cTRAP: cTRAP package

In cTRAP: Identification of candidate causal perturbations from differential gene expression data

Description

Compare differential gene expression results with those from big datasets (e.g. CMap), allowing to infer which types of perturbations may explain the observed difference in gene expression.

Details

Input: To use this package, a named vector of differentially expressed gene metric is needed, where its values represent the significance and magnitude of the differentially expressed genes (e.g. t-statistic) and its names are gene symbols.

Workflow: The differentially expressed genes will be compared against selected perturbation conditions by:

• Spearman or Pearson correlation with z-scores of differentially expressed genes after perturbations from CMap. Use function rankSimilarPerturbations with method = "spearman" or method = "pearson"

• Gene set enrichment analysis (GSEA) using the (around) 12 000 genes from CMap. Use function rankSimilarPerturbations with method = gsea.

Available perturbation conditions for CMap include:

• Cell line(s).

• Perturbation type (gene knockdown, gene upregulation or drug intake).

• Drug concentration.

• Time points.

Values for each perturbation type can be listed with getCMapPerturbationTypes()

Output: The output includes a data frame of ranked perturbations based on the associated statistical values and respective p-values.

cTRAP documentation built on Nov. 8, 2020, 10:58 p.m.