Awesome
countsimQC
countsimQC
is an R package that provides functionality to create a
comprehensive report comparing many different characteristics across multiple
count data sets. One important use case is comparing one or more
synthetic (e.g., RNA-seq) count matrices to a real count matrix, possibly the
one based on which the synthetic data sets were generated. However, any
collection of one or more count matrices can be visualized and compared.
If you use countsimQC
for your work, we appreciate if you cite the
accompanying paper:
- Soneson C and Robinson MD: Towards unified quality verification of synthetic count data with countsimQC. Bioinformatics 34(4):691-692 (2018).
Installation
countsimQC
can be installed from
Bioconductor with the
following commands. Note that R version >= 3.5 and Bioconductor
version >= 3.8 are required in order to use the BiocManager
package.
If you have an older version of R (3.4), you can still install
countsimQC
v0.5.4 (see the Releases
tab in the GitHub repository).
Please see the NEWS
file for differences between versions.
## Install `BiocManager` if needed
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## Install countsimQC
BiocManager::install("countsimQC")
Getting started
To run countsimQC
and generate a report, you simply need to call the
function countsimQCReport()
, with an input consisting of a named list of
DESeqDataSets
(see the
DESeq2
package for a description of this class). Each DESeqDataSet
should
correspond to one data set and contain a count matrix, a data frame with sample
information and a design formula, which is needed for proper dispersion
calculations. To generate a DESeqDataSet
from a count matrix counts
, a
sample information data frame sample_df
and a design formula formula
(of the form ~ predictors
), you can do as follows:
library(DESeq2)
dds <- DESeqDataSetFromMatrix(countData = counts,
colData = sample_df,
design = formula)
There are many other ways of generating valid DESeqDataSets
, depending on in
what form your counts are (e.g., reading directly from
HTSeq output, or from a tximport
output object (see the
DESeq2 vignette).
countsimQC
contains an small example list with subsets of three data sets:
two synthetic ones and the real data set that was used to generate them. The
following code generates a comparative report for these three data sets:
library(countsimQC)
data(countsimExample)
countsimQCReport(ddsList = countsimExample,
outputFile = "countsimReport.html",
outputDir = "./",
description = "This is a comparison of three count data sets.")
For more detailed information about how to use the package, we refer to the vignette:
browseVignettes("countsimQC")
Example reports
- Comparison of 16S microbiome species count matrices for four body subsites from the Human Microbiome Project
- Comparison of three real bulk RNA-seq data sets
- Comparison of gene- and transcript-level count matrices for a single-cell RNA-seq data set
- Comparison of four real scRNA-seq data sets
- Comparison of two simulated scRNA-seq data sets to the underlying real data set
- Comparison of six simulated bulk RNA-seq data set with different number of genes