norm_DESeq2 {benchdamic}

R Documentation

norm_DESeq2

Description

Calculate normalization factors from a phyloseq object to scale the raw library sizes. Inherited from DESeq2 estimateSizeFactors function.

Usage

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

Arguments

object	phyloseq object containing the counts to be normalized.
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 normalization factors is added to the phyloseq sample_data slot.

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

# VERY IMPORTANT: to convert normalization factors to scaling factors divide
# them by the library sizes and renormalize.
scaleFacts = normFacts / phyloseq::sample_sums(ps_stool_16S)
# Renormalize: multiply to 1
scaleFacts = scaleFacts/exp(mean(log(scaleFacts)))

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