-
Notifications
You must be signed in to change notification settings - Fork 0
/
356-1_RNASeq.Rmd
2980 lines (2347 loc) · 131 KB
/
356-1_RNASeq.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
---
title: "P356-1 T-cells"
author: "Analyst: Alex Hu"
output: pdf_document
editor_options:
chunk_output_type: console
---
Project Summary
------
FIXME
```{r setup, echo=FALSE, message=FALSE, warning=FALSE}
library(apird)
library(mongolite)
libs <- getProjectLibs("378", searchType = "regex")
anno <- getAnno(libs)
rownames(anno) <- anno$libid
counts <- t(getGeneCounts(libs))
#counts <- read.table(paste0("counts.txt"),sep="\t",header=TRUE,row.names=1)
#write.table(counts,paste0("counts.txt"),sep="\t",quote=FALSE,col.names=NA)
library(knitr)
library(dplyr)
library(ggrepel)
library(circlize)
library(ComplexHeatmap)
# library(ggplot2); theme_set(theme_bw(20) + theme(panel.grid.major = element_blank(),
# panel.grid.minor = element_blank()) +
# theme(legend.key = element_blank()))
library(ggplot2);theme_set(theme_bw(20) + theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour="black", fill=NA, size=1),
axis.text=element_text(colour="black"),
axis.ticks=element_line(colour="black"))+
theme(legend.key = element_blank()))
library(edgeR)
library(limma)
library(gplots)
library(RColorBrewer)
library(ggthemes)
library(ggbeeswarm)
library(viridis)
library(stringr)
library(readxl)
library(heatmap3)
library(readxl)
#library(ggforce)
#Load Matt's library which includes a function for making barcode plots
library(geneSetTools)
library(umap)
library(reshape2)
datadir = "../../data/2020-08-24/"
plotdir = paste0(datadir,"plots/")
options(stringsAsFactors = FALSE)
pgrep <- function(s,l){
return( l[grep(s,l)])
}
### This is monocle code for kaz
options(stringsAsFactors = FALSE)
expression_matrix <- as.matrix(read.table("../../data/2020-09-07/TTR2_data.csv",sep=","))
gene_names <- read.table("../../data/2020-09-07/TTR2_anno.csv",sep=",")[,1]
expression_matrix <- expression_matrix[ !is.na(gene_names),]
gene_names <- gene_names[ !is.na(gene_names)]
gene_names[ duplicated(gene_names) ] <- paste0(gene_names[ duplicated(gene_names) ],".2")
cell_names <- read.table("../../data/2020-09-07/TTR2_cell.csv",sep=",")[,1]
rownames(expression_matrix) <- gene_names
colnames(expression_matrix) <- cell_names[2:length(cell_names)]
####
# filter genes that are genes that are expressed in at last 10% of cells
expression_matrix <- expression_matrix[rowSums(expression_matrix > 0) > 0.1*ncol(expression_matrix),]
gm <- data.frame( gene_short_name=rownames(expression_matrix) )
rownames(gm) <- gm$gene_short_name
cds <- new_cell_data_set(expression_matrix, gene_metadata = gm)
cds <- preprocess_cds(cds, num_dim = 100)
cds <- reduce_dimension(cds )
```
```{r loading}
metrics <- getMetrics(libs)
rownames(metrics) <- sapply( metrics$libid_fcid, function(x) strsplit(x,"_")[[1]][1])
design <- metrics
design$libId <- str_extract(design$libid_fcid, "lib[0-9]+")
rownames(design) <- design$libId
colnames(counts)[1:ncol(counts)] <- str_extract(colnames(counts)[1:ncol(counts)], "lib[0-9]+")
design <- merge(design, anno, by.x = "libId", by.y="row.names")
rownames(design) <- design$libId
design$cdr <- colSums(counts>1)[rownames(design)]
## label which samples are cancer and which are not
# Could you put different labels on 1_X - 4_X from naïve mice and 5_X – 8_X from mice having cancer? There are 8 different groups and naïve T cell that is for subtraction
sampnums <- sapply( design$sample_name, function(s) strsplit(s,"_")[[1]][1])
design$cancer <- rep("Tn",nrow(design))
design$cancer[ sampnums %in% as.character(1:4) ] <- "naive"
design$cancer[ sampnums %in% as.character(5:8) ] <- "cancer"
write.table(designm,"../../data/2021-10-08/annotations.txt",sep="\t",col.names=NA,quote=FALSE)
```
```{r get TCRs}
nameconv <- c("Tn","DN","IL1RL1+","TSLPR+","DP")
names(nameconv) <- c("CD44low","TSLPR-Il1rl1-","TSLPR-Il1rl1+","TSLPR+Il1rl1-","TSLPR+Il1rl1+")
designm$sort2 <- factor( nameconv[ designm$sort_short], levels=nameconv)
designm$mid <- sapply(designm$providedSampleName, function(s) strsplit(s,"_")[[1]][1])
designm$midsort <- paste( designm$mid, designm$sort2, sep="-")
tcrs <- getTcrs(libs, removeBulk = FALSE) # returns 604 chains
dupnts <- tcrs$full_nt_sequence[ duplicated(tcrs$full_nt_sequence)]
dupids <- paste0("s",1:length(dupnts))
names(dupids) <- dupnts
tcrs$shared<- tcrs$full_nt_sequence %in% dupnts
tcrs <- cbind(tcrs, designm[ tcrs$libid, !colnames(designm) %in% colnames(tcrs)])
tcrs$ntid <- rep(".",nrow(tcrs))
tcrs[tcrs$shared,"ntid"] <- dupids[ tcrs[tcrs$shared,"full_nt_sequence"]]
tcrs$mid <- designm[tcrs$libid,"mid"]
table(tcrs[tcrs$shared,"ntid"],tcrs[tcrs$shared,"mid"])
table(tcrs[tcrs$shared,"ntid"],tcrs[tcrs$shared,"midsort"])
```
```{r set_up_qc_parameters, fig.width=4, fig.height=3}
#Set QC cuts
align_cut = 80
total_reads_cut = 1
median_cv_cut = 1
#Get a colorblind palette
cb_pal <- colorblind_pal()(8)
cb_pal <- cb_pal[2:8]
my_cb_pal <- colorRampPalette(cb_pal)(length(unique(design$Sample.Id)))
```
RNA-seq Quality Metrics
------
In performing quality control, the following three metrics are examined:
1. The total number of reads in each library (libraries with less than 1 million reads are suspect for bulk). For single cell, we expect: ????
2. The percent alignment of each library (higher is better)
3. Median CV coverage. This is the the median coefficient of variation of coverage of the 1000 most highly expressed transcripts. It measures read bias along the transcript. Ideally, this value would be 0.
A histogram plotting the number of reads in P143 libraries is follows. The target number of reads for a bulk library is ~5 million. Many of these libraries have fewer than 1 million reads, which is unfortunate and may be too small to analyze properly.
```{r qcplots_total_reads, fig.width=4, fig.height=3}
liborder <- design$libId[ order( design$fastq_total_reads, decreasing=TRUE ) ]
ggplot(design, aes(x=libId, y=fastq_total_reads/10^6, fill=sort)) +
geom_col() +
labs(y = "millions of reads") +
geom_hline(yintercept = total_reads_cut, linetype = 4) +
theme(text = element_text(size=16)) +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
scale_x_discrete(limits = liborder)
invisible(dev.off())
```
The following plots compare the median CV of coverage and the percent alignment of reads in each library. High quality libraries will fall in the upper left quadrant of the box (high percent alignment and low median CV coverage).
There is a large number of low-quality samples defined by low percent alignment and high median cv coverage, and most of these samples come from patient 31. However, there is a cluster of patient 33 cells that also have high median cv coverage and low percent alignment. Plotting the same libraries with total reads on the Y-axis shows that the patient 33 cells with low alignment are the same cells that have very low read counts.
```{r qcplots_coverage_vs_alignment, fig.width=4, fig.height=3, results='hide'}
g <- ggplot(design, aes(x=median_cv_coverage, y=pct_aligned, color=sort) ) +
geom_point(size=2, alpha=1.0) +
labs(x = "median cv coverage", y = "percent alignment", color ="Patient number")+
geom_hline(yintercept = align_cut)+
geom_vline(xintercept = median_cv_cut)+
theme(text = element_text(size=12))
print(g)
png(paste(plotdir,"QC_coverage_vs_alignment.png",sep=""), height = 400, width = 600)
print(g)
invisible(dev.off())
```
```{r make_qc_cuts}
design$qc_pass <- design$fastq_total_reads > total_reads_cut &
design$pct_aligned > align_cut &
design$median_cv_coverage < median_cv_cut
design_qc <- design %>% dplyr::filter(qc_pass ==TRUE)
rownames(design_qc) <- design_qc$libId
counts_qc <- counts[,colnames(counts) %in% design_qc$libId]
```
Sex Check
------
To check for sample swaps, the reported sex can be compared to the X and Y chromosome read counts in the sequencing data. The histogram below is colored according to the reported sex and displays the log-ratio of X to Y read counts. The peak of libraries with a high X:Y counts ratio (ie look like samples from females according to sequencing) match the reported sex of female and the peak with a low X:Y ratio match the reported sex. There are some libraries in between the peaks where the sequencing data doesn't provide a clear sex prediction. This suggests that more stringent quality control may be needed. Also, the libraries from patient 33 show a wide variation of count ratios when we expect them to be all roughly the same given that they are from the same patient.
```{r geneFiltering}
# Get mef features into a bed file
x <- read.table("../../data/2021-10-08/mm10.ensGene_Mef2c.gtf",sep="\t")
x$cnum <- gsub("chr","",x[,1])
x$name <- paste0( x[,3], " ", x[,9])
x <- x[,c("cnum","V4","V5","name")]
write.table(x,"../../data/2021-10-08/mm10_mef2_features.bed",col.names=FALSE,row.names=FALSE,quote=FALSE,sep="\t")
#Get protein coding genes with HGNC symbols
gene_key <- read.table("../../../data/ensemblkey_GRCm38.txt", header = TRUE,sep = "\t",na.strings = "")
genes_mgi <- gene_key[!is.na(gene_key$mgi_symbol),]
ens2entrez <- gene_key[ rownames(comparisons[[1]]) %in% gene_key$ensembl_gene_id, "entrezgene"]
names(ens2entrez) <- gene_key[ rownames(comparisons[[1]]) %in% gene_key$ensembl_gene_id, "ensembl_gene_id"]
counts_mgi <- counts_qc[rownames(counts_qc) %in% genes_mgi$ensembl_gene_id,]
genes_pc <- subset(genes_mgi, genes_mgi$gene_biotype == "protein_coding") #21119
genes_pc <- genes_pc[!duplicated(genes_pc$ensembl_gene_id),] #remove duplicated ensembl genes #21117
counts_pc <- merge(genes_pc, counts_qc, by.x="ensembl_gene_id", by.y ="row.names")
gene_key_pc <- counts_pc[,1:4] #First three columns contain annotation information
counts_pc <- counts_pc[,5:ncol(counts_pc),] #The remaining columns contain counts information \
rownames(counts_pc) <- gene_key_pc[,1]
#Define a function to filter out lowly expressed genes
gene_filter <- function(counts_in, per_cutoff){
#Keep genes with cpm of at least one in at least per_cutoff fraction of libraries
#CPM normalize
counts_cpm_norm <- as.data.frame(t(t(counts_in*10^6)/colSums(counts_in)))
#Filter out lowly expressed genes
keepRows <- rowSums((counts_cpm_norm) >= 1) >= per_cutoff*ncol(counts_cpm_norm)
counts_filtered <- counts_in[keepRows,]
return(counts_filtered)
}
#Run function to filter lowly expressed genes
counts_all_filtered <- gene_filter(counts_mgi, 0.20)
counts_pc_filtered <- gene_filter(counts_pc, 0.20)
normalize_counts <- function(counts_in, method){
#normalize using tmm or deconvolution
#tmm is good for bulk RNAseq
#deconvolution is best for large datasets of single cell RNAseq
#deconvolution is NOT recommended for smaller datasets (less than a few hundred cells)
if(method == "decon"){
#Normalize using the deconvolution algorithm
decon_norm_factors <- computeSumFactors(as.matrix(counts_in))
counts_norm <- as.data.frame(t(t(counts_in)/decon_norm_factors))
}
if(method == "tmm"){
#Normalize using the TMM algorithm
dge <- DGEList(counts_in)
dge <- calcNormFactors(dge)
counts_norm <- cpm(dge, normalized.lib.sizes=TRUE)
}
return(counts_norm)
}
counts_pc_norm <- normalize_counts(counts_pc_filtered, "tmm")
counts_all_norm <- normalize_counts(counts_all_filtered, "tmm")
colnames(counts) <- sapply( colnames(counts), function(s) strsplit(s,"_")[[1]][1] )
x <- counts[ rownames(counts_pc_norm)[1:20],colnames(counts_pc_norm)]
y <- counts_pc_norm[1:20,]
plot(log(x[!is.na(x)]), log(y[!is.na(x)]))
plot(x[!is.na(x)], y[!is.na(x)])
# Create a dataframe of gene names to attach to the DGEList(). Keep both ensembl_gene_id, and hgnc_symbol
fgenes <- genes_pc[match(rownames(counts_pc_norm ), genes_pc$ensembl_gene_id), c("ensembl_gene_id", "mgi_symbol")]
dge <- DGEList(counts=counts_pc_norm, genes=fgenes)
design <- design[ colnames(counts_pc_norm),]
labcounts_pc_norm <- counts_pc_norm
cnames <- paste(design$sample_name,design$sort,design$cancer,sep="\n")
colnames(labcounts_pc_norm) <- cnames
labcounts_pc_norm <- cbind( data.frame( mgi_symbol=comparisons[[1]][rownames(counts_pc_norm),"mgi_symbol"]), counts_pc_norm)
write.table( labcounts_pc_norm, paste0(plotdir,"normalized_counts.txt"), col.names=NA,quote=FALSE,sep="\t" )
```
Gene Filtering
------
A filter is applied to keep only genes with HGNC symbols that have been annotated as protein coding. This keeps `r nrow(genes_pc)` of 64345 genes. A second filter that selects genes with a count of at least one in 10% of libraries is also applied. This keeps `r nrow(counts_pc_filtered)` of the `r nrow(genes_pc)` genes from the first filter. The selected genes are normalized using the TMM (trimmed mean of M values) algorithm.
Principal Component Analysis
------
Principal component analysis (PCA) looks for broad trends in gene expression across libraries in an unsupervised manner. There is no obviously apparent structure in PCA space, and the amount of variation described by the first two principal components is low, suggesting that there may be no single major overwhelming source of variation (like a batch effect) in the data.
```{r pca, fig.width=4, fig.height=3}
#Run PCA on the normalized log2 transformed counts data
dat <- log2(as.data.frame(t(dge$counts))+0.5)
pca = prcomp(dat, center=TRUE, scale=FALSE)
#Get PCA resutls and merge with sample information stored in metrics
sum_pca = summary(pca)
pca_scores= as.data.frame(pca$x)
pdatscores <- merge(design_qc, pca_scores, by.x = "libId", by.y="row.names")
pc1_lab = paste("PC1 (", round(100*sum_pca$importance[2, 1], 1), "%)", sep="")
pc2_lab = paste("PC2 (", round(100*sum_pca$importance[2, 2], 1), "%)", sep="")
pc3_lab = paste("PC3 (", round(100*sum_pca$importance[2, 3], 1), "%)", sep="")
pc4_lab = paste("PC4 (", round(100*sum_pca$importance[2, 4], 1), "%)", sep="")
pc5_lab = paste("PC5 (", round(100*sum_pca$importance[2, 5], 1), "%)", sep="")
pc6_lab = paste("PC6 (", round(100*sum_pca$importance[2, 6], 1), "%)", sep="")
pc3genes <- colnames(dat)[order( -1*abs(pca$rotation[,3]) )[1:75]]
pdatscores$sort_short <- gsub("CD45\\+TCR\\+CD4\\+Foxp3\\+","",pdatscores$sort)
pdatscores$sort_short <- gsub(" cells","",pdatscores$sort_short)
png(paste0(plotdir,"pca_sort_cancer.png"),width=500,height=400)
ggplot(pdatscores[pdatscores$sort != "CD45+TCR+CD4+Foxp3-CD44low cells",],aes(x=PC1,y=PC2,color=cancer)) + geom_point() + facet_wrap(~sort_short) + scale_color_manual(values=c("naive"="black","cancer"="red"))
dev.off()
png(paste0(plotdir,"pca_sort_cancer.png"),width=450,height=300)
ggplot(pdatscores[pdatscores$sort != "CD45+TCR+CD4+Foxp3-CD44low cells",],aes(x=PC1,y=PC2,color=sort_short, shape=cancer)) + geom_point(size=3) + scale_shape_manual(values=c("cancer"=17,"naive"=8)) + labs(color="sort",shape="") + scale_color_manual(values=c("black","red","blue","orange"))
dev.off()
make_colors <- function(values){
cb_pal <- colorblind_pal()(8)
numvals <- length(values)
my_cb_pal <- colorRampPalette(cb_pal)(numvals)
colorlist = c()
for( i in 1:numvals ){
colorlist[values[i]] <- my_cb_pal[i]
}
return(colorlist)
}
png(paste(plotdir,"PCA_PC1_PC2_stimulation.png",sep=""), height=400, width=600)
ggplot() +
geom_point(data=pdatscores, aes(x=PC1, y=PC2, color = sample.stimulation, shape= as.factor(Visit.Number.Descriptor) ), size=1.5)+
labs(x = pc1_lab, y = pc2_lab)+
theme(text = element_text(size=12))
dev.off()
png(paste(plotdir,"PCA_PC1_PC2_timepoint.png",sep=""), height=400, width=600)
ggplot() +
geom_point(data=pdatscores, aes(x=PC1, y=PC2, color = as.factor(Visit.Number.Descriptor), shape= sample.stimulation), size=1.5)+
labs(x = pc1_lab, y = pc2_lab)+
theme(text = element_text(size=12))
dev.off()
p1 <- ggplot() +
geom_point(data=pdatscores, aes(x=PC1, y=PC2, color = sample.stimulation ), size=1.5)+
labs(x = pc1_lab, y = pc2_lab, color="Stimulant")+
theme(text = element_text(size=12))
p2 <- ggplot() +
geom_point(data=pdatscores, aes(x=PC3, y=PC4, color = sample.stimulation ), size=1.5)+
labs(x = pc3_lab, y = pc4_lab, color="Stimulant")+
theme(text = element_text(size=12))
p3 <- ggplot() +
geom_point(data=pdatscores, aes(x=PC1, y=PC2, color = as.factor(Visit.Number.Descriptor) ), size=1.5)+
labs(x = pc1_lab, y = pc2_lab, color="Study Group")+
theme(text = element_text(size=12))
p4 <- ggplot() +
geom_point(data=pdatscores, aes(x=PC3, y=PC4, color = as.factor(Visit.Number.Descriptor) ), size=1.5)+
labs(x = pc3_lab, y = pc4_lab, color="Study Group")+
theme(text = element_text(size=12))
pushViewport(viewport(layout = grid.layout(2 , 2)))
vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y)
print(p1, vp = vplayout(1,1))
print(p2, vp = vplayout(1,2))
print(p3, vp = vplayout(2,1))
print(p4, vp = vplayout(2,2))
ggplot() +
geom_point(data=pdatscores, aes(x=PC1, y=PC3, color = sample.stimulation ), size=1.5)+
labs(x = pc1_lab, y = pc3_lab, color="Study Group")+
theme(text = element_text(size=12))
ggplot() +
geom_point(data=pdatscores, aes(x=PC1, y=PC2, color = MEDIAN_CV_COVERAGE), size=1.5)+
labs(x = pc1_lab, y = pc2_lab)+
theme(text = element_text(size=12))
ggplot() +
geom_point(data=pdatscores, aes(x=PC1, y=PC2, color = fastq_total_reads), size=1.5)+
labs(x = pc1_lab, y = pc2_lab)+
theme(text = element_text(size=12))
ggplot() +
geom_point(data=pdatscores, aes(x=PC1, y=PC2, color = mapped_reads_w_dups), size=1.5)+
labs(x = pc1_lab, y = pc2_lab)+
theme(text = element_text(size=12))
```
```{r}
cancerlibs <- design_qc$libid[ design_qc$cancer == "cancer"]
naivelibs <- design_qc$libid[ design_qc$cancer == "naive"]
dat_cancer <- log2(as.data.frame(t(dge$counts[,cancerlibs]))+0.5)
pca_cancer = prcomp(dat_cancer, center=TRUE, scale=FALSE)
#Get PCA resutls and merge with sample information stored in metrics
sum_pca = summary(pca_cancer)
pca_scores= as.data.frame(pca_cancer$x)
pdatscores <- merge(design_qc, pca_scores, by.x = "libId", by.y="row.names")
pc1_lab = paste("PC1 (", round(100*sum_pca$importance[2, 1], 1), "%)", sep="")
pc2_lab = paste("PC2 (", round(100*sum_pca$importance[2, 2], 1), "%)", sep="")
pc3_lab = paste("PC3 (", round(100*sum_pca$importance[2, 3], 1), "%)", sep="")
pc4_lab = paste("PC4 (", round(100*sum_pca$importance[2, 4], 1), "%)", sep="")
pc5_lab = paste("PC5 (", round(100*sum_pca$importance[2, 5], 1), "%)", sep="")
pc6_lab = paste("PC6 (", round(100*sum_pca$importance[2, 6], 1), "%)", sep="")
png( paste0(plotdir,"cancer_pca.png"),width=300,height=200)
ggplot() +
geom_point(data=pdatscores, aes(x=PC1, y=PC2, color = sort_short), size=2)+
labs(x = pc1_lab, y = pc2_lab, title="Cancer")+
theme(text = element_text(size=12)) + scale_color_manual(values=c("black","red","blue","orange"))
dev.off()
dat_naive <- log2(as.data.frame(t(dge$counts[,naivelibs]))+0.5)
pca_naive = prcomp(dat_naive, center=TRUE, scale=FALSE)
#Get PCA resutls and merge with sample information stored in metrics
sum_pca = summary(pca_naive)
pca_scores= as.data.frame(pca_naive$x)
pdatscores <- merge(design_qc, pca_scores, by.x = "libId", by.y="row.names")
pc1_lab = paste("PC1 (", round(100*sum_pca$importance[2, 1], 1), "%)", sep="")
pc2_lab = paste("PC2 (", round(100*sum_pca$importance[2, 2], 1), "%)", sep="")
pc3_lab = paste("PC3 (", round(100*sum_pca$importance[2, 3], 1), "%)", sep="")
pc4_lab = paste("PC4 (", round(100*sum_pca$importance[2, 4], 1), "%)", sep="")
pc5_lab = paste("PC5 (", round(100*sum_pca$importance[2, 5], 1), "%)", sep="")
pc6_lab = paste("PC6 (", round(100*sum_pca$importance[2, 6], 1), "%)", sep="")
png( paste0(plotdir,"naive_pca.png"),width=300,height=200)
ggplot() +
geom_point(data=pdatscores, aes(x=PC1, y=PC2, color = sort_short), size=2)+
labs(x = pc1_lab, y = pc2_lab, title="Naive")+
theme(text = element_text(size=12)) + scale_color_manual(values=c("black","red","blue","orange"))
dev.off()
```
```{r volcano}
limma_volcano <- function( gtable, outfile="", title="", gs=c(), anno=TRUE, allanno = FALSE ){
p_cutoff = 0.05
fc_cutoff = 1.0
ixes = c(1)
if( length(gs) == 0){
ixes <- which(gtable$adj.P.Val <= p_cutoff)
}else{
ixes <- which(gtable$adj.P.Val <= p_cutoff & gtable$mgi_symbol %in% gs)
}
if( length(ixes) > 50 & (allanno == FALSE) ){
ixes <- ixes[1:50]
}
#png(outfile, height = 600, width = 900)
p <- ggplot(data = gtable, aes(x=logFC, y=-log10(adj.P.Val), color = logFC>0)) +
geom_point(size=1.5, shape = 19) + scale_color_manual(values = c("orange", "red"))+
theme(legend.position = "none") + labs(x="logFC",y="-log10 FDR",title=title)+
geom_hline(yintercept=-log10(p_cutoff), color="black",linetype="dotted",size=1.0)+
geom_vline(xintercept=-fc_cutoff, color="black",linetype="dotted",size=1.0)+
geom_vline(xintercept=fc_cutoff, color="black",linetype="dotted",size=1.0)+
theme(text = element_text(size=16))
if(anno & length(ixes)>0){
p <- p + geom_text_repel(data=gtable[ixes,], aes(logFC, -log10(adj.P.Val), fontface="bold", label=mgi_symbol), size=4, color="black")
}
print(p)
#dev.off()
return(p)
}
limma_volcano_highlight <- function( gtable, outfile="", title="", gs=c(), p_cutoff = 0.05, fc_cutoff = 1.0, labp=0.05 ){
gtable$highlight <- gtable$mgi_symbol %in% gs
gtable <- gtable[ order( gtable$highlight),]
ixes = c(1)
if( length(gs) == 0){
ixes <- which(gtable$adj.P.Val <= p_cutoff)
}else{
ixes <- which(gtable$adj.P.Val <= labp & gtable$mgi_symbol %in% gs)
}
if( length(ixes) > 50 ){
ixes <- ixes[1:50]
}
#png(outfile, height = 600, width = 900)
p <- ggplot(data = gtable, aes(x=logFC, y=-log10(adj.P.Val), color = highlight)) +
geom_point(size=1.5, shape = 19) + scale_color_manual(values = c("FALSE"="grey", "TRUE"="red"))+
theme(legend.position = "none") + labs(x="logFC",y="-log10 FDR",title=title)+
geom_hline(yintercept=-log10(p_cutoff), color="black",linetype="dotted",size=1.0)+
geom_vline(xintercept=-fc_cutoff, color="black",linetype="dotted",size=1.0)+
geom_vline(xintercept=fc_cutoff, color="black",linetype="dotted",size=1.0)+
theme(text = element_text(size=16))
if(length(ixes)>0){
p <- p + geom_text_repel(data=gtable[ixes,], aes(logFC, -log10(adj.P.Val), fontface="bold", label=mgi_symbol), size=4, color="black")
}
print(p)
#dev.off()
return(p)
}
p <- limma_volcano_highlight( comparisons3[["TSLPpos:IL1RL1pos"]], gs=hallmark[,"HALLMARK_INFLAMMATORY_RESPONSE"],labp=.5, title="DP positive synergy\nInflammatory Response"); print(p)
p <- limma_volcano_highlight( comparisons[["TSLPRpos.IL1RL1pos.Cancer"]], gs=hallmark[,"HALLMARK_OXIDATIVE_PHOSPHORYLATION"], labp=0.1); print(p)
toplot <- c("cancer.TSLPR.v.IL1RL1","naive.TSLPR.v.IL1RL1")
for( comp in toplot ){
print(comp)
gname <- gsub("\\."," ", comp)
gname <- gsub("pos","+",gname)
gname <- gsub("neg","-",gname)
p <- limma_volcano( comparisons[[comp]], title=gname)
png( paste0(plotdir,comp,"_volcano.png"), width=400,height=400)
print(p)
dev.off()
}
toplot <- c("IL1RL1.naive","IL1RL1.cancer")
for( comp in toplot ){
print(comp)
gname <- gsub("\\."," ", comp)
gname <- gsub("pos","+",gname)
gname <- gsub("neg","-",gname)
p <- limma_volcano( comparisons[[comp]], title=gname)
png( paste0(plotdir,comp,"_volcano.png"), width=400,height=400)
print(p)
dev.off()
}
genes <- comparisons[[1]]$mgi_symbol
cytokines <- genes[ grepl("^Il",genes)]
cytokines <- c(cytokines,genes[ grepl("^Tgf",genes)])
cytokines <- c(cytokines,genes[ grepl("^Ifn",genes)])
cytokines <- c(cytokines,genes[ grepl("^Tnf",genes)])
for( comp in names(comparisons)[ grepl("Cancer",names(comparisons))] ){
print(comp)
gname <- gsub("\\."," ", comp)
gname <- gsub("pos","+",gname)
gname <- gsub("neg","-",gname)
p <- limma_volcano( comparisons[[comp]], title=gname, gs = cytokines)
png( paste0(plotdir,comp,"_cytokines_volcano.png"), width=400,height=400)
print(p)
dev.off()
}
for( comp in names(comparisons4)[2:length(comparisons4)] ){
print(comp)
gname <- gsub(":","x", comp)
#p <- limma_volcano( comparisons4[[comp]], title=comp)
#png( paste0(plotdir,"interactionmodel_filtered/",gname,"_volcano.png"), width=400,height=400)
p <- limma_volcano( comparisons4[[comp]], title=gname, gs = cytokines)
png( paste0(plotdir,"interactionmodel_filtered/",gname,"_cytokines_volcano.png"), width=400,height=400)
print(p)
dev.off()
}
```
```{r}
libs <- colnames(dge)
countsm <- dge[,libs]
designm <- design_qc[ colnames(countsm),]
designm$sort <- gsub("\\+","p",designm$sort)
designm$sort <- gsub("\\-","m",designm$sort)
designm$sort <- gsub(" cells","",designm$sort)
designm$sort <- relevel(as.factor( designm$sort), ref="CD45pTCRpCD4pFoxp3mCD44low")
designm$cancer[ designm$cancer == "Tn"] <- "naive"
designm$cancer <- relevel(as.factor( designm$cancer), ref="naive")
#### Design mat 1
design_mat <- model.matrix(~0+designm$cancer:designm$sort)
colnames(design_mat) <- gsub("designm\\$cancer","",colnames(design_mat))
colnames(design_mat) <- gsub("designm\\$sort","",colnames(design_mat))
colnames(design_mat) <- gsub("(Intercept)","Intercept",colnames(design_mat))
colnames(design_mat) <- gsub(":",".",colnames(design_mat))
design_mat <- design_mat[, colSums(design_mat)>0]
print( colnames(design_mat) )
vwts <- voomWithQualityWeights(countsm, design= design_mat, plot=T, span=0.1)
vfit<- lmFit(vwts, design = design_mat)
cont.matrix <- makeContrasts(
TSLPRneg.IL1RL1neg.Cancer = cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
TSLPRpos.IL1RL1neg.Cancer = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m - naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m,
TSLPRneg.IL1RL1pos.Cancer = cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p,
TSLPRpos.IL1RL1pos.Cancer = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p,
Cancer= (cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m + cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m + cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p + cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m - naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p)/4,
naive.TSLP = naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
naive.IL1RL1 = naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
naive.TSLP.IL1RL1 = naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
cancer.TSLP = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
cancer.IL1RL1 = cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
cancer.TSLP.IL1RL1 = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
cancer.TSLP.vnaive = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
cancer.IL1RL1.vnaive = cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
cancer.TSLP.IL1RL1.vnaive = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
cancer.DN.vnaive = cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
cancer.DP.v.TSLPR = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m,
cancer.DP.v.IL1RL1 = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p,
naive.DP.v.TSLPR = naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m,
naive.DP.v.IL1RL1 = naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p,
both.DP.v.TSLPR = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m + naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m,
both.DP.v.Il1RL1 = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p + naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p,
both.TSLP.IL1RL1 = naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p + cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
both.IL1RL1 = naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p + cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
both.TSLP = naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m + cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
DPSynergy = naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m + naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
DPSynergy.Cancer = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p - cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m + cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m,
DNcancer.v.Tn = cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m - naive.CD45pTCRpCD4pFoxp3mCD44low,
TSLPcancer.v.Tn = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m - naive.CD45pTCRpCD4pFoxp3mCD44low,
IL1RL1cancer.v.Tn = cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p - naive.CD45pTCRpCD4pFoxp3mCD44low,
DPcancer.v.Tn = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - naive.CD45pTCRpCD4pFoxp3mCD44low,
DNnaive.v.Tn = naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m - naive.CD45pTCRpCD4pFoxp3mCD44low,
TSLPnaive.v.Tn = naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m - naive.CD45pTCRpCD4pFoxp3mCD44low,
IL1RL1naive.v.Tn = naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p - naive.CD45pTCRpCD4pFoxp3mCD44low,
DPnaive.v.Tn = naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p - naive.CD45pTCRpCD4pFoxp3mCD44low,
TSLPR.naive = naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p + naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p,
TSLPR.cancer = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p + cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p,
IL1RL1.naive = naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p + naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m - naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m,
IL1RL1.cancer = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1p + cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1m - cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m,
naive.TSLPR.v.IL1RL1 = naive.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m - naive.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p,
cancer.TSLPR.v.IL1RL1 = cancer.CD45pTCRpCD4pFoxp3pTSLPRpIl1rl1m - cancer.CD45pTCRpCD4pFoxp3pTSLPRmIl1rl1p,
levels=design_mat)
#vfit_eb <- eBayes(vfit)
#ilc2stimDE <- topTable( vfit_eb, coef=which(colnames(design_mat) == "ILC2.CD137Pos"),number=Inf, sort.by="P")
#table( ilc2stimDE$adj.P.Val <= 0.05)
vfit_c <- contrasts.fit(vfit, cont.matrix)
vfit_c_eb <- eBayes(vfit_c)
comparisons <- list()
for( i in 1:ncol(cont.matrix) ){
compname <- colnames(cont.matrix)[i]
z <- topTable (vfit_c_eb, coef = i, number=Inf, sort.by="P")
comparisons[[compname]] <- z
print(compname)
sigs <- z$adj.P.Val <= 0.05
print(table(sigs))
#write.table( z, paste0(plotdir,compname,"_DE.txt"),sep="\t",col.names=NA,quote=FALSE)
}
z <- comparisons[["TSLPRpos.IL1RL1pos.Cancer"]]
z <- z[order(z$P.Value),]
write.table(z[ z$logFC > 0, ], paste0(plotdir,"dp_cancer_up.txt"),quote=FALSE,sep="\t",col.names=NA)
write.table(z[ z$logFC < 0, ], paste0(plotdir,"dp_cancer_down.txt"),quote=FALSE,sep="\t",col.names=NA)
g <- rownames(comparisons[[1]])
FCs <- data.frame( sapply(comparisons,function(de) de[g,"logFC"]) )
ggplot( FCs, aes(y=TSLP.IL1RL1, x=TSLP+IL1RL1)) + geom_point(alpha=0.3) + labs(y="logFC\n TSLP IL1RL1\ndouble positive vs. double negative", x= "combined logFC\n TSLP1 IL1RL1\n single positives vs. double negative" )
```
```{r start geo submission process}
ganno <- designm
ganno$sort <- gsub("p","+",ganno$sort); ganno$sort <- gsub("m","-",ganno$sort)
ganno$title <- paste(ganno$mid,ganno$cancer,ganno$sort, sep=".")
ganno <- data.frame( "Sample name"=ganno$libid,"title"=ganno$title, "source name"="colon", "organism" ="mus musculus", "characteristics: cancer"=ganno$cancer, description="","characteristics: sort"=ganno$sort, "characteristics: mouse id"=ganno$mid, molecule="rna","characteristics: genotype"="Foxp3-yfp B6","processed.data.file"="P356-1_AMERGEFC24AND25M5_200817_combined_counts.csv","raw.data.file"=paste0(ganno$libid,"_AMERGEFC24AND25M5_star_alignments.bam"))
ganno <- ganno[order(ganno[,"Sample.name"]),]
write.table(ganno,"../../data/tslp_st2_KO_rnaseq/anno.txt",sep="\t",row.names=FALSE,quote=FALSE)
```
```{r alternative}
libs <- colnames(dge)
countsm <- dge[,libs]
designm <- design_qc[ colnames(countsm),]
designm$control <- ifelse( designm$sort == "CD45+TCR+CD4+Foxp3-CD44low cells","TRUE","FALSE")
designm$control <- relevel( as.factor(designm$control), ref="TRUE")
designm$TSLP <- ifelse( grepl("TSLPR-",design$sort), "neg", "pos" )
designm$IL1RL1 <- ifelse( grepl("Il1rl1-",design$sort), "neg", "pos" )
designm$cancer[ designm$cancer == "Tn"] <- "naive"
designm$cancer <- ifelse(design$cancer == "cancer","pos","neg")
designm$cancer <- relevel(as.factor( designm$cancer), ref="neg")
## design_mat_3
design_mat_3 <- model.matrix(~control + cancer*TSLP*IL1RL1, data=designm)
vwts3 <- voomWithQualityWeights(countsm, design= design_mat_3, plot=T, span=0.1)
vfit3<- lmFit(vwts3, design = design_mat_3)
#vfit_eb <- eBayes(vfit)
#ilc2stimDE <- topTable( vfit_eb, coef=which(colnames(design_mat) == "ILC2.CD137Pos"),number=Inf, sort.by="P")
#table( ilc2stimDE$adj.P.Val <= 0.05)
vfit_eb_3 <- eBayes(vfit3)
comparisons3 <- list()
for( i in 1:ncol(design_mat_3) ){
compname <- colnames(design_mat_3)[i]
#comparisons_ageseason_stim[[compname]] <- topTable (vfit_c_eb_ageseason_stim, coef = i, number=Inf, sort.by="P")
z <- topTable (vfit_eb_3, coef = i, number=Inf, sort.by="P")
comparisons3[[compname]] <- z
print(compname)
sigs <- z$adj.P.Val <= 0.05
print(table(sigs))
#write.table( z, paste0(plotdir,gsub(":",".",compname),"_interactionmodel_DE.txt"),sep="\t",col.names=NA,quote=FALSE)
}
```
```{r alternative, no Tn}
libs <- colnames(dge)
libs <- libs[ design[libs,"sort"] != "CD45+TCR+CD4+Foxp3-CD44low cells" ]
countsm <- dge[,libs]
designm <- design_qc[ colnames(countsm),]
designm$TSLP <- ifelse( grepl("TSLPR-",designm$sort), "neg", "pos" )
designm$IL1RL1 <- ifelse( grepl("Il1rl1-",designm$sort), "neg", "pos" )
designm$cancer <- ifelse(designm$cancer == "cancer","pos","neg")
designm$cancer <- relevel(as.factor( designm$cancer), ref="neg")
## design_mat_3
design_mat_4 <- model.matrix(~cancer*TSLP*IL1RL1, data=designm)
vwts4 <- voomWithQualityWeights(countsm, design= design_mat_4, plot=T, span=0.1)
vfit4<- lmFit(vwts4, design = design_mat_4)
#vfit_eb <- eBayes(vfit)
#ilc2stimDE <- topTable( vfit_eb, coef=which(colnames(design_mat) == "ILC2.CD137Pos"),number=Inf, sort.by="P")
#table( ilc2stimDE$adj.P.Val <= 0.05)
vfit_eb_4 <- eBayes(vfit4)
comparisons4 <- list()
for( i in 1:ncol(design_mat_4) ){
compname <- colnames(design_mat_4)[i]
#comparisons_ageseason_stim[[compname]] <- topTable (vfit_c_eb_ageseason_stim, coef = i, number=Inf, sort.by="P")
z <- topTable (vfit_eb_4, coef = i, number=Inf, sort.by="P")
comparisons4[[compname]] <- z
print(compname)
sigs <- z$adj.P.Val <= 0.05
print(table(sigs))
#write.table( z, paste0(plotdir,"interactionmodel_filtered/",gsub(":",".",compname),"_interactionmodel_DE.txt"),sep="\t",col.names=NA,quote=FALSE)
}
```
```{r alternative, no Tn}
libs <- colnames(dge)
libs <- libs[ design[libs,"sort"] != "CD45+TCR+CD4+Foxp3-CD44low cells" ]
countsm <- dge[,libs]
designm <- design_qc[ colnames(countsm),]
designm$TSLP <- ifelse( grepl("TSLPR-",designm$sort), "neg", "pos" )
designm$IL1RL1 <- ifelse( grepl("Il1rl1-",designm$sort), "neg", "pos" )
designm$cancer <- ifelse(designm$cancer == "cancer","pos","neg")
designm$cancer <- relevel(as.factor( designm$cancer), ref="neg")
## design_mat_3
design_mat_5 <- model.matrix(~cancer*(TSLP+IL1RL1), data=designm)
vwts5 <- voomWithQualityWeights(countsm, design= design_mat_5, plot=T, span=0.1)
vfit5<- lmFit(vwts5, design = design_mat_5)
#vfit_eb <- eBayes(vfit)
#ilc2stimDE <- topTable( vfit_eb, coef=which(colnames(design_mat) == "ILC2.CD137Pos"),number=Inf, sort.by="P")
#table( ilc2stimDE$adj.P.Val <= 0.05)
vfit_eb_5 <- eBayes(vfit5)
comparisons5 <- list()
for( i in 1:ncol(design_mat_5) ){
compname <- colnames(design_mat_5)[i]
#comparisons_ageseason_stim[[compname]] <- topTable (vfit_c_eb_ageseason_stim, coef = i, number=Inf, sort.by="P")
z <- topTable (vfit_eb_5, coef = i, number=Inf, sort.by="P")
comparisons5[[compname]] <- z
print(compname)
sigs <- z$adj.P.Val <= 0.05
print(table(sigs))
#write.table( z, paste0(plotdir,"cancerinteractionmodel_filtered/",gsub(":",".",compname),"_interactionmodel_DE.txt"),sep="\t",col.names=NA,quote=FALSE)
}
```
```{r c2 stuff}
library(msigdbr)
c2 = data.frame( msigdbr(species = "Homo sapiens", category = "C2") )
sets <- unique(c2$gs_name)
sets <- sets[ grepl("IL[0-9]+",sets) | grepl("PROSTAG",sets) | grepl("CYTOK",sets) ]
c2 <- c2[ c2$gs_name %in% sets,]
#c2_list <- lapply( sets, function(set) rownames(comparisons_ageseason_stim[[1]])[ comparisons_ageseason_stim[[1]]$HGNC.symbol %in% c2[ c2$gs_name == set,"human_gene_symbol"] ] )
c2_list <- lapply( sets, function(set) as.character(c2[ c2$gs_name == set,"human_gene_symbol"] ) )
names(c2_list) <- sets
listToFrame <- function(l){
maxN <- max( sapply(l, length))
df <- data.frame( sapply( l, function(g) c(as.character(g),rep("",maxN-length(g))) ) )
return(df)
}
c2_frame <- listToFrame(c2_list)
c2_frame <- c2_frame[, apply(c2_frame, 2, function(g) length(unique(g)) > 5 )]
```
```{r gsea}
library(msigdbr)
untidy_geneset <- function(gs){
gs <- gs[order(gs$gs_name),]
maxlen <- max(table(gs$gs_name))
bounds <- which( !duplicated(gs$gs_name))
bounds <- c(bounds,nrow(gs)+1)
d <- rep("",maxlen)
l <- bounds[2]-bounds[1]
d[1:l] <- gs$gene_symbol[bounds[1]:(bounds[2]-1)]
for( i in 2:(length(bounds)-1) ){
dn <- rep("",maxlen)
l <- bounds[i+1]-bounds[i]
dn[1:l] <- gs$gene_symbol[bounds[i]:(bounds[i+1]-1)]
d <- cbind(d,dn)
}
colnames(d) <- gs$gs_name[!duplicated(gs$gs_name)]
d <- data.frame( rbind( colnames(d),d) )
return(d)
}
hallmark <- msigdbr(species = "Mus musculus", category = "H") %>% dplyr::filter(gs_cat == "H")
hallmark <- untidy_geneset(hallmark)
c7<- msigdbr(species = "Mus musculus", category = "C7")
c7 <- untidy_geneset(c7)
getGSEAS <- function( comps, vwts, genesets, nrots=5000 ){
dmat <- vwts$design
gseas <- list()
for( comp in names(comps) ){
if(grepl("ntercept",comp)){ next }
for( gs in names(genesets ) ){
compname <- paste0( gs, " ", comp )
print(compname)
if( compname %in% names(gseas) ){ next }
r <- roast(
y=vwts,
index=ids2indices( genesets[[gs]], identifiers=gene_key$mgi_symbol[match(rownames(vwts), gene_key$ensembl_gene_id)]),
design=dmat,
contrast= which(colnames(dmat) == comp),
nrot=nrots)
sigsets <- rownames(r)[r$FDR <= 0.05]# | r$FDR.Mixed <= 0.05]
print(length(sigsets))
if(length(sigsets)>0){print( r[sigsets,])}
gseas[[compname]] <- r
#write.table(r,paste0(plotdir,"interactionmodel_filtered/",comp,"_hallmark.txt"),sep="\t",quote=FALSE,col.names=NA)
}
}
return(gseas)
}
getGSEAS_contrast <- function( comps, vwts,contmat, genesets, nrots=5000 ){
dmat <- vwts$design
gseas <- list()
for( comp in names(comps) ){
if(grepl("ntercept",comp)){ next }
for( gs in names(genesets ) ){
compname <- paste0( gs, " ", comp )
print(compname)
if( compname %in% names(gseas) ){ next }
r <- roast(
y=vwts,
index=ids2indices( genesets[[gs]], identifiers=gene_key$mgi_symbol[match(rownames(vwts), gene_key$ensembl_gene_id)]),
design=dmat,
contrast= contmat[colnames(dmat),comp],
nrot=nrots)
sigsets <- rownames(r)[r$FDR <= 0.05]# | r$FDR.Mixed <= 0.05]
print(length(sigsets))
if(length(sigsets)>0){print( r[sigsets,])}
gseas[[compname]] <- r
#write.table(r,paste0(plotdir,"interactionmodel_filtered/",comp,"_hallmark.txt"),sep="\t",quote=FALSE,col.names=NA)
}
}
return(gseas)
}
genesets <- list(hallmark=hallmark)
gseas1 <- getGSEAS_contrast( comparisons, vwts, cont.matrix, genesets, 10000)
sigsets <- c()
for( compname in names(gseas1)[6:11]){
#for( compname in names(gseas1)[ grepl("Cancer",names(gseas1)) & grepl("TSLPR",names(gseas1))]){
fname <- paste0( plotdir,"gseas/",gsub(" ", ".", compname),".txt")
print(compname)
print( head(gseas1[[compname]]))
sigsets <- unique(c(rownames(gseas1[[compname]])[gseas1[[compname]]$FDR <= 0.05]), sigsets)
write.table( gseas1[[compname]], fname, quote=FALSE,col.names=NA,sep="\t")
}
sigsets <- rownames(gseas1[["hallmark TSLPRpos.IL1RL1pos.Cancer"]])[gseas1[["hallmark TSLPRpos.IL1RL1pos.Cancer"]]$FDR <= 0.05]
cancernames <- names(gseas1)[ grepl("Cancer",names(gseas1)) & grepl("TSLPR",names(gseas1))]
gseasfdrs <- do.call(rbind, lapply(gseas1[cancernames], function(g) -log10(g[sigsets,"PValue"]) * ifelse(g[sigsets,"Direction"] == "Up", 1, -1) ) )
colnames(gseasfdrs) <- gsub("HALLMARK","",gsub("_"," ",sigsets))
rownames(gseasfdrs) <- c("DN","TSLPR+","IL1RL1+","DP")
col_fun <- colorRamp2(c(-4, 0, 4), c("blue", "white", "red"))
Heatmap(t(gseasfdrs), name="-log10 p-value\ndirectional", col=col_fun)
gseas4 <- getGSEAS( comparisons4, vwts4, genesets, 10000)
gseas3 <- getGSEAS( comparisons3, vwts3, genesets, 10000)
for( comp in names(gseas3)) {
print(comp)
print(gseas3[[comp]][1:10,c("NGenes","Direction","FDR")])
}
gseas6 <- getGSEAS( comparisons6, vwts6, genesets, 10000)
gseas7 <- getGSEAS( comparisons7, vwts7, genesets, 10000)
comp <- "TSLPRpos.IL1RL1pos.Cancer"
hr <- roast(
y=vwts,
index=ids2indices( hallmark, identifiers=gene_key$mgi_symbol[match(rownames(vwts), gene_key$ensembl_gene_id)]),
design=design_mat,
contrast=cont.matrix[colnames(design_mat),comp],
nrot=10000)
head(hr)
genesets <- list(hallmark=hallmark)
gseas <- list()
for( comp in names(comparisons4) ){
if(grepl("ntercept",comp)){ next }
print(comp)
for( gs in names(genesets ) ){
compname <- paste0( gs, " ", comp )
if( compname %in% names(gseas) ){ next }
r <- roast(
y=vwts4,
index=ids2indices(hallmark, identifiers=gene_key$mgi_symbol[match(rownames(vwts4), gene_key$ensembl_gene_id)]),
design=design_mat_4,
contrast=which( colnames(design_mat_4) == comp),
nrot=5000)
sigsets <- rownames(r)[r$FDR <= 0.05]# | r$FDR.Mixed <= 0.05]
print(length(sigsets))
if(length(sigsets)>0){print( r[sigsets,])}
gseas[[compname]] <- r
write.table(r,paste0(plotdir,"interactionmodel_filtered/",comp,"_hallmark.txt"),sep="\t",quote=FALSE,col.names=NA)
}
}
```
``` {r goana_and_kegga}
library("org.Mm.eg.db")
tidy_bh_go <- function(go){
# For Goana output
if("Term" %in% colnames(go) ){
up <- go[,c("Term","Ont","N","Up","P.Up")]
down <- go[,c("Term","Ont","N","Down","P.Down")]
colnames(down) <- c("Term","Ont","N","N Genes Changed","PValue")
colnames(up) <- c("Term","Ont","N","N Genes Changed","PValue")
}
# For Kegga output
else{
up <- go[,c("Pathway","N","Up","P.Up")]
down <- go[,c("Pathway","N","Down","P.Down")]
colnames(down) <- c("Pathway","N","N Genes Changed","PValue")
colnames(up) <- c("Pathway","N","N Genes Changed","PValue")
}
down$direction <- rep("Down",nrow(down))
up$direction <- rep("Up",nrow(down))
go <- rbind(up,down)