norm_edgeR: norm_edgeR

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

norm_edgeR

Description

Calculate normalization factors from a phyloseq or TreeSummarizedExperiment object. Inherited from edgeR calcNormFactors function.

Usage

norm_edgeR(
  object,
  assay_name = "counts",
  method = c("TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "none"),
  refColumn = NULL,
  logratioTrim = 0.3,
  sumTrim = 0.05,
  doWeighting = TRUE,
  Acutoff = -1e+10,
  p = 0.75,
  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	normalization method to be used. Choose between TMM, TMMwsp, RLE, upperquartile, posupperquartile or none.
refColumn	column to use as reference for method="TMM". Can be a column number or a numeric vector of length nrow(object).
logratioTrim	the fraction (0 to 0.5) of observations to be trimmed from each tail of the distribution of log-ratios (M-values) before computing the mean. Used by method="TMM" for each pair of samples.
sumTrim	the fraction (0 to 0.5) of observations to be trimmed from each tail of the distribution of A-values before computing the mean. Used by method="TMM" for each pair of samples.
doWeighting	logical, whether to use (asymptotic binomial precision) weights when computing the mean M-values. Used by method="TMM" for each pair of samples.
Acutoff	minimum cutoff applied to A-values. Count pairs with lower A-values are ignored. Used by method="TMM" for each pair of samples.
p	numeric value between 0 and 1 specifying which quantile of the counts should be used by method="upperquartile".
verbose	an optional logical value. If TRUE, information about the steps of the algorithm is printed. Default verbose = TRUE.
...	other arguments are not currently used.

Value

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

See Also

calcNormFactors 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_edgeR(object = ps, method = "TMM")

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

# VERY IMPORTANT: edgeR uses normalization factors to normalize library sizes
# not counts. They are used internally by a regression model. To make edgeR 
# normalization factors available for other methods, such as DESeq2 or other 
# DA methods based on scaling or size factors, we need to transform them into
# size factors. This is possible by multiplying the factors for the library 
# sizes and renormalizing. 
normLibSize = normFacts * colSums(phyloseq::otu_table(ps_stool_16S))
# Renormalize: multiply to 1
sizeFacts = normLibSize/exp(colMeans(log(normLibSize)))

mcalgaro93/benchdamic documentation built on Nov. 28, 2022, 6:55 p.m.