ave_similarities: Average similaries for each considered perturbed size

In linbingqing/stableDE: Stability of methods for differential expression analysis of RNA-Seq data

Description

Calculate average similaries between sets of selected features from the generated datasets for each perturbed size and the set of DE features from the original dataset

Usage

ave_similarities(counts, group, DEmethod = c("edgeR", "edgeR_robust",
  "DESeq_glm", "DESeq2", "samr_SAMseq", "EBSeq", "limma_voom"),
  alpha.vec = seq(0.01, 0.1, 0.01), cut.point = 0.05, nrep = 5)

Arguments

counts	a matrix of raw read counts
group	factor that giving the experimental condition for each sample
DEmethod	differential expression analysis method: edgeR, edgeR_robust, DESeq_glm, DESeq2, samr_SAMseq, EBSeq or limma_voom.
alpha.vec	numeric vector of perturbed size, default values is seq(0.01, 0.1, 0.01).
cut.point	threshold for adjusted P values, default value is 0.05.
nrep	number of repetition for each perturbed size, default value is 5.

Value

ave_sim matrix of perturbed size and the corresponding average similaries.

pval.mat matrix of p-values and adjusted pvalues for the original dataset

Examples

 bottomly <- RNAseq_data(sel_size = 3)
 res <- ave_similarities(counts = bottomly$counts_evaluation,
     group = bottomly$group_evaluation, DEmethod = "edgeR")

linbingqing/stableDE documentation built on May 29, 2019, 3:06 a.m.