get_metrics {chem16S}

Calculate chemical metrics of community reference proteomes

Description

Combines taxonomic classifications with reference proteomes for taxa to get amino acid compositions of community reference proteomes. Amino acid compositions are used to calculate chemical metrics.

Usage

  get_metrics(RDP, map, refdb = "GTDB_214", taxon_AA = NULL, groups = NULL,
    return_AA = FALSE, zero_AA = NULL, metrics = c("Zc", "nO2", "nH2O"))

Arguments

RDP

data frame, taxonomic abundances produced by read_RDP or ps_taxacounts

map

data frame, taxonomic mapping produced by map_taxa

refdb

character, name of reference database (‘⁠GTDB_214⁠’ or ‘⁠RefSeq_206⁠’)

taxon_AA

data frame, amino acid compositions of taxa, used to bypass refdb specification

groups

list of indexing vectors, samples to be aggregated into groups

return_AA

logical, return the amino acid composition for each sample instead of the chemical metrics?

zero_AA

character, three-letter abbreviation(s) of amino acid(s) to assign zero counts for calculating chemical metrics

metrics

character, the chemical metrics to calculate

Details

get_metrics calculates selected chemical metrics for the community reference proteome in each sample. The community reference proteome is computed from the amino acid compositions of reference proteomes for taxa (obtained from the reference database in refdb), multiplied by taxonomic abundances given in RDP. RDP may include results from the RDP Classifier (read using read_RDP) or derived from the OTU table of a phyloseq-class object (see ps_taxacounts). map defines the taxonomic mapping between RDP and refdb. Then, chemical metrics are calculated from the amino acid composition of the community reference proteome. The default chemical metrics are carbon oxidation state (Z_C), stoichiometric oxidation state (n_O2), and stoichiometric hydration state (n_H2O). See calc_metrics for other available metrics.

groups, if given, is a list of one or more indexing vectors (with logical or numeric values) corresponding to samples whose taxonomic classifications are aggregated into groups before calculating amino acid compositions and chemical metrics.

Value

A data frame with one row for each sample, corresponding to columns 5 and above of RDP. The sample names are in the first column, which is named Run by default, or group if the groups argument is provided. The remaining columns have numeric values and are named for each of the calculated metrics.

See Also

calc_metrics, plot_metrics

Examples

## First two examples are for RDP Classifier with default training set
## and mapping to NCBI taxonomy with RefSeq reference proteomes

# Get chemical metrics for all samples in a dataset
RDPfile <- system.file("extdata/RDP/BGPF13.tab.xz", package = "chem16S")
RDP <- read_RDP(RDPfile)
map <- map_taxa(RDP, refdb = "RefSeq_206")
# This is a data frame with 14 rows and Run, Zc, nO2, and nH2O columns
(metrics <- get_metrics(RDP, map, refdb = "RefSeq_206"))

# Read the metadata file
mdatfile <- system.file("extdata/metadata/BGPF13.csv", package = "chem16S")
# Create list with metadata and metrics in same sample order
mdat <- get_metadata(mdatfile, metrics)
# Calculate metrics for aggregated samples of Archaea and Bacteria
groups <- list(A = mdat$metadata$domain == "Archaea",
  B = mdat$metadata$domain == "Bacteria")
# This is a data frame with 2 rows and group, Zc, nO2, and nH2O columns
get_metrics(RDP, map, refdb = "RefSeq_206", groups = groups)

## For the next example, classifications were made using the RDP Classifer
## with 16S rRNA sequences from GTDB r207, but
## reference proteomes are based on GTDB r214
## (this uses the default option of refdb = "GTDB_214").
RDPfile.GTDB <- system.file("extdata/RDP-GTDB_207/BGPF13.tab.xz", package = "chem16S")
RDP.GTDB <- read_RDP(RDPfile.GTDB)
map.GTDB <- map_taxa(RDP.GTDB)
metrics.GTDB <- get_metrics(RDP.GTDB, map.GTDB)

# Plot Zc from GTDB vs RefSeq
xylim <- range(metrics$Zc, metrics.GTDB$Zc)
plot(metrics$Zc, metrics.GTDB$Zc, xlim = xylim, ylim = xylim, type = "n")
lines(xylim, xylim, lty = 2, col = 8)
points(metrics$Zc, metrics.GTDB$Zc, pch = mdat$metadata$pch, bg = mdat$metadata$col)
md.leg <- mdat$metadata[1:2, ]
legend("bottomright", md.leg$domain, pch = md.leg$pch, pt.bg = md.leg$col)
title(quote(italic(Z)[C]~"from GTDB vs RefSeq"))

# To exclude tryptophan, tyrosine, and phenylalanine
# from the calculation of chemical metrics
get_metrics(RDP.GTDB, map.GTDB, zero_AA = c("Trp", "Tyr", "Phe"))