CalcCGS: Calculate cluster-wise gene statistics for sCVdata

In BaderLab/scClustViz: Differential Expression-based scRNAseq Cluster Assessment and Viewing

Calculate cluster-wise gene statistics for sCVdata

Description

Calculates gene summary statistics per cluster for the clusters in an sCVdata object, using the gene expression matrix from the input data object. This is called by CalcSCV and you shouldn't need to call it on its own.

Usage

CalcCGS(sCVd, inD)

## S4 method for signature 'sCVdata'
CalcCGS(sCVd, inD)

Arguments

sCVd	An sCVdata object.
inD	The input dataset. An object of class seurat or SingleCellExperiment. Other data classes are not currently supported. Please submit requests for other data objects here!

Details

To help track its progress, this function uses progress bars from pbapply. To disable these, set pboptions(type="none"). To re-enable, set pboptions(type="timer").

Value

The function returns a list of dataframes. Each list element contains a named list of clusters at that resolution. Each of those list elements contains a dataframe where each sample is a gene, containing the following variables: DR is the proportion of cells in the cluster in which that gene was detected. MDGE is mean normalized gene expression for that gene in only the cells in which it was detected (see meanLogX for mean calculation). MGE is the mean normalized gene expression for that gene in all cells of the cluster (see meanLogX for mean calculation).

Methods (by class)

• sCVdata: Calculate cluster-wise gene stats for sCVdata

See Also

CalcSCV for wrapper function to calculate all statistics for an sCVdata object, and fx_calcCGS for the internal function performing the calculations.

Examples

## Not run: 
ClustGeneStats(your_sCV_obj) <- CalcCGS(sCVd=your_sCV_obj,
                                        inD=your_scRNAseq_data_object)

## End(Not run)

BaderLab/scClustViz documentation built on Sept. 10, 2023, 11:51 p.m.