skIncrPPCA {BiocSklearn} | R Documentation

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

### Description

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

### Usage

    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

    # demo SE made with TENxGenomics:
    # mm = matrixSummarizedExperiment(h5path, 1:27998, 1:1500)
    # saveHDF5SummarizedExperiment(mm, "tenx_1500")
    #
    se1500 = 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)

[Package BiocSklearn version 1.2.0 Index]