`subsample`

function is recommended).It is also useful for reproducing the results of an earlier run (see Details).

`generateSubsampledMatrix(counts, proportion, seed, replication = 1)`

counts

Original matrix of read counts

proportion

The specific proportion to subsample

seed

A subsampling seed, which can be extracted from a subsamples
or summary.subsamples object. If not given, doesn't set the seed.

replication

Replicate number: allows performing multiple deterministic
replications at a given subsampling proportion

- subsamples matrix at specified subsampling proportion

`ss`

by retrieving the seed with
`getSeed(ss)`

. When given along with the original counts, the
proportion, and the replication number (if more than one subsampling was done
at each proportion) this produces the same matrix as was used in the analysis.The seed is calculated deterministically using an md5 hash of three combined values: the global seed used for the subsampling object, the subsampling proportion, and the replication # for that proportion.

data(hammer) hammer.counts = Biobase::exprs(hammer)[, 1:4] hammer.design = Biobase::pData(hammer)[1:4, ] hammer.counts = hammer.counts[rowSums(hammer.counts) >= 5, ] ss = subsample(hammer.counts, c(.01, .1, 1), treatment=hammer.design$protocol, method=c("edgeR", "DESeq2", "voomLimma")) seed = getSeed(ss) # generate the matrices used at each subsample subm.01 = generateSubsampledMatrix(hammer.counts, .01, seed) subm.1 = generateSubsampledMatrix(hammer.counts, .1, seed)