process_dds: Processing and data transformation of DESeqDataSet

Description Usage Arguments Details Value Author(s)

View source: R/GEX_process_dds.R

Description

Process count matrix of DESeqDataSet and optionally convert it to a SummarizedExperiment object.

Usage

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process_dds(dds, min_rowsum = 10, norm_sizeFactors = FALSE,
  transform.function = NULL, return_SummarizedExperiment = FALSE, ...)

Arguments

dds

DESeqDataSet

min_rowsum

numeric. Minimum rowsum of countmatrix. All rows with rowSums < min_rowsum are removed from the count matrix.

norm_sizeFactors

logical. If TRUE normalize for library size. This may already be included in the data transformation (e.g. for rlog and varianceStabilizingTransformation).

transform.function

function name to be used for data transformation, e.g. DESeq2::rlog or DESeq2::rlogTransformation for log2-transformation, DESeq2::varianceStabilizingTransformation, fpkm, fpm, voom.

return_SummarizedExperiment

logical. If True function returns a SummarizedExperiment object. If FALSE the processed input object is returned (may be matrix, DESeqDataSet or DESeqTransform).

...

further parameter for transform.function

Details

The count matrix of the input object is filtered for low abundancy transcripts, normalized for library size factor (size factors are first calculated if necessary) and is transformed according to the function given in transform.function. Parameters for the selected transform.function can be passed via the ... -argument (see help for the respective function). The DESeq2 package recommends varianceStabilizingTransformation for further processing of the read count matrix. If you prefer to analyse reads with limma, you may apply voom transformation as recommended in the limma documentation. The voom transformation can be complemented by the same between-array normalization methods as would be used for microarrays (e.g. quantile normalization) via the normalize.method parameter of voom. The function will return either the processed input object or an SummarizedExperiment object if return_SummarizedExperiment is set to TRUE. In this case, the design parameter of the initial DESeqDataSet is omitted.

Value

SummarizedExperiment or processed input object (matrix, DESeqDataSet or DESeqTransform depending on applied data transformation)

Author(s)

Frank Ruehle


frankRuehle/systemsbio documentation built on Dec. 8, 2018, 10:27 p.m.