diffNoisyGenes: Function for extracting genes with elevated variability in a...

In RaceID: Identification of Cell Types, Inference of Lineage Trees, and Prediction of Noise Dynamics from Single-Cell RNA-Seq Data

Function for extracting genes with elevated variability in a cluster

Description

This function extracts genes with significantly elevated variability in a cluster on a basis of a Wilcoxon rank sum-test between cells in a cluster and all remaining cells.

Usage

diffNoisyGenes(noise, cl, set, bgr = NULL, no_cores = 1)

Arguments

noise	List object with the background noise model and a variability matrix, returned by the compNoise function.
cl	List object with clustering information, returned by the graphCluster function.
set	Postive integer number or vector of integers corresponding to valid cluster numbers. The function reports genes with elevated variability in all clusters contained in set.
bgr	Postive integer number or vector of integers corresponding to valid cluster numbers. Background set for comparison. The function reports genes with elevated variability in all clusters contained in set compared to clusters in bgr. Default is NULL and the comparison is against all clusters not in set.
no_cores	Positive integer number. Number of cores for multithreading. If set to NULL then the number of available cores minus two is used. Default is NULL.

Value

Data.frame reporting the log2 fold change between clusters in set and the remaining clusters and the p-value for elevated variability for each genes. Rows are ordered by decreasing log2 fold change.

Examples

res <- pruneKnn(intestinalDataSmall,knn=10,alpha=1,no_cores=1,FSelect=FALSE)
noise <- compNoise(intestinalDataSmall,res,pvalue=0.01,genes = NULL,no_cores=1)
cl <- graphCluster(res,pvalue=0.01)
ngenes <- diffNoisyGenes(noise,cl,c(1,2),no_cores=1)

RaceID documentation built on Sept. 28, 2023, 5:06 p.m.