differExp_discrete: Find differential expression genes or miRNAs from given...

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

Description

This function will apply one of three statistical methods, including t.test, wilcox.test and limma, to find differential expression genes or miRNAs with, discrete phenotype data, and then filter the genes or miRNAs (rows) which have bigger p-value than cutoff.

Usage

differExp_discrete(se, class, method = c("t.test", "limma",
  "wilcox.test", "DESeq"), limma.trend = FALSE, t_test.var = FALSE,
  log2 = FALSE, p_value.cutoff = 0.05, p_adjust.method = "BH",
  logratio = 0.5)

Arguments

se	SummarizedExperiment for input format.
class	string. Choose one features from all rows of phenotype data.
method	statistical method for finding differential genes or miRNAs, including "t.test", "wilcox.test", "limma". Default is "t.test".
limma.trend	logical, only matter when limma is chosen to be the method. From function eBayes.
t_test.var	logical, only matter when limma is chosen to be the method. Whether to treat the two variances as being equal. From function t.test
log2	logical, if this data hasn't been log2 transformed yet, this one should be TRUE Default is FALSE.
p_value.cutoff	an numeric value indicating a threshold of p-value for every genes or miRNAs (rows). Default is 0.05.
p_adjust.method	Correction method for multiple testing. (If you are using DESeq for method, this param would not affect the result) From function p.adjust. Default is "BH".
logratio	an numeric value indicating a threshold of logratio for every genes or miRNAs (rows). Default is 0.5.

Value

data expression data in matrix format, with sample name in columns and gene symbol or miRNA name in rows.

See Also

t.test for Student's t-Test; wilcox.test for Wilcoxon Rank Sum and Signed Rank Tests.

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 t.test
mirna_d <- differExp_discrete(
   se = mirna_se,
   class = "ER",
   method = "t.test"
)

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