doAllDiffAnalysis: Differential Analysis for specific clusters (according to...

In HuobinTan/scVAT: Single Cell Visualization Analysis Toolkit

Description

Differential Analysis for specific clusters (according to group.key)

Usage

doAllDiffAnalysis(vat, group.key = "cluster", use.genes = NULL,
  method = "t", min.logfc = 0.25, only.pos = FALSE, min.avg = 0.1,
  min.diff.avg = -Inf, top.num = Inf, min.cells = 1,
  verbose = TRUE)

Arguments

vat	VAT entity
group.key	Key value storing Group from colnames([e-mail]) for Differential Analysis
use.genes	Genes ,Default is to use all genes
method	Method which does differential analysise. Available options are: "t" : t-test (default) "wilcox" : Wilcoxon rank sum test "DESeq2 : DE based on a model using the negative binomial distribution (Love et al, Genome Biology, 2014), required DESeq2 library.
min.logfc	At least X-fold difference (log-scale) between the two groups of cells. Default is 0.25
only.pos	Only return positive markers (FALSE by default)
min.avg	only compare genes which average min.avg cells in either of two groups, Default is 0.1
min.diff.avg	only compare genes which minimum difference between two groups. Default is -Inf
top.num	only return top.num results. Default is Inf, and return all results
min.cells	Minimum number of cells expressing the gene in at least min.cells. Default is 1
verbose	Show the progress bar

Value

p-value adjustment is performed using bonferroni correction based on the total number of genes in the dataset.

HuobinTan/scVAT documentation built on May 31, 2019, 2:20 p.m.