This function is used to classify the differentially expressed genes of single-cell RNA-seq (scRNA-seq) data found by
DEsingle. It takes the output data frame from
DEsingle as input.
A output data frame from
A number of (0,1) to specify the threshold of FDR.
A data frame containing the differential expression (DE) analysis results and DE gene types and states.
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.
Type: Types of DE genes. DEs represents different expression status; DEa represents differential expression abundance; DEg represents general differential expression.
State: State of DE genes, up represents up-regulated; down represents down-regulated.
DEsingle, for the detection of differentially expressed genes from scRNA-seq data.
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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