DEsingle: DEsingle: Detecting differentially expressed genes from...

In miaozhun/DEsingle: DEsingle for detecting three types of differential expression in single-cell RNA-seq data

DEsingle: Detecting differentially expressed genes from scRNA-seq data

Description

This function is used to detect differentially expressed genes between two specified groups of cells in a raw read counts matrix of single-cell RNA-seq (scRNA-seq) data. It takes a non-negative integer matrix of scRNA-seq raw read counts or a SingleCellExperiment object as input. So users should map the reads (obtained from sequencing libraries of the samples) to the corresponding genome and count the reads mapped to each gene according to the gene annotation to get the raw read counts matrix in advance.

Usage

DEsingle(counts, group, parallel = FALSE, BPPARAM = bpparam())

Arguments

counts	A non-negative integer matrix of scRNA-seq raw read counts or a SingleCellExperiment object which contains the read counts matrix. The rows of the matrix are genes and columns are samples/cells.
group	A vector of factor which specifies the two groups to be compared, corresponding to the columns in the counts matrix.
parallel	If FALSE (default), no parallel computation is used; if TRUE, parallel computation using BiocParallel, with argument BPPARAM.
BPPARAM	An optional parameter object passed internally to bplapply when parallel=TRUE. If not specified, bpparam() (default) will be used.

Value

A data frame containing the differential expression (DE) analysis results, rows are genes and columns contain the following items:

• theta_1, theta_2, mu_1, mu_2, size_1, size_2, prob_1, prob_2: MLE of the zero-inflated negative binomial distribution's parameters of group 1 and group 2.

• total_mean_1, total_mean_2: Mean of read counts of group 1 and group 2.

• foldChange: total_mean_1/total_mean_2.

• norm_total_mean_1, norm_total_mean_2: Mean of normalized read counts of group 1 and group 2.

• norm_foldChange: norm_total_mean_1/norm_total_mean_2.

• chi2LR1: Chi-square statistic for hypothesis testing of H0.

• pvalue_LR2: P value of hypothesis testing of H20 (Used to determine the type of a DE gene).

• pvalue_LR3: P value of hypothesis testing of H30 (Used to determine the type of a DE gene).

• FDR_LR2: Adjusted P value of pvalue_LR2 using Benjamini & Hochberg's method (Used to determine the type of a DE gene).

• FDR_LR3: Adjusted P value of pvalue_LR3 using Benjamini & Hochberg's method (Used to determine the type of a DE gene).

• pvalue: P value of hypothesis testing of H0 (Used to determine whether a gene is a DE gene).

• pvalue.adj.FDR: Adjusted P value of H0's pvalue using Benjamini & Hochberg's method (Used to determine whether a gene is a DE gene).

• Remark: Record of abnormal program information.

Author(s)

Zhun Miao.

See Also

DEtype, for the classification of differentially expressed genes found by DEsingle.

TestData, a test dataset for DEsingle.

Examples

# Load test data for DEsingle
data(TestData)

# Specifying the two groups to be compared
# The sample number in group 1 and group 2 is 50 and 100 respectively
group <- factor(c(rep(1,50), rep(2,100)))

# Detecting the differentially expressed genes
results <- DEsingle(counts = counts, group = group)

# Dividing the differentially expressed genes into 3 categories
results.classified <- DEtype(results = results, threshold = 0.05)

miaozhun/DEsingle documentation built on Jan. 25, 2023, 4:27 p.m.