skIncrPPCA: optionally fault tolerant incremental partial PCA for...

Description Usage Arguments Value Note Examples

Description

optionally fault tolerant incremental partial PCA for projection of samples from SummarizedExperiment

Usage

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skIncrPPCA(se, chunksize, n_components, assayind = 1,
  picklePath = "./skIdump.pkl", matTx = force, ...)

Arguments

se

instance of SummarizedExperiment

chunksize

integer number of samples per step

n_components

integer number of PCs to compute

assayind

not used, assumed set to 1

picklePath

if non-null, incremental results saved here via sklearn.externals.joblib.dump, for each chunk. If NULL, no saving of incremental results.

matTx

a function defaulting to force() that accepts a matrix and returns a matrix with identical dimensions, e.g., function(x) log(x+1)

...

not used

Value

python instance of sklearn.decomposition.incremental_pca.IncrementalPCA

Note

Will treat samples as records and all features (rows) as attributes, projecting. to an n_components-dimensional space. Method will acquire chunk of assay data and transpose before computing PCA contributions. In case of crash, restore from picklePath using SklearnEls()$joblib$load after loading reticulate. You can use the n_samples_seen_ component of the restored python reference to determine where to restart. You can manage resumption using skPartialPCA_step.

Examples

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# demo SE made with TENxGenomics:
# mm = matrixSummarizedExperiment(h5path, 1:27998, 1:1500)
# saveHDF5SummarizedExperiment(mm, "tenx_1500")
#
if (requireNamespace("HDF5Array")) {
  se1500 = HDF5Array::loadHDF5SummarizedExperiment(
     system.file("hdf5/tenx_1500", package="BiocSklearn"))
  lit = skIncrPPCA(se1500[, 1:50], chunksize=5, n_components=4)
  round(cor(pypc <- lit$transform(dat <- t(as.matrix(assay(se1500[,1:50]))))),3)
  rpc = prcomp(dat)
  round(cor(rpc$x[,1:4], pypc), 3)
}

BiocSklearn documentation built on Nov. 1, 2018, 3:50 a.m.