RegenrichSet-class {RegEnrich} | R Documentation

## RegenrichSet class

### Description

The `RegenrichSet` is the fundamental class that RegEnrich package is working with.

### Slots

`assayRaw`

  `matrix`, the initial raw expression data.

`colData`

  `DataFrame` object, indicating sample information. Each row represent a sample and each column represent a feature of samples.

`assays`

  `SimpleList` object, containing the expression data after filtering (and after Variance Stabilizing Transformation, i.e. VST, if the differential analysis method is 'Wald_DESeq2' or 'LRT_DESeq2').

`elementMetadata`

  DataFrame object, a slot for saving results by differential expression analysis, containing at least three columns:'gene', 'p' and 'logFC'.

`topNetwork`

  `TopNetwork` object, a slot for saving top network edges. After regulator-target network inference, a `TopNetwork-class` object is assigned to this slot, containing only top ranked edges in the full network. Default is NULL.

`resEnrich`

  `Enrich` object, a slot for saving enrichment analysis either by Fisher's exact test (FET) or gene set enrichment analysis (GSEA).

`resScore`

  `Score` object, a slot for saving regulator ranking results. It contains five components, which are 'reg' (regulator), 'negLogPDEA' (-log10(p values of differential expression analysis)), 'negLogPEnrich' (-log10(p values of enrichment analysis)), 'logFC' (log2 fold changes), and 'score' (RegEnrich ranking score).

`paramsIn`

  list. The parameters used in the whole RegEnrich analysis. This slot can be updated by respecifying arguments in each step of RegEnrich analysis.

`paramsOut`

  a list of four elements: DeaMethod (differential expression method), networkType (regulator-target network construction method), percent (what percentage of edges from the full network is used), and enrichTest (enrichment method). By default, each element is NULL.

`network`

  `TopNetwork` object, a slot for saving a full network.

[Package RegEnrich version 1.0.1 Index]