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.
A non-negative integer matrix of scRNA-seq raw read counts or a
A vector of factor which specifies the two groups to be compared, corresponding to the columns in the counts matrix.
If FALSE (default), no parallel computation is used; if TRUE, parallel computation using
An optional parameter object passed internally to
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.
norm_total_mean_1, norm_total_mean_2: Mean of normalized read counts of group 1 and group 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.
TestData, a test dataset for DEsingle.
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# 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)
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