sdcd: Sexual dimorphic and COPD differential analysis
In COPDSexualDimorphism: Sexual dimorphic and COPD differential analysis for gene expression and methylation.

Description Usage Arguments Details Value Author(s) References See Also Examples

Given linear models (limma) from a stratified analysis, sdcd compares the coefficients of the main effects across sexes. It then reports on the markers with significant differences in the coefficients. Typically used in conjunction with limma.

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sdcd(male.fit, female.fit, coeff, genes, fdr.cutoff = 0.25, stat = c("z", "t"), file.prefix = "male.female.copd", class.names = c("male", "female"), write.file = TRUE)
sdcd.vmr(male.fit, female.fit, coeff, genes, fdr.cutoff = 0.25, stat = c("z", "t"), annotate = FALSE, annotate.with = c("genes","NCBI"), file.prefix = "male.female.copd", class.names = c("male", "female"), write.file = TRUE)
sdcd.core(male.fit, female.fit, coeff, stat = c("z", "t"))

`male.fit, female.fit`	Objects of type limma, as generated by the function eBayes. OR a list with fields: coefficients (matrix), stdev.unscaled (matrix), sigma (numeric vector), df.residual (numeric), and df.prior (numeric).
`coeff`	Coefficients of the main effect of interest. `sex` for COPD-strratified analysis and `COPD` for sex-stratified analysis. This should correspond to a column name of the matrices `coefficients` and `stdev.unscaled` in `male.fit` and `female.fit`.
`genes`	Annotation of the gene expression probes.
`fdr.cutoff`	Numeric cutoff for FDR q-values
`stat`	Choices between "z" and "t". For "z", a z-test is used to assess significance of the difference between the regression coefficients. For "t", the t-statistics, as opposed to the coefficients themselves, are contrasted.
`annotate`	For SDCD methylation analyis, a boolean to determin if the methylated regions (VMRs) should be annotated by genes within 10kb or not.
`annotate.with`	For SDCD methylation analyis, when `annotate == TRUE` this option indicates how to annotate the methylated regions. The `"genes"` option uses the input object while the `"NCBI"` option uses function `GetNeighGenes` in the package `NCBI2R`.
`file.prefix`	Prefix for output file name.
`class.names`	An array of character strings of length two representing the two strata.
`write.file`	A boolean that determined weather the results should be written out as files.

This is to be used in conjuction with the package limma. Linear model fits are passed to the sdcd function. The main function is for gene expression data while the function sdcd.vmr is for methylation data. The main functionality is the same between the two, but the data annotation and output are slightly different. See intended usage in the vignette.

A data.frame with gene information and the following columns:

`*.beta`	Coefficients of the main effects in the two strata
`*.sd`	Standard deviation of the coefficients of the main effects in the two strata
`*.t`	T-statistics associated with the coefficients of the main effects in the two strata
`*.p.value`	P-value associated with the coefficients of the main effects in the two strata
`beta.diff`	The difference between the coefficients in the two strata
`beta.diff.pooled.sd`	Standard deviation of `beta.diff`
`stratum1.stratum2.p`	P-value associated with `beta.diff`
`stratum1.stratum2.p.adj`	Benjamini-Hochberg FDR corrected p-value

J Fah Sathirapongsasuti

Sathirapongsasuti JF, Glass K, Huttenhower C, Quackenbush J, DeMeo DL. Integrative Genomics of Sexual Dimorphism in COPD. (In Prep).

lgrc.expr

data(lgrc.expr.meta)
data(lgrc.expr)
data(lgrc.genes)

## Sex-stratified
design.mtx = cbind(ctrl=1,
		copd=as.integer(grepl("COPD",colnames(expr))),
		age=expr.meta$age,
		pkyr=expr.meta$pkyrs)

good.idx = apply(design.mtx,1,function(x){!any(is.na(x))}) & (expr.meta$gender == "1-Male")
male.fit = lmFit(log(expr)[,good.idx], design.mtx[good.idx,])
male.fit = eBayes(male.fit)

good.idx = apply(design.mtx,1,function(x){!any(is.na(x))}) & (expr.meta$gender == "2-Female")
female.fit = lmFit(log(expr)[,good.idx], design.mtx[good.idx,])
female.fit = eBayes(female.fit)

male.female.copd.beta.diff.genes = sdcd(male.fit, female.fit, "copd", lgrc.genes, fdr.cutoff=0.25, file.prefix="male.female.copd")

## COPD-stratified
design.mtx = cbind(ctrl=1,
		gender=expr.meta$gender,
		age=expr.meta$age,
		pkyr=expr.meta$pkyrs)

good.idx = apply(design.mtx,1,function(x){!any(is.na(x))}) & grepl("COPD",colnames(expr))
copd.fit = lmFit(log(expr)[,good.idx], design.mtx[good.idx,])
copd.fit = eBayes(copd.fit)

good.idx = apply(design.mtx,1,function(x){!any(is.na(x))}) & grepl("CTRL",colnames(expr))
ctrl.fit = lmFit(log(expr)[,good.idx], design.mtx[good.idx,])
ctrl.fit = eBayes(ctrl.fit)

copd.ctrl.gender.beta.diff.genes = sdcd(copd.fit, ctrl.fit, "gender", lgrc.genes, fdr.cutoff=0.25, file.prefix="copd.ctrl.gender", class.names=c("copd","ctrl"))

## Combine
sdcd.genes = merge(copd.ctrl.gender.beta.diff.genes, male.female.copd.beta.diff.genes, by=setdiff(intersect(names(copd.ctrl.gender.beta.diff.genes), names(male.female.copd.beta.diff.genes)),c("beta.diff","beta.diff.pooled.sd")))
sdcd.genes = unique(sdcd.genes)
print(paste("There are ", nrow(sdcd.genes), " SDCD genes"))