lmFitWrapper {iCheck} | R Documentation |
A wrapper function for the function 'lmFit' of the R Bioconductor package 'limma'.
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 right-hand-side of |
pvalAdjMethod |
One of p-value adjustment methods provided by
the R function |
alpha |
Significance level. A test is claimed to be significant
if the adjusted p-value < |
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 p-value adjustment. |
frame |
A data frame containing test results sorted according
to the ascending order of unadjusted p-values 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 p-values 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 p-values 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>
# 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 = 1.0e-3, 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)