metagenome_contributions: Partition metagenome functional contributions according to...

In vmikk/metagMisc: Miscellaneous functions for metagenomic analysis

Partition metagenome functional contributions according to function, OTU, and sample.

Description

This function partitions the predicted contribution to the metagenomes from each organism in the given OTU table for each function (e.g., KO) in each sample.

Usage

metagenome_contributions(
  otu_tab,
  func_tab,
  tax_tab = NULL,
  features = NULL,
  NSTI_present = TRUE,
  rel_abund = TRUE,
  remove_zero_contributions = TRUE
)

Arguments

otu_tab	Data frame with OTU abundances (rows = OTUs, columns = Samples, first column = OTU names)
func_tab	Data frame with precalculated function predictions on per OTU basis (rows = OTUs, columns = feature counts, first column = OTU names)
tax_tab	(Optional) Data frame with OTU taxonomy (rows = OTUs, columns = taxonomy ranks, first column = OTU names). If provided, taxonomy ranks will be added to the resulting table for each OTU
features	Character vector with function names; if provided, results will be limited to only the specified functions
NSTI_present	Logical; idnicating weather NSTI values are present in the last column of func_tab
rel_abund	Logical; if TRUE, OTU counts will be transformed to relative abundances
remove_zero_contributions	Logical; if TRUE, OTUs with zero contribution will be removed from results

Details

This function is analogous to the 'metagenome_contributions.py' from PICRUSt. Each line in the results relates how much a single OTU (third column) contributes to a single KO (first column) within a single sample (second column). The fifth column contains the actual relative abundance contributed by this OTU, and the other columns contain other information about the abundance of the OTU and the percentage contribution of this OTU. The last columns provide the taxonomy information for the OTU.

Value

Data frame

References

https://picrust.github.io/picrust/scripts/metagenome_contributions.html

Examples

## Create dummy data
set.seed(111)
NSAMP=10    # number of samples
NSPEC=30    # number of species/OTUs
NGENES=20   # number of features

dummy_name <- function(len = 5){ paste(sample(letters, size = len, replace = T), collapse = "") }

# Table with gene counts per OTU
func_tab <- data.frame(
              OTU_ID = replicate(n = 100, expr = dummy_name()),
              matrix(data = sample(0:100, size = 100*NGENES, replace = T), nrow = 100),
              stringsAsFactors = F)
colnames(func_tab)[-1] <- paste("F", 1:(ncol(func_tab)-1), sep="")

# Table with OTU abundances
otu_tab <- data.frame(
              OTU = sample(func_tab$OTU_ID, size = NSPEC),
              matrix(data = sample(1:10000, size = NSPEC*NSAMP, replace = T), nrow = NSPEC),
              stringsAsFactors = F)
colnames(otu_tab)[-1] <- paste("Samp", 1:(ncol(otu_tab)-1), sep="")

# Table with OTU taxonomy annotation
tax_tab <- data.frame(OTU_ID = func_tab$OTU_ID,
              Kingdom = sort(sample(LETTERS[1:4], size = nrow(func_tab), replace = T)),
              Phylum = sort(sample(LETTERS[5:20], size = nrow(func_tab), replace = T)),
              stringsAsFactors = F)

metag <- metagenome_contributions(otu_tab, func_tab, tax_tab,
                     features = c("F1", "F2", "F3"), NSTI_present = F, rel_abund = FALSE,
                     remove_zero_contributions = TRUE)
head(metag)

vmikk/metagMisc documentation built on June 20, 2024, 7:20 a.m.