,H1,2,2.Day5,Untreated,Untreated,Untreated,Untreated,Untreated,Untreated,NA,NA,NA,NA,NA,NA,NA,272.4,34.46,, UNTD5M29,UNTD5M29,C -D 5-M 29,A2,2,2.Day5,Untreated,Untreated,Untreated,Untreated,Untreated,Untreated,NA,NA,NA,NA,NA,NA,NA,427.2,49.89,, UNTD5M30,UNTD5M30,C -D 5-M 30,B2,2,2.Day5,Untreated,Untreated,Untreated,Untreated,Untreated,Untreated,NA,NA,NA,NA,NA,NA,NA,369.6,44.97,, UNTD10M26,UNTD10M26,C -D 10-M 26,C2,2,3.Day10,Untreated,Untreated,Untreated,Untreated,Untreated,Untreated,NA,NA,NA,NA,NA,NA,NA,253.2,25.56,, UNTD10M27,UNTD10M27,C -D 10-M 27,D2,2,3.Day10,Untreated,Untreated,Untreated,Untreated,Untreated,Untreated,NA,NA,NA,NA,NA,NA,NA,775.2,40.21,, UNTD10M28,UNTD10M28,C -D 10-M 28,E2,2,3.Day10,Untreated,Untreated,Untreated,Untreated,Untreated,Untreated,NA,NA,NA,NA,NA,NA,NA,327.6,34.96,, UNTD10M29,UNTD10M29,C -D 10-M 29,F2,2,3.Day10,Untreated,Untreated,Untreated,Untreated,Untreated,Untreated,NA,NA,NA,NA,NA,NA,NA,549.6,45.86,, UNTD10M30,UNTD10M30,C -D 10-M 30,G2,2,3.Day10,Untreated,Untreated,Untreated,Untreated,Untreated,Untreated,NA,NA,NA,NA,NA,NA,NA,361.2,52.57,, UNTD15M26,UNTD15M26,C -D 15-M 26,H2,2,4.Day15,Untreated,Untreated,Untreated,Untreated,Untreated,Untreated,NA,NA,NA,NA,NA,NA,NA,456,17.29,, UNTD15M28,UNTD15M28,C -D 15-M 28,A3,2,4.Day15,Untreated,Untreated,Untreated,Untreated,Untreated,Untreated,NA,NA,NA,NA,NA,NA,NA,823.2,92.1,, ,,,,,,,,,,,,,,,,,,,,,, diff --git a/notes.txt b/notes.txt new file mode 100644 index 0000000..ae1ef19 --- /dev/null +++ b/notes.txt @@ -0,0 +1,77 @@ +notes.txt + +justin212k@ip-10-61-11-160:/Data_500/Cubi_Chesnel_EPAN12_0120/plate2$ stitch_reads.sh + +etc. + +duplicates exist, beware: + +justin212k@ip-10-61-11-160:/Data_500/Cubi_Chesnel_EPAN12_0120/repeatsamples$ 'ls' | xargs -I {} -n 1 find .. -iname {} +../repeatsamples/Sample_CBBM1 +../repeatsamples/Sample_CUB2BM6 +../repeatsamples/Sample_CUB2D15M10 +../plate2/Sample_CUB2D15M10 +../repeatsamples/Sample_CUB2D15M7 +../plate1/Sample_CUB2D15M7 +../repeatsamples/Sample_CUB2D5M9 +../plate1/Sample_CUB2D5M9 +../repeatsamples/Sample_GEND10M24 +../repeatsamples/Sample_METD10M13 +../plate1/Sample_METD10M13 +../repeatsamples/Sample_METD5M12 +../repeatsamples/Sample_METD5M14 +../plate1/Sample_METD5M14 + +changed ntc to ntc1, ntc2 for each plate. + +cat hi.txt | sort | uniq -c +yields 2 ntc and dupes of: + +CUB2D15M10 plate 2 +CUB2D15M7 1 +CUB2D5M9 1 +METD10M13 1 +METD5M14 1 + +all of which have one copy in repeat samples + +made special samname_to_bc.txt for repeatsamples samples, those were using the old sample names + +remove fails from data +CUB2D0M6_splat.fna +CUB2D15M10_splat.fna +CUB2D15M7_splat.fna +CUB2D5M9_splat.fna +METD10M13_splat.fna +METD5M14_splat.fna + +justin212k@ip-10-61-11-160:/Data_500/Cubi_Chesnel_EPAN12_0120$ 'ls' -R | grep splat | grep -v barcode_errors | sort | uniq -c | sort +yields +... + 1 VAND5M20_splat.fna + 2 CUB2D0M6_splat.fna + 2 CUB2D15M10_splat.fna + 2 CUB2D15M7_splat.fna + 2 CUB2D5M9_splat.fna + 2 GENTD10M24_splat.fna + 2 METD10M13_splat.fna + 2 METD5M14_splat.fna + +those 7 samples put in originals_of_repeated_samples/ + +additionally, METD5M12 and CB315D0M1 have no originals, but are in repeated samples + + +justin212k@ip-10-61-11-160:/Data_500/Cubi_Chesnel_EPAN12_0120$ cat plate1/seqs.trim.fna plate2/seqs.trim.fna repeatsamples/seqs.trim.fna > combined_plates_and_repeats/seqs.trim.fna + +from in combined_plates_and_repeats, make_otu_table.sh + +Seqs/sample detail: + VAND15M19: 562.0 + GENTD5M22: 1788.0 + VAND10M18: 8907.0 + GENTD5M23: 31345.0 + +... + +remove top 3 \ No newline at end of file diff --git a/params_CB315Time.R b/params_CB315Time.R new file mode 100644 index 0000000..61144ab --- /dev/null +++ b/params_CB315Time.R @@ -0,0 +1,228 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + + useHelv <- FALSE + + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + #within_between_analysis <- TRUE + alternate_pch_Thld <-1000 + +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('CB315time'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('CB315time') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +# contVar <- NULL +contVar <- c('gDNAlab') + +# === normalization paramters === +normalize <- T # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- F + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- TRUE + filter3.num.permutes <- 100 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- TRUE + useANOVA <- TRUE + useWelch <- F + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- T # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- FALSE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========## diff --git a/params_CB315vCUB2.R b/params_CB315vCUB2.R new file mode 100644 index 0000000..8937f22 --- /dev/null +++ b/params_CB315vCUB2.R @@ -0,0 +1,228 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + + useHelv <- FALSE + + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + #within_between_analysis <- TRUE + alternate_pch_Thld <-1000 + +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('CB315vCUB2'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('CB315vCUB2') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +# contVar <- NULL +contVar <- c('gDNAlab') + +# === normalization paramters === +normalize <- T # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- F + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- TRUE + filter3.num.permutes <- 100 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- TRUE + useANOVA <- TRUE + useWelch <- F + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- T # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- FALSE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========## diff --git a/params_CB315vMET.R b/params_CB315vMET.R new file mode 100644 index 0000000..f7ec817 --- /dev/null +++ b/params_CB315vMET.R @@ -0,0 +1,228 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + + useHelv <- FALSE + + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + #within_between_analysis <- TRUE + alternate_pch_Thld <-1000 + +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('CB315vMET'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('CB315vMET') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +# contVar <- NULL +contVar <- c('gDNAlab') + +# === normalization paramters === +normalize <- T # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- F + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- TRUE + filter3.num.permutes <- 100 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- TRUE + useANOVA <- TRUE + useWelch <- F + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- T # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- FALSE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========## diff --git a/params_CB315vUNT.R b/params_CB315vUNT.R new file mode 100644 index 0000000..79a9849 --- /dev/null +++ b/params_CB315vUNT.R @@ -0,0 +1,228 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + + useHelv <- FALSE + + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + #within_between_analysis <- TRUE + alternate_pch_Thld <-1000 + +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('CB315vUNT'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('CB315vUNT') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +# contVar <- NULL +contVar <- c('gDNAlab') + +# === normalization paramters === +normalize <- T # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- F + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- TRUE + filter3.num.permutes <- 100 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- TRUE + useANOVA <- TRUE + useWelch <- F + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- T # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- FALSE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========## diff --git a/params_CB315vVAN.R b/params_CB315vVAN.R new file mode 100644 index 0000000..6f54840 --- /dev/null +++ b/params_CB315vVAN.R @@ -0,0 +1,228 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + + useHelv <- FALSE + + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + #within_between_analysis <- TRUE + alternate_pch_Thld <-1000 + +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('CB315vVAN'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('CB315vVAN') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +# contVar <- NULL +contVar <- c('gDNAlab') + +# === normalization paramters === +normalize <- T # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- F + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- TRUE + filter3.num.permutes <- 100 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- TRUE + useANOVA <- TRUE + useWelch <- F + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- T # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- FALSE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========## diff --git a/params_CUB2Time.R b/params_CUB2Time.R new file mode 100644 index 0000000..2de0bc6 --- /dev/null +++ b/params_CUB2Time.R @@ -0,0 +1,228 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + + useHelv <- FALSE + + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + #within_between_analysis <- TRUE + alternate_pch_Thld <-1000 + +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('CUB2time'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('CUB2time') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +# contVar <- NULL +contVar <- c('gDNAlab') + +# === normalization paramters === +normalize <- T # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- F + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- TRUE + filter3.num.permutes <- 100 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- TRUE + useANOVA <- TRUE + useWelch <- F + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- T # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- FALSE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========## diff --git a/params_CUB2vUNT.R b/params_CUB2vUNT.R new file mode 100644 index 0000000..dff102c --- /dev/null +++ b/params_CUB2vUNT.R @@ -0,0 +1,228 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + + useHelv <- FALSE + + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + #within_between_analysis <- TRUE + alternate_pch_Thld <-1000 + +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('CUB2vUNT'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('CUB2vUNT') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +# contVar <- NULL +contVar <- c('gDNAlab') + +# === normalization paramters === +normalize <- T # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- F + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- TRUE + filter3.num.permutes <- 100 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- TRUE + useANOVA <- TRUE + useWelch <- F + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- T # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- FALSE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========## diff --git a/params_GENTvUNT.R b/params_GENTvUNT.R new file mode 100644 index 0000000..d13b535 --- /dev/null +++ b/params_GENTvUNT.R @@ -0,0 +1,228 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + + useHelv <- FALSE + + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + #within_between_analysis <- TRUE + alternate_pch_Thld <-1000 + +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('GENTvUNT'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('GENTvUNT') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +# contVar <- NULL +contVar <- c('gDNAlab') + +# === normalization paramters === +normalize <- T # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- F + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- TRUE + filter3.num.permutes <- 100 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- TRUE + useANOVA <- TRUE + useWelch <- F + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- T # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- FALSE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========## diff --git a/params_METTime.R b/params_METTime.R new file mode 100644 index 0000000..fd5c3ca --- /dev/null +++ b/params_METTime.R @@ -0,0 +1,228 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + + useHelv <- FALSE + + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + #within_between_analysis <- TRUE + alternate_pch_Thld <-1000 + +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('METtime'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('METtime') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +# contVar <- NULL +contVar <- c('gDNAlab') + +# === normalization paramters === +normalize <- T # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- F + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- TRUE + filter3.num.permutes <- 100 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- TRUE + useANOVA <- TRUE + useWelch <- F + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- T # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- FALSE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========## diff --git a/params_METvUNT.R b/params_METvUNT.R new file mode 100644 index 0000000..e78ddbf --- /dev/null +++ b/params_METvUNT.R @@ -0,0 +1,228 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + + useHelv <- FALSE + + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + #within_between_analysis <- TRUE + alternate_pch_Thld <-1000 + +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('METvUNT'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('METvUNT') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +# contVar <- NULL +contVar <- c('gDNAlab') + +# === normalization paramters === +normalize <- T # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- F + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- TRUE + filter3.num.permutes <- 100 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- TRUE + useANOVA <- TRUE + useWelch <- F + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- T # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- FALSE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========## diff --git a/params_VANTime.R b/params_VANTime.R new file mode 100644 index 0000000..02ad8bd --- /dev/null +++ b/params_VANTime.R @@ -0,0 +1,228 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + + useHelv <- FALSE + + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + #within_between_analysis <- TRUE + alternate_pch_Thld <-1000 + +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('VANtime'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('VANtime') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +# contVar <- NULL +contVar <- c('gDNAlab') + +# === normalization paramters === +normalize <- T # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- F + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- TRUE + filter3.num.permutes <- 100 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- TRUE + useANOVA <- TRUE + useWelch <- F + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- T # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- FALSE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========## diff --git a/params_VANvUNT.R b/params_VANvUNT.R new file mode 100644 index 0000000..83fc7fd --- /dev/null +++ b/params_VANvUNT.R @@ -0,0 +1,228 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + + useHelv <- FALSE + + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + #within_between_analysis <- TRUE + alternate_pch_Thld <-1000 + +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('VANvUNT'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('VANvUNT') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +# contVar <- NULL +contVar <- c('gDNAlab') + +# === normalization paramters === +normalize <- T # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- F + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- TRUE + filter3.num.permutes <- 100 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- TRUE + useANOVA <- TRUE + useWelch <- F + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- T # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- FALSE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========## diff --git a/params_overview.R b/params_overview.R new file mode 100644 index 0000000..ac43f7e --- /dev/null +++ b/params_overview.R @@ -0,0 +1,224 @@ +### ========= input parameters section =========== + +## rarely changed params: + + # === image format parameters === + # the suffix of saveImageAs variable stands for + # an image format. The following list is a subset + # of all formats supported by a2ping. + # Here is a complete list of supported a2ping outputs: BMP EPS GIF JPEG PBM PCL5 PDF PDF1 PGM PNG PPM PS TIFF XPM XWD markedEPS markedPS + # If you think that I've ommited some important format, + # please let me know or simply add the appropriate flag (and add it to imageFormat table in core.R) + saveImageAsPDF <- TRUE + saveImageAsPS <- TRUE # this needs to be TRUE for the latex report file + saveImageAsPBM <- FALSE + saveImageAsPGM <- FALSE + saveImageAsPPM <- FALSE + saveImageAsPNG <- FALSE + saveImageAsXWD <- FALSE + saveImageAsBMP <- FALSE + saveImageAsTIFF <- FALSE + saveImageAsJPEG <- FALSE + saveImageAsGIF <- FALSE + saveImageAsXPM <- FALSE + + doAbtTables <- FALSE + useHelv <- FALSE + dumpAbuTables <- TRUE + dumpAnnTbl <- FALSE # use annotate_tables.py instead of g3anntbl.csv + dumpBinaryTables <- TRUE + dumpDistTables <- TRUE + alternate_pch_Thld <-1000 +## environment/user specific config: + # === output directory === + # try to avoid dashes in directory names, they are interpolated into dots +dataDir <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/PhyCA.Stats.Out" # directory for storing all PhyCA-Stats output data +sysTime <- format(Sys.time(), "%H.%M.%S") # directory for storing all this particular run of PhyCA-Stats +outDir <- paste(dataDir,"/",Sys.Date(),"_",sysTime,".PhyCA.Stats.Out",sep="") +outDir <- gsub("-",".",outDir) + +nCPUs <- 3 # number of CPUs +# Note, that right now, snowfall parallel set up for each fragment of the pipeline is done separately. In production, I would consider only one initialization. + +# === binaries === +binExtractAnnotation <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractAnnotation.pl" # full path to extractAnnotation.pl script +binComUnderChars <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/commentOutUnderscoreChars.sh" # shell script to comment out underscore characters in latex tabels +binExtHeader <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/extractHeader.pl" # path to perl script extracting headers of data files (formatted for unifrac) +unifracBin <- "macqiime beta_diversity.py" # path to qiime pkg for unifrac, works with qiime svn revision 2507 +# Note that ps2pdf is used for ps-to-pdf conversion +# and a2ping for conversion to other formats +# Right now it is assumed that ps2pdf and a2ping are in the PATH variable + +# === script paths === +coreLibPath <- "/Users/lalithaviswanathan/Documents/Project/PhyCA_Stats/code/coreLib.R" + +# === input files === +otuAnnFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/abund_rep_set.GG98_FL.taxonomy.80.csv" # combo of previous seqTaxFile and seqAnnFile +pathToTree <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/gg_97_otus_4feb2011.tre" + +## project specific params: +# === input files === +bFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered_31345.txt" # FULL path to presence/absence table +aFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/ref97_gg98_filtered_31345.txt" # FULL path to abundance table +mFile <- "/Users/lalithaviswanathan/Documents/Project/Cubi_LChesnel_EPAN12_0120/metadata.csv" # FULL path to metadata table; check the format of this file - get rid of OTU_ID field +# It is assumed that the second column of mFile, short_name, consists of short names of the files - no longer than 10 characters. Also, metadata column names must start with letters not numbers, else R puts an "X" in front. +unequal.scale.profiles <- T # profiles of abundance vs. sample for an OTU are scaled to that OTU's max if TRUE + + +## phyca-stats run params here to end + +# R needs to know types of metadata columns. In particular, categorical variables have to to be identified by the user. +# There are two ways to specify categorical variables +# 1. write the names of categorical variabls into a comma delimeted file (mmFile) +# 2. specify the names of categorical variabls here (mFactors) +# mFactors <- NULL # set mFactors to NULL if the user does not specify categorical variables +mFactors <- c('Timepoint', 'Treatment','CB315vUNT','METvUNT','CUB2vUNT', 'VANvUNT','GENTvUNT','CB315vMET','CB315vCUB2','CB315vVAN','CB315time','METtime','CUB2time','VANtime'); + +# here define which factor(s) will be used for filtering. +mFilterFactors <- c('') # what is actually used in the anylsis only in DataRedn3 or 5 l 56 ff + + +# in doDataRedn6 the 'subjects' and 'pairings' columns must be specified here +# they must also be specified in the mFactors list +# currently the t.test is done but wilcox is also available as is FDR adjustment (paramters and conditional statements need to be added) +# subjectsFactor <- c('PatientID') # s1, s2, etc +# pairingsFactor <- c('CvCtrl') # pre, post etc (must be only two levels) # turn on for filter 6 and place in factor + +# For bioenv, Mantel and Adonis tests need a selection of continuous +# variables. +# There are two options: + +# 1. User specifies only categorical variables and all others are +# treated as continuous (unless R detects a character variable and +# excludes it from the list of continuous variables) This is a +# good choice if the user wants to use bioenv() to identify +# categorical variables associated with distances of environmental +# variables. + +# 2. User specifies continuous variables that she wants to use in +# Mantel and Adonis tests. + +# Note that contVar has to be defined, even if you don't want to specify any +# continuous variables. In this case, just set +#contVar <- NULL +contVar <- c('gDNAlab') + + +# === normalization paramters === +normalize <- F # normalization flag, if set to TRUE, the normalization of all data reduction tables is performed. +normTarget <- 1e6 # target normalization value (used only for totalSum) +whichNorm <- "totalSum" #"totalSum", "rankNorm", "uqNorm" + + +# === flags for different data reduction methods === +# Data reduction methods are numbered as in SOW document. +doDataRedn1 <- T + Redn1SampleRemoval <- FALSE # if a sample has zero OTUs in bt1 then remove sample + # may need to set to true if you want bt1 ordinations/clusterings if data set had a clean negative control + # this can create problems with sample labels + +doDataRedn2 <- F + +doDataRedn3 <- F + permute.filter3 <- F + filter3.num.permutes <- 4 + +doDataRedn4 <- F + rangeQtThld <- 0.9 # quantile treshold for the max/min ratios; only taxons with the max/min ratios in the 90% quantile will be selected in the data reduction method #4 + +doDataRedn5 <- F + useANOVA <- F + useWelch <- TRUE + useKW <- F + pValThld <- .05 # threshold of ANOVA type p-values. pValThld <- FALSE will auto-select >= 50 taxa in filter 5 + filterMin <- 10 # if pValThld <- FALSE, phyca-stats will select >= this many OTUs, rounding the p value to the nearest greater power of 10 + permute.filter5 <- 1 # 0=off, 1=shuffle samples into groups, 2=shuffle all abundandance data + filter5.num.permutes <- 20 + +doDataRedn6 <- F +doDataRedn7 <- F +doDataRedn8 <- F +doDataRedn9 <- F +# 9 in one per correlated cluster per species/subfamily + +# === parameters for distance methods === +createSorensenDist <- F +createUnifracDist <- T +createWgtdUnifracDist <- T +createBrayCurtisDist <- F +createEcldnDist <- F + +# === ordination parameters === +doPCoA <- T # Principal Coordinate Analysis +doPCA <- FALSE # Principal Component Analysis +doNMDS <- FALSE # Non metric Multi-Dimensional Scaling +hexbinThld <- 330 # threshold for switching to a hexbin plot in ordination scatterplot plotOrd2dAllFtrs() routine + +# === biplots === +doEnvfit <- FALSE # used for biplots +contVar_Envfit <- 'nseqs' # just pick one + +ordTrellis1 <- FALSE # if TRUE, single categorical variable trellis plots are generated +ordTrellis2 <- FALSE # if TRUE, two categorical variables trellis plots are generated + +# === hierarchical clustering parameters === +doHCNN <- FALSE # hierarchical clustering using single linkage +doHCAN <- T # hierarchical clustering using average linkage +doHCFN <- FALSE # hierarchical clustering using complete linkage +doHCWard <- FALSE # hierarchical clustering using Ward linkage +sepThickness <- 3; + +# === singnificance testing parameters === +# There are three significance type of tests, all from vegan package: +# bioenv(), mantel() and adonis(). Here are the flags that control +# which of them will be run. +doBioEnv <- FALSE +doMantel <- FALSE +doAdonis <- T +doAdonisPairs <- F + + +# params used for bioenv and mantel only +# The dissimilarity index used for community data in 'vegdist' +distIndex <- "bray" # possible values (specified in the appropriate + # pull down menu) are: manhattan, euclidean, + # canberra, bray, kulczynski, jaccard, gower, + # altGower, morisita, horn, mountford, raup , + # binomial or chao. + +# used for bioenv and mantel only +# The correlation method used in 'cor', 'bioenv' and 'mantel'subject of unknown paternal ethnic origin +corMethod <- "spearman" # possible values (specified in the + # appropriate pull down menu) : pearson, + # kendall, spearman + + +# === PAM === +doPAM <- F # run PAM classifier for each categorical variable to classify samples with missing data within the selected variable +pamMinNoORFs <- 10 +pamMaxNoORFs <- 20 +pamfilter <- 1 # usually filter 9 is used (feb 2012) +## in PAM plots groups will be ordered alphabeticaly. So if you want to see "pre" before "post" use "1_pre" and "2_post" + + +# === iTOL === +# itol requires filter 5, make sure doDataRedn5 <- TRUE +doITOL <- FALSE +itolBaseline <- 'healthy' +itolTaxonLevel <- 'family' + +# === compositional, horizontal barcharts === +doTaxonPropBarCharts <- F # generate bar charts of proportions at different taxonomic levels +doTaxonAbundancePropBarCharts <-TRUE +nTaxaInPropBarCharts <- 8 # maximum number of taxa to display in these bar charts.3 +# there will be an extra black category for 'Other', for a total of +# nTaxaInPropBarCharts + 1 elements per bar +dumpBarChartTables <- TRUE + +# === richness plots === +doTaxonRichnessCharts <- F +dumpRichnessChartTables <- TRUE +richnessTaxonLevels <- c("phylum","class","order","family","genus","species") +# full options are c("phylum","class","order","family","genus","species") + +### ========= end of input parameters section ===========##