mainAnalysis: Wrapper function for full automated analysis

In RNAither: Statistical analysis of high-throughput RNAi screens

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.

Note

This function is deprecated and kept only for backwards compatibility. Please use the "rnaiter" function instead.

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, 
0, c("SigIntensity", "NbCells"), "biological_process")

RNAither documentation built on Nov. 8, 2020, 8:06 p.m.