MRfulltable {metagenomeSeq} | R Documentation |
Extract a table of the top-ranked features from a linear model fit. This
function will be updated soon to provide better flexibility similar to
limma's topTable. This function differs from link{MRcoefs}
in that it
provides other information about the presence or absence of features to help
ensure significant features called are moderately present.
MRfulltable(obj, by = 2, coef = NULL, number = 10, taxa = obj$taxa, uniqueNames = FALSE, adjustMethod = "fdr", group = 0, eff = 0, numberEff = FALSE, ncounts = 0, file = NULL)
obj |
A list containing the linear model fit produced by lmFit through fitZig. |
by |
Column number or column name specifying which coefficient or contrast of the linear model is of interest. |
coef |
Column number(s) or column name(s) specifying which coefficient or contrast of the linear model to display. |
number |
The number of bacterial features to pick out. |
taxa |
Taxa list. |
uniqueNames |
Number the various taxa. |
adjustMethod |
Method to adjust p-values by. Default is "FDR". Options
include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr",
"none". See |
group |
One of five choices: 0,1,2,3,4. 0: the sort is ordered by a decreasing absolute value coefficient fit. 1: the sort is ordered by the raw coefficient fit in decreasing order. 2: the sort is ordered by the raw coefficient fit in increasing order. 3: the sort is ordered by the p-value of the coefficient fit in increasing order. 4: no sorting. |
eff |
Filter features to have at least a "eff" quantile or number of effective samples. |
numberEff |
Boolean, whether eff should represent quantile (default/FALSE) or number. |
ncounts |
Filter features to those with at least 'counts' counts. |
file |
Name of output file, including location, to save the table. |
Table of the top-ranked features determined by the linear fit's coefficient.
fitZig
fitFeatureModel
MRcoefs
MRtable
fitPA
data(lungData) k = grep("Extraction.Control",pData(lungData)$SampleType) lungTrim = lungData[,-k] lungTrim=filterData(lungTrim,present=30) lungTrim=cumNorm(lungTrim,p=0.5) smokingStatus = pData(lungTrim)$SmokingStatus mod = model.matrix(~smokingStatus) fit = fitZig(obj = lungTrim,mod=mod) # head(MRfulltable(fit)) #### fit = fitFeatureModel(obj = lungTrim,mod=mod) # head(MRfulltable(fit))