prepare_detergent: This is the frist stage and involves preparing the Panel of...
In kcygan/dryclean: rPCA based method to de-noise genomic coverage data

Description Usage Arguments Value Author(s)

View source: R/dryclean.R

This function takes in gRanges outputs from fragCounter and extracts GC corrected read count data and carries rPCA decomposition on the matrix thus created. The normal samples used to form the PON can be selected randomly or by clustering the genomic bacground or all samples can be used.

prepare_detergent(normal.table.path = NA, use.all = TRUE,
  choose.randomly = FALSE, choose.by.clustering = FALSE,
  number.of.samples = 50, save.pon = FALSE, path.to.save = NA,
  verbose = TRUE, num.cores = 1, tolerance = 1e-04)

`normal.table.path`	character path to data.table containing two columns "sample" and "normal_cov". See manual for details
`use.all`	boolean (default == TRUE). If all normal samples are to be used for creating PON
`choose.randomly`	boolean (default == FALSE). If a random subset of normal samples are to be used for creating PON.
`choose.by.clustering`	boolean (default == FALSE). Clusters normal samples based on the genomic background and takes a random sample from within the clusters.
`number.of.samples`	interger (default == 50). If choose.by.clustering == TRUE, this is the number of clusters at which to cut tree.
`save.pon`	boolean (default == FALSE). If PON needs to be saved.
`path.to.save`	charater (default == NA). Path to save the PON created if save.pon == TRUE.
`verbose`	boolean (default == TRUE). Outputs progress.
`num.cores`	interger (default == 1). Number of cores to use for parallelization
`tolerance`	numeric (default == 0.0001). Tolerance for error for batch rPCA. We suggest keeping this value.

prepare_detergent returns a list containing the following components:

`L`	array_like; low-rank component; (m, n) dimensional array.
`S`	array_like; sparse component; (m, n) dimensional array.
`k`	numeric; estimated rank of the matrix; used in the online implementation.
`U.hat`	array_like; left singular vectors of L; (m, k) dimensional array.
`V.hat`	array_like; right singular vectors of L; (n, k) dimensional array.
`sigma.hat`	array_like; singular values; vector of length (k).