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.

Optimised to run in ShinyProxy with Celery/Flower backend with argument `shinyproxy = TRUE`.

### Usage

    cTRAP(
      ...,
      commonPath = "data",
      expire = 14,
      fileSizeLimitMiB = 50,
      flowerURL = NULL,
      port = getOption("shiny.port"),
      host = getOption("shiny.host", "127.0.0.1")
    )

### Arguments

`...` | Objects
`commonPath` | Character: path where to store data common to all sessions
`expire` | Character: days until a session expires (message purposes only)
`fileSizeLimitMiB` | Numeric: file size limit in MiB
`flowerURL` | Character: Flower REST API's URL (`NULL` to avoid using Celery/Flower backend)
`port` | The TCP port that the application should listen on. If the `port` is not specified, and the `shiny.port` option is set (with `options(shiny.port = XX)`), then that port will be used. Otherwise, use a random port between 3000:8000, excluding ports that are blocked by Google Chrome for being considered unsafe: 3659, 4045, 5060, 5061, 6000, 6566, 6665:6669 and 6697. Up to twenty random ports will be tried.
`host` | The IPv4 address that the application should listen on. Defaults to the `shiny.host` option, if set, or `"127.0.0.1"` if not. See Details.

### 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.

### Value

Launches result viewer and plotter (returns `NULL`)

### See Also

Other visual interface functions: `launchCMapDataLoader()`, `launchDiffExprLoader()`, `launchDrugSetEnrichmentAnalyser()`, `launchMetadataViewer()`, `launchResultPlotter()`

[Package cTRAP version 1.12.0 Index]