RegenrichSet-class: RegenrichSet class

In RegEnrich: Gene regulator enrichment analysis

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.

RegEnrich documentation built on March 7, 2021, 2 a.m.