differExp_discrete {anamiR} | R Documentation

## Find differential expression genes or miRNAs from given 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"
    )

[Package anamiR version 1.12.0 Index]