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AnalyteExplorer

R-CMD-check docker

The goal of AnalyteExplorer is to pre-process data for the AnalyteExplorer module in ImmuneSpace.

Installation

You can install the development version of AnalyteExplorer from GitHub with:

# install.packages("remotes")
remotes::install_github("RGLab/AnalyteExplorer")

Workflow with ImmuneSpace

library(AnalyteExplorer)
library(UpdateAnno)
options(debug_dir = tempdir())
labkey.url.base <- "https://datatools.immunespace.org"
labkey.url.path <- "/AnalyteExplorer"

genes <- process_data("genes")
validate(genes)
res <- update_table(genes)

cohorts <- process_data("cohorts")
validate(cohorts)
res <- update_table(cohorts)

btm <- process_data("blood_transcription_modules")
validate(btm)
res <- update_table(btm)

signatures <- process_data("gene_signatures")
validata(signautres)
res <- update_table(signatures)

summaries <- process_data("gene_expression_summaries")
validate(summaries)
res <- update_table(summaries)

Data Processing

process_blood_transcription_modules()

id name genes matched_gene_ontology_terms number_of_genes module_category
M0 targets of FOSL1/2 (M0) CCL2, COL1A2, DCN, IL6, CXCL8, LIF, MGP, MMP1, MMP2, MMP9, PLAU, THBD extracellular space (11), extracellular region (11), protein binding (9) 12 TF targets
M1.0 integrin cell surface interactions (I) (M1.0) COL1A1, COL1A2, COL5A1, DPYSL3, MYH10, NRP1, PTK2, RHOC, RRAS, SEMA6A protein binding (26), axon guidance (20), extracellular region (16) 29 molecular function
M1.1 integrin cell surface interactions (II) (M1.1) AHSP, ALAD, ALAS2, CPOX, E2F2, FECH, GATA1, HEMGN, HMBS, PLEK2, TMOD1 protein binding (12), extracellular region (12), extracellular matrix structural constituent (9) 12 molecular function
M2.0 extracellular matrix (I) (M2.0) CD1D, HLA-DMA, HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DQA2, METTL7A, WDFY4 protein binding (27), extracellular region (26), extracellular matrix (21) 30 location

process_gene_expression_summaries()

  • This function creates a gene expression table by cohort, timepoint, and analyte type (gene, blood transcript module, or gene signature).
  • Processing steps:
    1. Fetch gene expression matrices and metadata from ImmuneSpace and combine them into one ExpressionSet object.
      • Remove genes that are not available in all expression matrices.
    2. Remove samples that have negative timepoint and select one timepoint if sample has multiple baseline timepoints.
    3. Create gene expression table summarized by analyte type.
      1. In sample level, when summarizing by blood transcript module or gene signature, compute geometric mean of the expression values of the genes in the module or signature
      2. In cohort level, compute the fold change of the expression values for all combinations of timepoints comparing to the baseline timepoint.
      3. Compute mean and standard deviation of those fold change values by analyte type
    4. Merge the three summarized tables
cohort sample_type study_accession condition timepoint analyte_id analyte_type mean_fold_change sd_fold_change id
healthy aldults Whole blood SDY1529 Yellow_Fever 0 A1CF gene 0 0 1
healthy aldults Whole blood SDY1529 Yellow_Fever 0 A2M gene 0 0 2
healthy aldults Whole blood SDY1529 Yellow_Fever 3 M0 blood transcription module -0.0385090 0.0875805 3894315
healthy aldults Whole blood SDY1529 Yellow_Fever 3 M1.0 blood transcription module -0.0181510 0.1051648 3894316
healthy aldults Whole blood SDY1529 Yellow_Fever 3 21357945_1_8 gene signature 0.7102219 0.5402085 4028003
healthy aldults Whole blood SDY1529 Yellow_Fever 3 21357945_2_9 gene signature 0.1105955 0.5026520 4028004