GSEA_res: Pipeline of anamiR is applied to given output from GSEA_ana.

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

Description

This function will use differExp_discrete and negative_cor to do the deeper analysis of given data which is from GSEA_ana.

Usage

GSEA_res(table, pheno.data, class, DE_method = c("t.test", "limma",
  "wilcox.test", "DESeq"), limma.trend = FALSE, t_test.var = FALSE,
  log2 = FALSE, p_adjust.method = "BH", cor_cut = -0.3)

Arguments

table	list format containing both selected gene and miRNA expression data for each chosen pathway. output of GSEA_ana
pheno.data	phenotype data.
class	string. Choose one features from all rows of phenotype data.
DE_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_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".
cor_cut	an numeric value indicating a threshold of correlation coefficient for every potential miRNA-genes interactions. Default is -0.3, however, if no interaction pass the threshold, this function would add 0.2 value in threshold until at least one interaction passed the threshold.

Value

list format containing matrix for each chosen pathway. The format of matrix is like the output from negative_cor.

See Also

differExp_discrete and negative_cor.

Examples

## Load example data

require(data.table)

cc <- system.file("extdata", "pheno_data.csv", package = "anamiR")
pheno.data <- fread(cc, fill = TRUE, header = TRUE)

## adjust data format
pheno_name <- pheno.data[["Sample"]]
pheno.data <- pheno.data[, -1]
pheno.data <- as.matrix(pheno.data)
row.names(pheno.data) <- pheno_name
data(table_pre)

result <- GSEA_res(table = table_pre, pheno.data = pheno.data,
 class = "ER", DE_method = "limma", cor_cut = 0)

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