multi_Differ {anamiR} | R Documentation

## Find differential expression groups of each genes or miRNA from 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")

[Package anamiR version 1.8.0 Index]