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

Description Usage Arguments Value See Also Examples

View source: R/multi_Differ.R

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

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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

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## 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")

AllenTiTaiWang/anamiR documentation built on May 5, 2019, 4:55 a.m.