mainAnalysis {RNAither} | R Documentation

## Wrapper function for full automated analysis

### Description

Performs a standard analysis of the data (quality and statistics) from a dataset file.

### Usage

    mainAnalysis(header, dataset, flagForSameExp, listOfNormalizations, listOfArgs4norm,
    listOfStatTests, listOfArgs4stat, multTestAdj, hitScoringVec1, hitScoringVec2,
    posNegFlag, flag4Gsea, vecOfChannels, whichOnto)

### Arguments

`header` | the header of a dataset file generated with `generateDatasetFile`
`dataset` | an R data frame generated with `generateDatasetFile`
`flagForSameExp` | either 0 or 1. If 1, all experiments defined in the column `ScreenNb` in the dataset file must have the same design (same type and same number of replicates - exact plate layout is irrelevant) so that Spearman's correlation coefficient can be computed between experiments (each with summarized replicates)
`listOfNormalizations` | a list of the normalization function to apply. Can be `LiWongRank`, `varAdjust`, `divNorm`, `quantileNormalization`, `BScore`, `ZScore`, `ZScorePerScreen`, `subtractBackground`, `lowessNorm`, `controlNorm`
`listOfArgs4norm` | a list containing, for each element of `listofnormalizations`, the arguments to be passed on
`listOfStatTests` | a list of the statistical tests to perform. Can be `Ttest`, `MannWhitney`, `RankProduct`
`listOfArgs4stat` | a list containing, for each element of `listofstattests`, the arguments to be passed on
`multTestAdj` | indicates the p-value correction for multiple testing - one of `"holm"`, `"hochberg"`, `"hommel"`, `"bonferroni"`, `"BH"`, `"BY"`, `"fdr"`, or `"none"` (Type `?p.adjust` for details))
`hitScoringVec1` | a vector of length 3 indicating (in that order): - scoring according to p-value (0: no, 1: yes) - scoring according to ZScore with ZScore < threshold (0: no, 1: yes), or according to ZScore < threshold and p-value < hitScoringVec2[1] (2) - scoring according to ZScore with ZScore > threshold (0: no, 1: yes), or according to ZScore > threshold and p-value < hitScoringVec2[1] (2). If hitScoringVec1[2] or hitScoringVec1[3] are equal to 2, hitScoringVec1[1] must be equal to one, otherwise p-values will not be computed.
`hitScoringVec2` | a vector of length 3 indicating the thresholds for hitscoringvec1
`posNegFlag` | either 0 (no controls available) or 1 (controls available)
`flag4Gsea` | Can be: - either 0: No GSEA analysis is performed - or 1: A GSEA analysis is performed for each hit scoring method - or a binary vector that allows to choose which hit scoring method(s) will be used for a GSEA analysis. Hit scoring methods are sorted as follows: first, hits are scored according to the p-values of each test specified in `listOfStatTests` . Then, if the option of scoring hits according to p-values and Intensities is chosen (see `hitScoringVec1` , for each test, a hit vector is generated. Finally, if the option of scoring hits according to Intensities only is chosen, hit vectors are generated for this option.
`vecOfChannels` | a character vector containing the names of the channels to be used for quality plots, for example `"SigIntensity"` or `"NbCells"`
`whichOnto` | one of the three GO hierarchies: `"biological_process"` , `"molecular_function"` or `"cellular_component"` - used for the GSEA analysis

### Value

Generates the html output files `index.html` and `indexnorm.html` containing the quality analysis of raw and normalized data, respectively, and `stats.html`, containing the statistical analysis. If several normalization methods are applied, an `indexnorm` file is generated after each.

### Examples

    data(exampleHeader, package="RNAither")
    data(exampleDataset, package="RNAither")

    mainAnalysis(header, dataset, 0, list(controlNorm), list(list(1, 0, "SigIntensity", 1)),
    list(Ttest, MannWhitney), list(list("l", 1, "SigIntensity", "GeneName"),
    list("l", 1, "SigIntensity", "GeneName")), "none", c(1, 0, 0), c(0.05, 0, 0), 1,
    1, c("SigIntensity", "NbCells"), "biological_process")

[Package RNAither version 1.4.2 Index]