cTRAP {cTRAP} | R Documentation

## cTRAP package

### 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.

[Package cTRAP version 1.5.0 Index]