multi_Differ: Find differential expression groups of each genes or miRNA...

In anamiR: An integrated analysis package of miRNA and mRNA expression data

Description

This function will apply anova ,a statistical methods, for each gene or miRNA (row) to find not only whether expression data of multiple groups differential expressed or not, but also tell specifically two groups from all are differential expression.

Usage

multi_Differ(se, class, anova_p_value = 0.05,
  post_hoc = c("scheffe.test", "duncan.test", "HSD.test"),
  post_hoc_p_value = 0.05)

Arguments

se	SummarizedExperiment for input format.
class	string. Choose one features from all rows of phenotype data.
anova_p_value	an numeric value indicating a threshold of p-value from anova for every genes or miRNAs (rows). Default is 0.05.
post_hoc	post hoc test for anova, including "scheffe.test", "HSD.test", "duncan.test".
post_hoc_p_value	an numeric value indicating a threshold of p-value from post hoc test for every genes or miRNAs (rows). Default is 0.05.

Value

data.frame format with extra columns containing information about differential expressed groups among all.

See Also

aov for fit an analysis of variance model.

Examples

## Use the internal dataset
data("mirna", package = "anamiR", envir = environment())
data("pheno.mirna", package = "anamiR", envir = environment())

## SummarizedExperiment class
require(SummarizedExperiment)
mirna_se <- SummarizedExperiment(
 assays = SimpleList(counts=mirna),
 colData = pheno.mirna)

## Finding differential miRNA from miRNA expression data with anova
aov <- multi_Differ(se = mirna_se, class = "Subtype",
   post_hoc = "scheffe.test")

anamiR documentation built on Oct. 31, 2019, 8:55 a.m.