Description Usage Arguments Details Value Author(s) Examples
A wrapper function for the function 'lmFit' of the R Bioconductor package 'limma'.
1 2 3 4 5 6 7 8 9 10  lmFitWrapper(
es,
formula = ~as.factor(gender),
pos.var.interest = 1,
pvalAdjMethod = "fdr",
alpha = 0.05,
probeID.var = "ProbeID",
gene.var = "Symbol",
chr.var = "Chromosome",
verbose = TRUE)

es 
An LumiBatch object.

formula 
An object of class 
pos.var.interest 
integer. Indicates which covariate on the righthandside of 
pvalAdjMethod 
One of pvalue adjustment methods provided by
the R function 
alpha 
Significance level. A test is claimed to be significant
if the adjusted pvalue < 
probeID.var 
character string. Name of the variable indicating probe ID in feature data set. 
gene.var 
character string. Name of the variable indicating gene symbol in feature data set. 
chr.var 
character string. Name of the variable indicating chromosome number in feature data set. 
verbose 
logical. Determine if intermediate output need to be suppressed. By default

This is a wrapper function of R Bioconductor functions
lmFit
and eBayes
to make it easier to input design and
output list of significant results.
A list with the following elements:
n.sig 
Number of significant tests after pvalue adjustment. 
frame 
A data frame containing test results sorted according
to the ascending order of unadjusted pvalues for the covariate of the
interest. The data frame contains
7 columns: 
statMat 
A matrix containing test statistics for all covariates and for all probes. Rows are probes and columns are covariates. The rows are ordered according to the ascending order of unadjusted pvalues for the covariate of the interest. 
pvalMat 
A matrix containing pvalues for all covariates and for all probes. Rows are probes and columns are covariates. The rows are ordered according to the ascending order of unadjusted pvalues for the covariate of the interest. 
pval.quantile 
Quantiles (minimum, 25
for all covariates including intercept provided in the
input argument 
frame.unsorted 
A data frame containing test results.
The data frame contains
7 columns: 
statMat.unsorted 
A matrix containing test statistics for all covariates and for all probes. Rows are probes and columns are covariates. 
pvalMat.unsorted 
A matrix containing pvalues for all covariates and for all probes. Rows are probes and columns are covariates. 
memGenes 
A numeric vector indicating the cluster membership
of probes (unsorted).

memGenes2 
A numeric vector indicating the cluster membership
of probes (unsorted).

mu1 
Mean expression levels for arrays for probe cluster 1
(average taking across all probes with 
mu2 
Mean expression levels for arrays for probe cluster 2
(average taking across all probes with 
mu3 
Mean expression levels for arrays for probe cluster 3
(average taking across all probes with 
ebFit 
object returned by R Bioconductor function 
Weiliang Qiu <stwxq@channing.harvard.edu>, Brandon Guo <brandowonder@gmail.com>, Christopher Anderson <christopheranderson84@gmail.com>, Barbara Klanderman <BKLANDERMAN@partners.org>, Vincent Carey <stvjc@channing.harvard.edu>, Benjamin Raby <rebar@channing.harvard.edu>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  # generate simulated data set from conditional normal distribution
set.seed(1234567)
es.sim = genSimData.BayesNormal(nCpGs = 100,
nCases = 20, nControls = 20,
mu.n = 2, mu.c = 2,
d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
outlierFlag = FALSE,
eps = 1.0e3, applier = lapply)
print(es.sim)
res.limma = lmFitWrapper(
es = es.sim,
formula = ~as.factor(memSubj),
pos.var.interest = 1,
pvalAdjMethod = "fdr",
alpha = 0.05,
probeID.var = "probe",
gene.var = "gene",
chr.var = "chr",
verbose = TRUE)

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