SE_fun: Identify highly informative genes using S-E model

In RyanYip-Kat/yipCat: Some function like ArchR style in Seurat Object

Description

Use S-E curve to identify highly informative genes.

Usage

SE_fun(expr, span = 0.5, r = 1, mt.method = "fdr", if.adj = T)

Arguments

expr	The expression matrix. Rows should be genes and columns should be cells.
span	The parameter α which controls the degree of smoothing.
r	A small fixed value to avoid log(0) of mean gene expression levels. The default value of r is set to 1, but can also be set to other values such as 0.1 and 0.01.
mt.method	The multiple testing method used in p.adjust.
if.adj	Whether to apply multiple testing method to adjust p.value.

Value

A tibble object with seven columns:

* Gene, the gene name.

* mean.expr, the mean expression levels of genes.

* entropy, the expected expression entropy from a given mean gene expression.

* fit, the mean expression levels of genes.

* ds, the entropy reduction against the null expectation.

* p.value, the significance of ds.

* p.adj, adjusted P value.

Examples

ent.res <- SE_fun(expr, span = 0.1, r = 1, mt.method = "fdr")

RyanYip-Kat/yipCat documentation built on Dec. 18, 2021, 11:55 a.m.