DA_limma | R Documentation |
Fast run for limma voom differential abundance detection method.
DA_limma( object, assay_name = "counts", pseudo_count = FALSE, design = NULL, coef = 2, norm = c("TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "none"), weights, verbose = TRUE )
object |
a phyloseq or TreeSummarizedExperiment object. |
assay_name |
the name of the assay to extract from the
TreeSummarizedExperiment object (default |
pseudo_count |
add 1 to all counts if TRUE (default
|
design |
character name of the metadata columns, formula, or design matrix with rows corresponding to samples and columns to coefficients to be estimated. |
coef |
integer or character index vector indicating which coefficients of the linear model are to be tested equal to zero. |
norm |
name of the normalization method to use in the differential
abundance analysis. Choose between the native edgeR normalization methods,
such as |
weights |
an optional numeric matrix giving observational weights. |
verbose |
an optional logical value. If |
A list object containing the matrix of p-values 'pValMat', the matrix of summary statistics for each tag 'statInfo', and a suggested 'name' of the final object considering the parameters passed to the function.
voom
for the mean-variance relationship
estimation, lmFit
for the linear model framework.
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 TMM 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) # Differential abundance DA_limma(object = ps_NF, pseudo_count = FALSE, design = ~ group, coef = 2, norm = "TMM")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.