use procedure tailored to multiple responses for a fixed design matrix

Usage

zs4manyYs(se, omit.hi.maf = FALSE, BPPARAM = BiocParallel::bpparam())

Arguments

se: RangedSummarizedExperiment
omit.hi.maf: logical(1) if TRUE, variants for which MAF exceeds 0.5 are omitted
BPPARAM: instance of BiocParallelParam to control parallel execution over genotype calls

Value

matrix with rows corresponding to molecular features and columns corresponding to genotypes

Examples

data(geuv19xse)
sds = MatrixGenerics::rowSds(assay(geuv19xse))
print(quantile(sds))
#>           0%          25%          50%          75%         100% 
#>     0.000000     2.220802    24.784532   152.732556 51865.584335 
BiocParallel::register(BiocParallel::SerialParam())
mafs = maf(geuv19xse)
mins = apply(data.matrix(mcols(getCalls(geuv19xse))), 1, min, na.rm=TRUE) # some -1 values
print(quantile(mins))
#>   0%  25%  50%  75% 100% 
#>   -1    0    0    0    2 
lk = filterCalls(geuv19xse, which(mafs>.25 & mins > -1))
lk = lk[which(sds>median(sds, na.rm=TRUE)),]
chk1 = zs4manyYs(lk)
#> some variants have MAF > 0.5 
data(geuv19_samples)
namedSex = geuv19_samples$Sex
names(namedSex) = geuv19_samples[["Sample name"]]
lk$Sex = namedSex[colnames(lk)]
table(lk$Sex)
#> 
#> female   male 
#>     46     45 
chk2 <- zs4manyYs(lk) # use covariate
#> some variants have MAF > 0.5 
plot(as.numeric(chk2), as.numeric(chk1))