Usage
anotaPerformQc(dataT=NULL, dataP=NULL, phenoVec=NULL,
generatePlot=FALSE, file="ANOTA_Total_vs_Polysomal_regressions.pdf",
nReg=200, correctionMethod="BH", useDfb=TRUE, useDfbSim=TRUE,
nDfbSimData=2000, useRVM=TRUE, onlyGroup=FALSE, useProgBar=TRUE)
Arguments
dataT
A matrix with cytosolic mRNA data. Non numerical rownames
are needed.
dataP
A matrix with translational activity data. Non numerical rownames
are needed.
phenoVec
A vector describing the sample classes (each class
should have a unique identifier). Note that dataT, dataP and phenoVec
must have the same sample order so that column 1 in dataP is
the translational activity data for a sample, column 1 in dataT is the
cytosolic mRNA data and position 1 in phenoVec describes the sample
class.
generatePlot
anota can plot the regression for each gene. However,
as there are many genes, this output is normally not
informative. Default is FALSE, no individual plotting.
file
If generatePlot is set to TRUE use file to set
desired file name (prints to current directory as a pdf).
Default is "ANOTA_Total_vs_Polysomal_regressions.pdf"
nReg
If generatePlot is set to TRUE, nReg can be used to
limit the number of output plots. Default is 200.
correctionMethod
anota adjusts the omnibus interaction and
sample class p-values for multiple testing. Correction method can be "Bonferroni", "Holm",
"Hochberg", "SidakSS", "SidakSD", "BH", "BY", "ABH" or
"TSBH" as implemented in the multtest package or "qvalue" as
implemented in the qvalue package. Default is "BH".
useDfb
Should anota assess the occurrence of highly influential
data points (defult is TRUE)?
useDfbSim
The random occurrence of dfbetas can be
simulated. Default is TRUE. FALSE represses simulation which reduces
computation time but makes interpretation of the dfbetas difficult.
nDfbSimData
If useDfbSim is TRUE the user can select the
number of samplings that will be performed per step (10 steps with
different correlations between the translationally activty and the
cytosolic mRNA level). Default is 2000.
useRVM
The Random Variance Model (RVM) can be
used for the omnibus sample class comparison. In this case the effect of RVM
on the distribution of the interaction significances needs to be
tested as well. Default (TRUE) leads to calculation of RVM p-values
for both omnibus interactions and omnibus sample class effects.
onlyGroup
It is possible to suppress the omnibus interaction
analysis and only perform the omnibus sample class effect analysis. Default
is FALSE (analyse both interactions and sample class effects.)
useProgBar
Should the progress bar be shown. Default is TRUE,
show progress bar.