HTSFilter-deprecated: Deprecated functions in package 'HTSFilter'

Description Usage Arguments Details Value Author(s) References

Description

These functions are provided for compatibility with older versions of ‘HTSFilter’ only, and will be defunct at the next release.

Usage

1
2
3
4
5
6
7
8
9
## S4 method for signature 'CountDataSet'
HTSFilter(x, conds = NA, s.min = 1, s.max = 200,
  s.len = 100, loess.span = 0.3, normalization = c("DESeq", "TMM",
  "none"), plot = TRUE, plot.name = NA, parallel = FALSE,
  BPPARAM = bpparam())

## S4 method for signature 'CountDataSet'
HTSBasicFilter(x, method, cutoff.type = "value",
  cutoff = 10, length = NA, normalization = c("DESeq", "TMM", "none"))

Arguments

x

A numeric matrix or data.frame representing the counts of dimension (g x n), for g genes in n samples, a DGEList object, a DGEExact object, a DGEGLM object, a DGELRT object, or a DESeqDataSet object.

conds

Vector of length n identifying the experimental condition of each of the n samples; required when sQuote(x) is a numeric matrix.

s.min

Minimum value of filtering threshold to be considered, with default value equal to 1

s.max

Maximum value of filtering threshold to be considered, with default value equal to 200

s.len

Length of sequence of filtering thresholds to be considered (from s.min to s.max) for the calculation of the global similarity index

loess.span

Span of the loess curve to be fitted to the filtering thresholds and corresponding global similarity indices, with default value equal to 0.3

normalization

Normalization method to be used to correct for differences in library sizes, with choices “TMM” (Trimmed Mean of M-values), “DESeq” (normalization method proposed in the DESeq package), “pseudo.counts” (pseudo-counts obtained via quantile-quantile normalization in the edgeR package, only available for objects of class DGEList and DGEExact), and “none” (to be used only if user is certain no normalization is required, or if data have already been pre-normalized by an alternative method)

plot

If “TRUE”, produce a plot of the calculated global similarity indices against the filtering threshold with superimposed loess curve

plot.name

If plot = “TRUE”, the name of the PDF file to be saved to the current working directory. If plot.name = NA, the plot is drawn in the current window

parallel

If FALSE, no parallelization. If TRUE, parallel execution using BiocParallel (see next argument BPPARAM). A note on running in parallel using BiocParallel: it may be advantageous to remove large, unneeded objects from the current R environment before calling the function, as it is possible that R's internal garbage collection will copy these files while running on worker nodes.

BPPARAM

Optional parameter object passed internally to bplapply when parallel=TRUE. If not specified, the parameters last registered with register will be used.

method

Basic filtering method to be used: “mean”, “sum”, “rpkm”, “variance”, “cpm”, “max”, “cpm.mean”, “cpm.sum”, “cpm.variance”, “cpm.max”, “rpkm.mean”, “rpkm.sum”, “rpkm.variance”, or “rpkm.max”

cutoff.type

Type of cutoff to be used: a numeric value indicating the number of samples to be used for filtering (when method = “cpm” or “rpkm”), or one of “value”, “number”, or “quantile”

cutoff

Cutoff to be used for chosen filter

length

Optional vector of length n containing the lengths of each gene in x; optional except in the case of method = “rpkm”

Details

The following functions are deprecated and will be made defunct. Objects of type ‘CountDataSet’ from the original ‘DESeq’ package will no longer be supported; users should make use of ‘DESeqDataSet’ objects from the ‘DESeq2’ package:

Value

Author(s)

Andrea Rau, Melina Gallopin, Gilles Celeux, and Florence Jaffrezic

References

R. Bourgon, R. Gentleman, and W. Huber. (2010) Independent filtering increases detection power for high- throughput experiments. PNAS 107(21):9546-9551.

P. Jaccard (1901). Etude comparative de la distribution orale dans une portion des Alpes et des Jura. Bulletin de la Societe Vaudoise des Sciences Naturelles, 37:547-549.

A. Rau, M. Gallopin, G. Celeux, F. Jaffrezic (2013). Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics, doi: 10.1093/bioinformatics/btt350.


HTSFilter documentation built on Nov. 1, 2018, 3:58 a.m.