norm_DESeq2: norm_DESeq2

In mcalgaro93/benchdamic: Benchmark of differential abundance methods on microbiome data

norm_DESeq2

Description

Calculate size factors from a phyloseq or TreeSummarizedExperiment object. Inherited from DESeq2 estimateSizeFactors function.

Usage

norm_DESeq2(
  object,
  assay_name = "counts",
  method = c("ratio", "poscounts", "iterate"),
  verbose = TRUE,
  ...
)

Arguments

object	a phyloseq or TreeSummarizedExperiment object.
assay_name	the name of the assay to extract from the TreeSummarizedExperiment object (default assayName = "counts"). Not used if the input object is a phyloseq.
method	Method for estimation: either "ratio", "poscounts", or "iterate". "ratio" uses the standard median ratio method introduced in DESeq. The size factor is the median ratio of the sample over a "pseudosample": for each gene, the geometric mean of all samples. "poscounts" and "iterate" offer alternative estimators, which can be used even when all features contain a sample with a zero (a problem for the default method, as the geometric mean becomes zero, and the ratio undefined). The "poscounts" estimator deals with a feature with some zeros, by calculating a modified geometric mean by taking the n-th root of the product of the non-zero counts. This evolved out of use cases with Paul McMurdie's phyloseq package for metagenomic samples. The "iterate" estimator iterates between estimating the dispersion with a design of ~1, and finding a size factor vector by numerically optimizing the likelihood of the ~1 model.
verbose	an optional logical value. If TRUE, information about the steps of the algorithm is printed. Default verbose = TRUE.
...	other parameters for DESeq2 estimateSizeFactors function.

Value

A new column containing the chosen DESeq2-based size factors is added to the sample_data slot of the phyloseq object or the colData slot of the TreeSummarizedExperiment object.

See Also

estimateSizeFactors for details. setNormalizations and runNormalizations to fastly set and run normalizations.

Examples

set.seed(1)
# Create a very simple phyloseq object
counts <- matrix(rnbinom(n = 60, size = 3, prob = 0.5), nrow = 10, ncol = 6)
metadata <- data.frame("Sample" = c("S1", "S2", "S3", "S4", "S5", "S6"),
                       "group" = as.factor(c("A", "A", "A", "B", "B", "B")))
ps <- phyloseq::phyloseq(phyloseq::otu_table(counts, taxa_are_rows = TRUE),
                         phyloseq::sample_data(metadata))

# Calculate the size factors
ps_NF <- norm_DESeq2(object = ps, method = "poscounts")
# The phyloseq object now contains the size factors:
sizeFacts <- phyloseq::sample_data(ps_NF)[, "NF.poscounts"]
head(sizeFacts)

# VERY IMPORTANT: DESeq2 uses size factors to normalize counts. 
# These factors are used internally by a regression model. To make DEseq2 
# size factors available for edgeR, we need to transform them into 
# normalization factors. This is possible by dividing the factors by the 
# library sizes and renormalizing. 
normSizeFacts = sizeFacts / colSums(phyloseq::otu_table(ps_stool_16S))
# Renormalize: multiply to 1
normFacts = normSizeFacts/exp(colMeans(log(normSizeFacts)))

mcalgaro93/benchdamic documentation built on March 10, 2024, 10:40 p.m.