plot_metrics {chem16S}

Plot chemical metrics of community reference proteomes

Description

Functions to plot chemical metrics of community reference proteomes.

Usage

  plot_metrics(mdat,
    xvar = "Zc", yvar = "nH2O",
    xlim = NULL, ylim = NULL,
    xlab = chemlab(xvar), ylab = chemlab(yvar),
    plot.it = TRUE, add = FALSE, identify = FALSE,
    points = TRUE, lines = FALSE, title = TRUE,
    cex = 1, pt.open.col = 1, pch1 = 1, pch2 = 21,
    return = "data", extracolumn = NULL
  )

Arguments

mdat

list, output by get_metadata

xvar

character, name of x variable

yvar

character, name of y variable

xlim

numeric, x axis limits

ylim

numeric, y axis limits

xlab

x axis label

ylab

y axis label

plot.it

logical, make a plot?

add

logical, add to existing plot?

identify

logical, run identify for interactive identification of points?

points

logical, plot points?

lines

logical, plot lines?

title

character, plot title

cex

numeric, point size

pt.open.col

color of border for open point symbols (pch > 20)

pch1

numeric, symbol for samples in group 1

pch2

numeric, symbol for samples in group 2

return

character, indicates whether to return ‘⁠data⁠’ values or group ‘⁠means⁠’

extracolumn

character, the name of one or more extra columns (from get_metadata) to include in the output

Details

plot_metrics plots the values of Z_C and n_H2O (or other variables indicated by xvar and yvar) provided in mdat$metrics. Symbol shape and color (pch and col) are taken from mdat$metadata.

If pch1 and pch2 are provided, then samples are classified into two groups according to the value of mdat$metadata$pch. Mean values of the chemical metrics for each group are plotted with star-shaped symbols.

Value

For plot_metrics, a data frame with columns for study name and Run IDs (‘⁠name⁠’, ‘⁠Run⁠’), chemical metrics (taken from mdat$metrics), and symbols and colors for plotting points (‘⁠pch⁠’, ‘⁠col⁠’).

References

Herlemann, D. P. R., Lundin, D., Andersson, A. F., Labrenz, M. and Jürgens, K. (2016) Phylogenetic signals of salinity and season in bacterial community composition across the salinity gradient of the Baltic Sea. Front. Microbiol. 7, 1883. doi:10.3389/fmicb.2016.01883

Examples

# Make a plot for the Baltic Sea salinity gradient
# (data from Herlemann et al., 2016)
RDPfile <- system.file("extdata/RDP/HLA+16.tab.xz", package = "chem16S")
RDP <- read_RDP(RDPfile)
map <- map_taxa(RDP, refdb = "RefSeq_206")
metrics <- get_metrics(RDP, map, refdb = "RefSeq_206")
mdatfile <- system.file("extdata/metadata/HLA+16.csv", package = "chem16S")
mdat <- get_metadata(mdatfile, metrics)
pm <- plot_metrics(mdat)
# Add a legend
legend <- c("< 6 PSU", "6-20 PSU", "> 20 PSU")
pch <- c(24, 20, 21)
pt.bg <- c(3, NA, 4)
legend("bottomright", legend, pch = pch, col = 1, pt.bg = pt.bg, bg = "white")
# Classify samples with low and high salinity
ilo <- mdat$metadata$salinity < 6
ihi <- mdat$metadata$salinity > 20
# Add convex hulls
canprot::add_hull(pm$Zc[ilo], pm$nH2O[ilo],
  col = adjustcolor("green3", alpha.f = 0.3), border = NA)
canprot::add_hull(pm$Zc[ihi], pm$nH2O[ihi],
  col = adjustcolor("blue", alpha.f = 0.3), border = NA)

# Show points for all samples and larger star-shaped points
# for mean values of high- and low-salinity samples
plot_metrics(mdat, pch1 = 21, pch2 = 24)

# Plot nO2 instead of Zc
plot_metrics(mdat, xvar = "nO2")

# Make a plot for only Proteobacteria
RDP <- read_RDP(RDPfile, lineage = "Proteobacteria")
map <- map_taxa(RDP, refdb = "RefSeq_206")
metrics <- get_metrics(RDP, map, refdb = "RefSeq_206")
mdatfile <- system.file("extdata/metadata/HLA+16.csv", package = "chem16S")
mdat <- get_metadata(mdatfile, metrics)
mdat$metadata$name <- paste(mdat$metadata$name, "(Proteobacteria)")
plot_metrics(mdat)