This page includes the data and code necessary to reproduce the results of the following paper:
Yang Liao, Dinesh Raghu, Bhupinder Pal, Lisa Mielke and Wei Shi. cellCounts: fast and accurate quantification of 10x Chromium single-cell RNA sequencing data. Under review.
A Linux computer running an operating system of CentOS 7 (or later) or Ubuntu 20.04 (or later) is recommended for running this analysis. The computer should have >2 TB of disk space and >64 GB of RAM. The following software packages need to be installed before running the analysis. Software executables generated after installation should be included in the $PATH environment variable.
- R (v4.0.0 or newer) https://www.r-project.org/
- Rsubread (v2.12.2 or newer) http://bioconductor.org/packages/3.16/bioc/html/Rsubread.html
- CellRanger (v6.0.1) https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome
- STARsolo (v2.7.10a) https://github.com/alexdobin/STAR
- sra-tools (v2.10.0 or newer) https://github.com/ncbi/sra-tools
- Seurat (v3.0.0 or newer) https://satijalab.org/seurat/
- edgeR (v3.30.0 or newer) https://bioconductor.org/packages/edgeR/
- limma (v3.44.0 or newer) https://bioconductor.org/packages/limma/
- mltools (v0.3.5 or newer) https://cran.r-project.org/web/packages/mltools/index.html
Reference packages generated by 10x Genomics are also required for this analysis and they can be downloaded from the following link (2020-A version for individual human and mouse reference packages should be selected):
After all these are done, you can simply run the shell script ‘test-all-new.bash’ to perform all the analyses carried out in the paper. This script will automatically download the mixture scRNA-seq data from the SRA database, and it will output a text file called ‘test-all.log’ that contains all the screen outputs and speed/accuracy results of CellRanger, STARsolo and cellCounts.
- School of Cancer Medicine