DA_DESeq2 {benchdamic} | R Documentation |
Fast run for DESeq2 differential abundance detection method.
DA_DESeq2( object, pseudo_count = FALSE, design = NULL, contrast = NULL, alpha = 0.05, norm = c("TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "none", "ratio", "poscounts", "iterate", "TSS", "CSSmedian", "CSSdefault"), weights, verbose = TRUE )
object |
phyloseq object. |
pseudo_count |
add 1 to all counts if TRUE (default
|
design |
(Required). A
|
contrast |
character vector with exactly three elements: the name of a factor in the design formula, the name of the numerator level for the fold change, and the name of the denominator level for the fold change. |
alpha |
the significance cutoff used for optimizing the independent filtering (by default 0.05). If the adjusted p-value cutoff (FDR) will be a value other than 0.05, alpha should be set to that value. |
norm |
name of the normalization method used to compute the
normalization factors to use in the differential abundance analysis. If
|
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 dispersion estimates 'dispEsts', the matrix of summary statistics for each tag 'statInfo', and a suggested 'name' of the final object considering the parameters passed to the function.
phyloseq_to_deseq2
for phyloseq to DESeq2
object conversion, DESeq
and
results
for the differential abundance method.
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 poscounts normalization factors ps_NF <- norm_DESeq2(object = ps, method = "poscounts") # The phyloseq object now contains the normalization factors: scaleFacts <- phyloseq::sample_data(ps_NF)[, "NF.poscounts"] head(scaleFacts) # Differential abundance DA_DESeq2(object = ps_NF, pseudo_count = FALSE, design = ~ group, contrast = c("group", "B", "A"), norm = "poscounts")