This is example R code for using opm in a standardised setting to create files describing phenotype microarray results run in PM mode, with special emphasis on the kind of information needed in microbial taxonomy.
It reads all CSV files within the working directory and produces YAML, HTML and PDF output. This code naively assumes that the relevant meta-information defining the organism groups is found within the CSV files. Apart from that, it would need only minimal adaptations for real-world use after modifying a copy of it within an R text editor.
But note that collect_template and include_metadata offer a much more
flexible mechanism for including metadata.
For advanced users, we recommend to use batch_opm instead for most of the
steps conducted below.
Author: Markus Goeker
library(opm)
Names of replicate IDs. If not within the input CSV data, automatically generated below. Technically not mandatory, but useful for addressing each plate.
replicate <- "Replicate"
Name of the organism entries used. If not directly found within the CSV data, a warning would be raised. If you have no strain numbers, use the term that best describes the data you have.
organism <- "Strain Number"
We assume that only the CSV files within the working directory should be
input and that the data read should be grouped by plate type. This code fails
if unreadable CSV files are there (the include and/or exclude argument
would be needed).
x <- read_opm(getwd(), convert = "grp", include = list("csv"))
stopifnot(names(x) == plate_type(x)) # the names already indicate the plate type
x # shows a summary of the contained elements
## [1] Length Plate.type Aggregated Discretized
## <0 rows> (or 0-length row.names)
##
## => MOPMX object with 0 element(s), details are shown above.
## Access the elements with [[ or $ to apply specific methods.
This needs to be adapted to the way CSV data have been recorded. Remember
that collect_template and include_metadata offer a much more flexible
mechanism for including metadata.
Create data frame without converting strings to factors.
md <- to_metadata(csv_data(x))
Create replicate IDs if they are not included.
if (!replicate %in% names(md)) {
message("NOTE: inserting replicate IDs")
md[, replicate] <- seq_len(nrow(md))
} else if (any(bad <- is.na(md[, replicate]))) {
message("NOTE: inserting replicate IDs")
md[bad, replicate] <- seq_len(nrow(md))[bad] # might yield non-unique IDs
md[, replicate] <- make.unique(md[, replicate]) # now enforce unique IDs
}
## NOTE: inserting replicate IDs
Insert a dummy organism entry if none is there, with a warning.
if (!organism %in% names(md)) {
md[, organism] <- rep_len("Unkown organism", nrow(md))
warning("inserting dummy organism name", immediate. = TRUE)
} else if (any(bad <- is.na(md[, organism]))) {
md[bad, organism] <- "Unkown organism"
warning("inserting dummy organism name", immediate. = TRUE)
}
## Warning: inserting dummy organism name
Adding metadata is easiest via a data frame whose order of rows is the same
than the order of OPM objects within the OPMS object and the order of
OPMX objects within a MOPMX object. We get such a data frame from the CSV
data, of course.
metadata(x) <- md[, c(organism, replicate)]
That's the time-consuming step here. Specify at most as many cores as you really have on your machine, and keep in mind that parallelisation does not work under Windows (for reasons caused by R itself).
if (grepl("windows", Sys.info()[["sysname"]], TRUE, TRUE)) {
nc <- 1
} else {
nc <- 8
}
x <- do_aggr(x, boot = 0, cores = nc, method = "splines",
options = set_spline_options("smooth.spline"))
Let's have a look at the upper part of the aggregation settings.
head(aggr_settings(x, join = "json"), 1)
## NULL
This discretisation is using exact k-means partitioning, without estimation of an intermediary state. This is OK if > 1 replicates are there and one can calculate ambiguity in another manner (see below).
x <- do_disc(x, cutoff = FALSE)
Let's have a look at the upper part of the discretisation settings.
head(disc_settings(x, join = "json"), 1)
## NULL
Set the CSS file that comes with opm as default for HTML tables.
opm_opt(css.file = opm_files("css"))
An alternative would be to copy it to the current working directory as
follows (and use the copy):
file.copy(opm_files("css")[[1]], opm_opt("css.file"), overwrite = TRUE)
opm_opt(css.file = "opm_styles.css")
For each plate type, create each of the following files:
for (name in names(x)) {
# Write YAML file. As it contains aggregated and discretised data and
# settings, too, full reproducibility is guaranteed.
#
write(to_yaml(x[[name]]), sprintf("Data_%s.yml", name))
# Write textual description of discretised results to a file, grouped per
# strain. Embed the content of the `CSS` file.
#
write(phylo_data(listing(x[[name]], as.groups = organism, html = TRUE),
html.args = html_args(embed.css = TRUE)),
sprintf("Description_%s.html", name))
# Write HTML table describing the discretised results. This cannot be done
# unless there are several replicates. Embed the content of the `CSS` file.
#
if (length(x[[name]]) > 1)
write(phylo_data(x[[name]], format = "html", as.labels = organism,
html.args = html_args(embed.css = TRUE)),
sprintf("Table_%s.html", name))
# Draw x-y-plot into PDF file.
#
pkgutils::mypdf(sprintf("Plot_%s.pdf", name))
print(xy_plot(x[[name]], include = list(organism, replicate)))
dev.off()
}