diffAnalyses {psichomics} | R Documentation

## Perform statistical analyses

### Description

Perform statistical analyses

### Usage

    diffAnalyses(
      data,
      groups = NULL,
      analyses = c("wilcoxRankSum", "ttest", "kruskal", "levene", "fligner"),
      pvalueAdjust = "BH",
      geneExpr = NULL,
      inputID = "sparklineInput"
    )

### Arguments

`data` | Data frame or matrix: gene expression or alternative splicing quantification
`groups` | Named list of characters (containing elements belonging to each group) or character vector (containing the group of each individual sample); if `NULL`, sample types are used instead when available, e.g. normal, tumour and metastasis
`analyses` | Character: statistical tests to perform (see Details)
`pvalueAdjust` | Character: method used to adjust p-values (see Details)
`geneExpr` | Character: name of the gene expression dataset (only required for density sparklines available in the interactive mode)
`inputID` | Character: identifier of input to get attributes of clicked event (Shiny only)

### Details

The following statistical analyses may be performed simultaneously via the `analysis` argument:

- `ttest` - Unpaired t-test (2 groups)

- `wilcoxRankSum` - Wilcoxon Rank Sum test (2 groups)

- `kruskal` - Kruskal test (2 or more groups)

- `levene` - Levene's test (2 or more groups)

- `fligner` - Fligner-Killeen test (2 or more groups)

- `density` - Sample distribution per group (only usable through the visual interface)

The following p-value adjustment methods are supported via the `pvalueAdjust` argument:

- `none`: do not adjust p-values

- `BH`: Benjamini-Hochberg's method (false discovery rate)

- `BY`: Benjamini-Yekutieli's method (false discovery rate)

- `bonferroni`: Bonferroni correction (family-wise error rate)

- `holm`: Holm's method (family-wise error rate)

- `hochberg`: Hochberg's method (family-wise error rate)

- `hommel`: Hommel's method (family-wise error rate)

### Value

Table of statistical analyses

### See Also

Other functions to perform and plot differential analyses: `plotDistribution()`

### Examples

    # Calculate PSI for skipped exon (SE) and mutually exclusive (MXE) events
    eventType <- c("SE", "MXE")
    annot <- readFile("ex_splicing_annotation.RDS")
    junctionQuant <- readFile("ex_junctionQuant.RDS")

    psi <- quantifySplicing(annot, junctionQuant, eventType=c("SE", "MXE"))
    group <- c(rep("Normal", 3), rep("Tumour", 3))
    diffAnalyses(psi, group)

[Package psichomics version 1.16.0 Index]