% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{diffwrap_counts}
\alias{diffwrap_counts}
\title{Simulated RNA-Seq read counts for the package examples}
\format{
An \code{integer} matrix with 405 rows (400 genes plus 5 summary rows) and 8 columns
  (samples \code{S01}-\code{S08}). Row names are gene identifiers, column names sample names.
}
\source{
Simulated by \file{data-raw/make_example_data.R} from
  \file{inst/extdata/example_counts.tsv}.
}
\usage{
diffwrap_counts
}
\description{
A small simulated count matrix used throughout the examples, tests and the
  vignette. The data are \strong{not} real measurements: they were generated from a negative
  binomial (gamma-Poisson) model so that the package can be demonstrated and tested without
  any external data or network access.
}
\details{
The experiment consists of eight samples in two groups of four
  (\code{"control"} and \code{"treated"}). Each of the four subjects (\code{P1}-\code{P4})
  contributes one control and one treated sample, so the data support paired and blocked
  analyses as well as the simple unpaired comparison. A subject-specific offset is built in,
  giving the paired designs a real effect to remove.

  Sixty of the 400 genes are truly differentially expressed (30 up-, 30 down-regulated) with
  absolute log2 fold changes between 1.2 and 3. The biological dispersion is 0.15
  (biological coefficient of variation of about 0.39) and library sizes are roughly 2-3
  million reads.

  Forty genes are given deliberately low expression so that they straddle the default
  \code{strict} filtering threshold of \code{diff_expr_filter_counts()} (more than 5 counts
  per million in at least half of the samples, which corresponds to about 13 counts here);
  28 of them are removed by that filter.

  The matrix additionally carries the five \command{htseq-count} summary rows
  (\code{__no_feature}, \code{__ambiguous}, \code{__too_low_aQual}, \code{__not_aligned} and
  \code{__alignment_not_unique}) that \code{diff_expr_filter_counts()} is expected to strip.
  They are retained on purpose so that the filtering step can be demonstrated.

  Gene identifiers are well-formed but fictitious Ensembl gene IDs; they do not correspond
  to real genes and will not return annotation from 'biomart'.
}
\examples{
data(diffwrap_counts)
dim(diffwrap_counts)
head(diffwrap_counts[, 1:4])
# the htseq-count summary rows that get filtered out:
diffwrap_counts[grep("^__", rownames(diffwrap_counts)), 1:4]
}
\seealso{
\code{\link{diffwrap_samp_info}} for the matching sample sheet.
}
\keyword{datasets}
