# countsimQC
[](https://github.com/csoneson/countsimQC/actions)
`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](https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx631/4345646/Towards-unified-quality-verification-of-synthetic). Bioinformatics 34(4):691-692 (2018).
## Installation
`countsimQC` can be installed from
[Bioconductor](https://www.bioconductor.org/packages/countsimQC/) 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](https://www.bioconductor.org/packages/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](http://htseq.readthedocs.io/en/release_0.9.1/) output, or from a [tximport](http://bioconductor.org/packages/release/bioc/html/tximport.html)
output object (see the
[DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) [vignette](http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html)).
`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](http://imlspenticton.uzh.ch/robinson_lab/countsimQC_example_reports/HMP_sampled_datasets_countsimQC.html)
- [Comparison of three real bulk RNA-seq data sets](http://imlspenticton.uzh.ch/robinson_lab/countsimQC_example_reports/bulkrnaseq_crossdataset_countsimQC.html)
- [Comparison of gene- and transcript-level count matrices for a single-cell RNA-seq data set](http://imlspenticton.uzh.ch/robinson_lab/countsimQC_example_reports/GSE74596_genevstx_countsimQC.html)
- [Comparison of four real scRNA-seq data sets](http://imlspenticton.uzh.ch/robinson_lab/countsimQC_example_reports/scrnaseq_crossdataset_countsimQC.html)
- [Comparison of two simulated scRNA-seq data sets to the underlying real data set](http://imlspenticton.uzh.ch/robinson_lab/countsimQC_example_reports/GSE48968-GPL13112_2simulations_countsimQC.html)
- [Comparison of six simulated bulk RNA-seq data set with different number of genes](http://imlspenticton.uzh.ch/robinson_lab/countsimQC_example_reports/countsimQC_compcodeR_simulations.html)
