- What is Cell Ranger?
What is Feature Barcoding?- Installation
- System Requirements
- Downloads
- Running Pipelines
- Generating FASTQs
- Specifying Input FASTQs
- Single-Sample Analysis
- Feature Barcoding Analysis
- Multi-Sample Aggregation
- Customized Reanalysis
- Map of the Pipeline
- Algorithms Overview
- Understanding Output
- Summary
- Analysis
- Matrices
- BAM
- Molecule Info
- CRISPR Outputs
- Antibody Outputs
- Metrics
- Downstream Analysis
- Using R
- Using Python
- Glossary of Terms
- Troubleshooting
10x Genomics
Chromium Single Cell Gene Expression
Cell Ranger3.0, printed on 11/14/2024
Secondary Analysis in R
As previously described, the feature-barcode matrices can be readily loaded into R to enable a wide variety of custom analyses using this languages packages and tools.
| Prior to Cell Ranger 3.0 10x Genomics supported an R package, called rkit, that enabled users to load and manipulate 10X data. However, due to the growth of third-party tools that provide similar capabilities, this package has been deprecated and it is unable to analyze data produced by the Cell Ranger 3.0 software. |
Third-Party Analysis Packages
The bioinformatics community is actively developing software to analyze Chromium Single Cell data. See below for some featured examples. These packages are not supported by 10x Genomics.
Seurat by the Satija Lab at New York Genome Center
Seurat 3.0 is specifically designed to handle the type of multi-data experiments enabled by Feature Barcoding technology, and can also read the latest output file produced by Cell Ranger 3.0. Note that Seurat versions below 3.0 do not support reading the new output files produced by Cell Ranger 3.0.
- Link with information how to download, install and use Seurat 3.0.
- See the tutorial named "Unsupervised identification of immune cell types and biomarkers from 2,700 PMBCs (10X Chromium)" at the Seurat website.
- Additional Seurat R tutorial script for the PBMC 33,000 Cell Dataset
Bioconductor
Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data using R, including data produced by 10X Genomics technology.
- Link to a list of Bioconductor packages for working with single cell data.
- Link to a list of Bioconductor workflows for working with single cell data.
- Link to a list of Bioconductor single cell data packages.