`bamtofastq` is a tool for converting 10x Genomics BAM files back into FASTQ files that can be used as inputs to re-run analysis. The FASTQs will be output into a directory structure identical to the `mkfastq` or `bcl2fastq` tools, so they are ready to input into the next pipeline (e.g. `cellranger count`, `spaceranger count`).

10x Genomics pipelines require FASTQs (with embedded barcodes) as input, so we developed `bamtofastq` to help users who want to reanalyze 10x Genomics data but only have access to BAM files. For example, some users may want to store BAM files only. Others might have downloaded our BAM data from NCBI SRA. The location of the 10x Genomics barcode varies depending on product and reagent version. `bamtofastq` determines the appropriate way to construct the original read sequence from the sequences and tags in the BAM file.

`bamtofastq` works with BAM files output by the following 10x Genomics pipelines:

   * `cellranger` ([see exceptions below](#issues))
   * `cellranger-atac`
   * `cellranger-arc`
   * `cellranger-dna`
   * `spaceranger`
   * `longranger` 

Background

`bamtofastq` is available for Linux and is compatible with RedHat/CentOS 5.2 or later, and Ubuntu 8.04 or later.

 * `bamtofastq` comes pre-bundled with the latest versions of the Cell Ranger, Cell Ranger ARC, Cell Ranger ATAC, and Space Ranger pipelines. You can find the executable in the `/lib/bin` folder within the installation, e.g. `cellranger-x.y.z/lib/bin/bamtofastq`. Once your pipeline is installed and on your `PATH`, you can run `[pipeline] bamtofastq`, (e.g. `cellranger bamtofastq`, `spaceranger bamtofastq`).
 * You can also [download bamtofastq from github](https://github.com/10XGenomics/bamtofastq/releases).


`bamtofastq` is a single executable that can be run directly and requires no compilation or installation. Place the executable file in a directory that is on your `PATH`, and make sure to `chmod 700` to make it executable.

Download and installation

10x Genomics BAMs produced by Cell Ranger v1.2+, Cell Ranger ATAC v1.0+, Cell Ranger ARC v1.0+, Space Ranger v1.0+, Cell Ranger DNA v1.0+, and Long Ranger v2.1+ contain header fields that permit automatic conversion to the correct FASTQ sequences. BAMs produced by older 10x Genomics pipelines may require special arguments or have some caveats, see [known issues](#issues) for details.

The FASTQ files output by `bamtofastq` contain the same set of sequences that were input to the original pipeline run, although the original order will not be preserved. 10x Genomics pipelines are generally insensitive to the order of the input data, so you can expect nearly identical results when re-running pipelines with `bamtofastq` outputs.

The usage of the `bamtofastq` command is as follows:

 ```
 bamtofastq [options] [bam-path][output-path]
 ```


Replace `[bam-path]` with the path to the input BAM, `[output-path]` with the path to the output directory, and include any relevant `[options]` ([listed below](#options)). For example, if your BAM file is located at `/path/to/mydata.bam`, you want the output in `/path/to/home/directory`, and you want to use eight threads, the command would be:

 ```
 bamtofastq --nthreads=8 /path/to/mydata.bam /path/to/home/directory
 ```


You can also print the options from the command line:

 ```
 bamtofastq --help
 ```


Run times for full-coverage WGS BAMs may be several hours.

Running the tool

`bamtofastq` options:

 Option | Description
 --- | ---
 `--nthreads=n` | Threads to use for reading BAM file. Default: 4
 `--locus=locus`| Optional. Only include read pairs mapping to locus. Use `chrom:start-end` format.
 `--reads-per-fastq=N`  | Number of reads per FASTQ chunk. Default: 50000000
 `--gemcode`  | Convert a BAM produced from GemCode data (Longranger 1.0 - 1.3)
 `--lr20` | Convert a BAM produced by Longranger 2.0
 `--cr11` | Convert a BAM produced by Cell Ranger 1.0-1.1
 `--bx-list=L`  | Only include BX values listed in text file `L`. Requires BX-sorted and indexed BAM file (see Long Ranger support for details).
 `--help` | Show the help screen

Options


* BAMs produced for TCR or BCR data, by aligning to a V(D)J reference with `cellranger vdj` or `cellranger multi`, are not supported by `bamtofastq`.
* Special tags included by 10x Genomics pipelines are required to reconstruct the original FASTQ sequences correctly. If your BAM file lacks the appropriate headers, you will get an error message: `WARNING: no @RG (read group) headers found in BAM file.`
* A common problem is using **edited** BAM files from SRA that are missing tags produced by Cell Ranger.
* You need the BAM file from the section labeled **Original Format** on the SRA downloads page.
* Here is an example of a [SRA dataset with the **Original Format** BAM available](https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR7276476).
* Only the **Original Format** BAM files have the necessary BAM tags preserved to work correctly with `bamtofastq`.
* The latest versions of `cellranger`, `cellranger-atac`, `cellranger-arc`, `cellranger-dna`, and `longranger` generate BAM files that automatically reconstruct complete FASTQ files representing all input reads. BAMs produced by older versions of `cellranger` and `longranger` have some caveats, listed below:

 Package |Version  |Pipelines| Extra Arguments |Complete FASTQs
 --- | --- | --- | --- | ---
 Cell Ranger |1.3+ |count| none| Yes
 Cell Ranger |1.2| count |none |Reads without a valid barcode will be absent from FASTQ. (These reads are ignored by Cell Ranger)
 Cell Ranger|  1.0-1.1 |count  |`--cr11` |Reads without a valid barcode will be absent from FASTQ. (These reads are ignored by Cell Ranger)
 Cell Ranger ARC |1.0.0+ |count  |none |Any sequenced bases in the i5 index read that are not part of the 10x Genomics barcode are dropped from the FASTQ output.
 Cell Ranger ATAC| 1.0.0+| count |none|  Any sequenced bases in the i5 index read that are not part of the 10x Genomics barcode are dropped from the FASTQ output.
 Cell Ranger DNA |1.0.0+ |cnv  |none|  Yes
 Long Ranger |2.1.3+ |wgs, targeted, align, basic  |none|  Yes
 Long Ranger |2.1.0 - 2.1.2  |wgs, targeted| none  |Yes
 Long Ranger |2.0| wgs, targeted|  `--lr20`| Yes
 Long Ranger |2.0.0 - 2.1.2| align, basic| Not Supported|N/A
 Long Ranger|  1.3 (GemCode) |wgs, targeted  |`--gemcode`  |Reads without a valid barcode will be absent from FASTQ. This will result in a ~5-10% loss of coverage.

Known issues and limitations

Converting 10x Genomics BAM Files to FASTQ

10x Genomics does not require third parties to include trademark ™ or registered trademark ® symbols for 10X, 10x Genomics, Chromium or any of the rest of its registered marks. 

Use of 10x Genomics trademarks

To cite 10x Genomics datasets, user guides, demonstrated protocols, technical notes, applications notes, or 10x Genomics software, use the following formats:

### **Datasets**

 *PBMCs from C57BL/6 mice (v1, 150x150)*, Single Cell Immune Profiling Dataset by Cell Ranger 3.1.0, 10x Genomics, (2019, July 24). 


### **User Guides/Demonstrated Protocols**

 *Chromium Single Cell V(D)J Reagent Kits with Feature Barcoding technology for Cell Surface Protein*, Document Number CG000186 Rev A, 10x Genomics, (2019, July 25).
 
 
 Document and Revision Numbers, such as CG000186 Rev A are located on the cover page in user guides and technical notes. Other literature lists the part number in the Copyright note at the bottom of the last page.


### **Technical Notes/Application Notes**

 *Technical Note – Resolving Cell Types as a Function of Read Depth and Cell Number*, Document Number CG000148, 10x Genomics, (2019, April 30).


### **10x Genomics software**

Use the following format when citing 10x Genomics software: 

 * 10x Genomics Software Name and Version x.y.z
 
 For example:
  * 10x Genomics Cell Ranger 7.1.0 
  * 10x Genomics Cell Ranger 7.0.0
  * 10x Genomics Space Ranger 2.1.0 
  * 10x Genomics Loupe Browser 6.5.0
  * 10x Genomics Loupe V(D)J Browser 3.0.0


**Note**: When citing software, be sure to specify the relevant software version number.

### **10x Genomics Cloud Analysis**

 To cite datasets generated in 10x Genomics Cloud Analysis, use the following format:
  * 10x Genomics Software Name and Version x.y.z using 10x Genomics Cloud Analysis


  For example:
   * 10x Genomics Cell Ranger 5.0.1 using 10x Genomics Cloud Analysis
   * 10x Genomics Cell Ranger 4.0.0 using 10x Genomics Cloud Analysis


### **Citations for 10x Genomics publications**

#### Supernova

Weisenfeld, Neil L., Kumar, Vijay, Shah, Preyas, Church, Deanna M., Jaffe, David B. (2017). Direct determination of diploid genome sequences. *Genome Research*. **27**: 757-767, [doi:10.1101/gr.214874.116](https://doi.org/10.1101/gr.214874.116)

#### Long Ranger

Marks, Patrick, et al, (2019). Resolving the full spectrum of human genome variation using Linked-Reads. *Genome Research*. **29**: 635-645, [doi:10.1101/gr.234443.118](https://doi.org/10.1101/gr.234443.118)

#### Cell Ranger

Zheng, Grace X.Y., Terry, Jessica M., [...] Bielas, Jason H. (2017). Massively parallel digital transcriptional profiling of single cells. *Nature Communications*. **8**: 1-12, [doi:10.1038/ncomms14049](https://doi.org/doi:10.1038/ncomms14049)

#### Cell Ranger ATAC

Satpathy, Ansuman T. et al, Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. *Nature Biotechnology*. **37**: 925-936, [doi:10.1038/s41587-019-0206-z](https://doi.org/10.1038/s41587-019-0206-z)

Citation guidelines

10x Genomics Citations

`index-hopping-filter` is a tool that filters index-hopped reads from a set of demultiplexed samples. The tool detects and removes likely index hopped reads from demultiplexed FASTQs, and in turn emits new, filtered, FASTQs with similar file and directory layout as the inputs, suitable for use with `cellranger count` and `cellranger vdj`.

  Note: Currently `index-hopping-filter` only supports samples produced by 3' Single Cell Gene Expression, 5' Single Cell Immune Profiling, Fixed RNA Profiling (FLEX), and Single Cell ATAC solutions. It cannot be used on Spatial Gene Expression samples. 


Index hopping is a known phenotype that can impact any pooled samples sequenced on Illumina platforms utilizing both patterned flow cells and exclusion amplification chemistry (i.e. HiSeqX, HiSeq3000/4000, NovaSeq).

Below is some literature from Illumina outlining the background behind index hopping:
 * [Effects of Index Misassignment on Multiplexing and Downstream Analysis](https://www.illumina.com/content/dam/illumina-marketing/documents/products/whitepapers/index-hopping-white-paper-770-2017-004.pdf)
 * [Minimize index hopping in multiplexed runs](https://www.illumina.com/science/education/minimizing-index-hopping.html)


To reduce and filter hopped sample indexes, we recommend the following library preparation best practices for any indexed sequencing library:
 * Remove free adapters from library preps
 * Ensure no sign of leftover primers or adaptors are present when performing post Library Construction QC (BioAnalyzer/TapeStation/Fragment Analyzer)
 * Only pool libraries before sequencing
 * A final 1.0X SPRI can be performed on these pooled libraries to remove any traces of free adaptors
 * Use Unique Dual Indexes vs. Single indexed libraries or Combinatorial Dual Indexes - Unique Dual Indexes will be available for Single Cell Gene Expression, Single Cell Immune Profiling, and Spatial Gene Expression Products. Visium - Q4 2019, SC GEX and IP with full software support - mid 2020


10x created `index-hopping-filter` to reduce the number of index-hopped reads reaching downstream analysis. It detects, from whole flow cells, duplicate library molecules appearing in more than one sample. Reads determined to be so duplicated are removed. A sample where the molecule is present with a high read count indicates the molecule is from that sample, and reads of that molecule are not removed.

Some index hopped library molecules are only observed in incorrect samples and thus cannot be detected as index hopped. For this reason, the tool mitigates but cannot eliminate all index hopped reads. In our testing, using samples sequenced to the recommended sequencing reads per cell, `index-hopping-filter` removes >70% of index hopped reads compared to a dual-indexed ground truth. At lower sequencing depths, or if less than all samples from a flow cell are used, a smaller fraction of index hopped reads will be detected. The following graph depicts the sensitivity (over a whole flow cell) of `index-hopping-filter` to reads from invading barcodes compared to a dual-indexed dataset as a function of sequencing depth.

 <Flex justifyContent={"center"}><Image src={'https://cdn.10xgenomics.com/image/upload/v1689029709/software-support/Cell%20Ranger/ihf-inv-sens.png'} width={'400px'} length={'400px'}/></Flex>

`index-hopping-filter` is available for Linux and is compatible with Redhat/CentOS 5.2 or later, and Ubuntu 8.04 or later.

[Download index-hopping-filter v1.1](https://github.com/10XGenomics/index_hopping_filter/releases/download/v1.1.0/index-hopping-filter)

`index-hopping-filter` is a single executable that can be run directly and requires no compilation or installation. Place the executable in a directory on your `PATH` and make sure to `chmod 700` to make it executable.

The `mkfastq` subcommand of `cellranger` produces FASTQ files directly usable by `index-hopping-filter`. If you have configured the IEM SampleSheet.csv or `cellranger mkfastq` simple CSV to uniquely identify distinct 10x libraries, then you can use the `cellranger mkfastq` output directory as input to `index-hopping-filter filter`.

#### Inputs of `index-hopping-filter` (10x Single Cell Gene Expression or Immune Profiling)

`index-hopping-filter`’s two subcommands both accept as input either a `cellranger mkfastq` output path or an `index-hopping-filter` configuration CSV. This CSV file must conform to the following schema, where `SampleId` must be an integer in the range `[0, 65534]`.

 | R1 | R2 | SampleId |
 |----|----|----------|
 | `/path/to/sample1/R1.fastq.gz` | `/path/to/sample1/R2.fastq.gz` | 1 |
 | `/path/to/sample2/R1.fastq.gz` | `/path/to/sample2/R2.fastq.gz` | 2 |


#### Inputs of `index-hopping-filter` (10x Single Cell ATAC)

`index-hopping-filter`’s two subcommands both accept as input either a `cellranger mkfastq` output path or an `index-hopping-filter` configuration CSV. This CSV file must conform to the following schema, where `SampleId` must be an integer in the range `[0, 65534]`. Compared to the configuration CSV for 10x Single Cell Gene Expression or Immune Profiling solutions, this configuration CSV requires an additional `R3` column, and the `--atac` flag must be provided to the subcommand.

 | R1 | R2 | R3 | SampleId |
 |----|----|----|----------|
 | `/path/to/sample1/R1.fastq.gz` | `/path/to/sample1/R2.fastq.gz` | `/path/to/sample1/R3.fastq.gz` | 1 |
 | `/path/to/sample2/R1.fastq.gz` | `/path/to/sample2/R2.fastq.gz` | `/path/to/sample2/R3.fastq.gz` | 2 |

Inputs

#### Outputs of `index-hopping-filter mkcsv`
 

`index-hopping-filter mkcsv` produces a configuration CSV to `stdout` based on the input. Redirect `stdout` to a file (e.g. `index-hopping-filter mkcsv /path/to/mkfastq/outs 1>config.csv`) and modify it as needed.

#### Outputs of `index-hopping-filter filter`

 Outputs | Description
 --- | ---
 outs/fastq_path | Directory containing emitted FASTQs with suspect index hopped reads removed.
 outs/web_summary.html | An HTML web summary that describes the number and percentage of reads removed per sample.
 outs/filtered_reads.csv | A CSV file describing the starting number of reads and the number of reads removed for each SampleId.
 _log | A log file for diagnostic purposes.

Outputs

### index-hopping-filter mkcsv

 Option | Description
 --- | ---
 `-a`, `--atac` | Generate a configuration csv for 10x scATAC-seq data
 `-h`, `--help` | Prints help information
 `-v`, `--version` | Prints version information


### index-hopping-filter filter

 Option | Description 
 --- | ---
 `-a`, `--atac` | Provide if you are running 10x scATAC-seq data
 `-n`, `--nthreads` | Maximum number of threads to use (default: number of cores) 
 `-h`, `--help` | Prints help information
 `-v`, `--version` | Prints version information

### Product compatibility

`index-hopping-filter` currently only works with 10x Single Cell Gene Expression, 10x Single Cell Immune Profiling, and 10x Single Cell ATAC solutions. Future versions may include support for other products.

### Multiple FASTQs from the same GEM wells

Correctly specifying `SampleId` is important. If multiple samples drawn from the same underlying 10x GEM well are passed into `index-hopping-filter` with distinct `SampleIds`, then `index-hopping-filter` will detect many reads as index hopped and remove them. This scenario arises when e.g. sequencing Immune Profiling-enriched libraries alongside the Gene Expression libraries from which they were derived. To avoid these false positive index hopping calls, use `index-hopping-filter mkcsv` to construct an initial configuration CSV, providing the `cellranger mkfastq` output directory as input. Next, modify the CSV to give demultiplexed samples derived from the same 10x GEM well a common `SampleId` value in the CSV.

Known issues

Mitigating Index Hopped Reads In Silico

Updated: July 22, 2022

In 10x Genomics whole transcriptome Gene Expression data, intronic mapped reads account for 20-40% of the reads. These reads were not counted by default in past versions of Cell Ranger. Recent data has indicated that intronic reads are [usable data](/support/single-cell-gene-expression/documentation/steps/sequencing/interpreting-intronic-and-antisense-reads-in-10-x-genomics-single-cell-gene-expression-data) (they arise from poly-A tracts in the transcripts, and are not generated via priming from genomic DNA). Including intronic reads, for both cellular and nuclei samples, could lead to higher sensitivity (higher UMI counts, more genes per cell) and less wasted sequencing.

This [Technical Note](/support/single-cell-gene-expression/documentation/steps/sequencing/interpreting-single-cell-gene-expression-data-with-and-without-intronic-reads) demonstrates how including intronic reads in Cell Ranger analysis resulted in more usable data and higher sensitivity for Single Cell 3’ and 5’ Gene Expression cellular datasets. In addition, for a Single Cell 3' Gene Expression 10k peripheral blood mononuclear cell (PBMC) dataset, cell type annotation and differential gene expression analyses were comparable with or without intronic reads.

<SupportCallout level={"info"}>
  <Text as={"span"}>
      {'New in Cell Ranger v7.0: Intronic reads are counted by default for whole transcriptome gene expression data.'}
  </Text>
</SupportCallout><br></br>

To empower our customers with higher sensitivity, Cell Ranger **v7.0 and later** include intronic reads **by default** towards UMI counting. Intronic reads can be included manually in your analysis with Cell Ranger **v5.0 - 6.1** with the following options:

 * If you are running the [cellranger count pipeline](/support/software/cell-ranger/analysis/running-pipelines/cr-gex-count), you can add the `--include-introns` flag to the command.
 * If you are running the `cellranger multi` pipeline for [3' Single Cell Gene Expression products](/support/software/cell-ranger/analysis/running-pipelines/cr-3p-multi) or [5' Single Cell Immune Profiling products](/support/software/cell-ranger/analysis/cr-5p-multi), you can add `include-introns,true` to the `[gene-expression]` section of Multi Config CSV.
 

If you have any concerns or questions, please reach out to the 10x Genomics support team at support@10xgenomics.com. 

Overview


* [Why should I include introns for my single cell whole transcriptome gene expression data analysis?](https://kb.10xgenomics.com/hc/en-us/articles/4998628924429)
* [Should I reanalyze my previous single cell data, if I have not included introns before?](https://kb.10xgenomics.com/hc/en-us/articles/4998627635213)
* [Are there any biases introduced by using intronic reads?](https://kb.10xgenomics.com/hc/en-us/articles/4998683531661)
* [I do not want to include intronic reads in my analysis. Can I do that in the upcoming Cell Ranger?](https://kb.10xgenomics.com/hc/en-us/articles/4998749959949)
* [Can I sequence less if I use intronic mode?](https://kb.10xgenomics.com/hc/en-us/articles/4998812429837)
* [Can I aggr data run with and without introns?](https://kb.10xgenomics.com/hc/en-us/articles/4998854073741)
* [Should I include introns for Targeted Gene Expression data analysis?](https://kb.10xgenomics.com/hc/en-us/articles/5078928864909)

Frequently asked questions


* [10k Human PBMCs, 3' v3.1, Chromium X (without intronic reads)](/resources/datasets/10k-human-pbmcs-3-v3-1-chromium-x-without-introns-3-1-high)
* [10k Human PBMCs, 3' v3.1, Chromium X (with intronic reads)](/resources/datasets/10k-human-pbmcs-3-v3-1-chromium-x-with-intronic-reads-3-1-high)
* [20k Mixture of NSCLC DTCs from 7 donors, 3' v3.1 (without intronic reads)](/resources/datasets/20-k-mixture-of-nsclc-dt-cs-from-7-donors-3-v-3-1-3-1-standard-6-1-0)
* [20k Mixture of NSCLC DTCs from 7 donors, 3' v3.1 (with intronic reads)](/resources/datasets/20k-mixture-of-nsclc-dtcs-from-7-donors-3-v3-1-with-intronic-reads-3-1-standard)
* [10k Human PBMCs, 5' v2.0, Chromium X (without intronic reads)](/resources/datasets/10k-human-pbmcs-5-v2-0-chromium-x-without-intronic-reads-2-standard)
* [10k Human PBMCs, 5' v2.0, Chromium X (with intronic reads)](/resources/datasets/10k-human-pbmcs-5-v2-0-chromium-x-with-intronic-reads-2-standard)
* [10k Bone Marrow Mononuclear Cells (BMMNCs), 5' v2.0 (without intronic reads)](/resources/datasets/10k-bone-marrow-mononuclear-cells-bmmncs-5-v2-0-without-intronic-reads-2-standard)
* [10k Bone Marrow Mononuclear Cells (BMMNCs), 5' v2.0 (with intronic reads)](/resources/datasets/10k-bone-marrow-mononuclear-cells-bmmncs-5-v2-0-with-intronic-reads-2-standard)

Example datasets

Recommendation on Including Introns for Gene Expression Analysis

A set of analysis pipelines that perform sample demultiplexing, barcode processing, single cell 3' and 5' gene counting, V(D)J transcript sequence assembly and annotation, and Feature Barcode analysis from single cell data.

Getting Started

Downloads & Installation

Cell Ranger Commands

Analysis

Tutorials

Algorithms

Advanced

Troubleshooting

Miscellaneous

Release Notes

Glossary

Datasets

Loupe Browser

Cell Ranger

Installation

Get Started with Cell Ranger

Convert BCL to FASTQ with Cell Ranger mkfastq

mkfastq

Cell Ranger mkfastq

Analyze Gene Expression and Feature Barcode data with Cell Ranger count

count

Cell Ranger count

aggr

Cell Ranger aggr

Rerun secondary analysis for a completed cellranger count or aggr run with different parameters

reanalyze

Cell Ranger reanalyze

Build a custom reference using Cell Ranger mkref

mkref

Cell Ranger mkref

Analyze cell multiplexing data with Cell Ranger multi

multi

Cell Ranger multi for 3' Cell Multiplexing

Analyze V(D)J and 5' GEX with Cell Ranger multi

multi for V(D)J

Cell Ranger multi for Immune Profiling

Analyze your GEX + VDJ + B cell Antigen Capture libraries with Cell Ranger multi

Cell Ranger multi for BEAM-Ab

Analyze your GEX + VDJ + T cell Antigen Capture libraries with Cell Ranger multi

Cell Ranger multi for BEAM-T

Analyze V(D)J libraries with Cell Ranger vdj

Cell Ranger vdj

Capturing Neutrophils in 10x Single Cell Gene Expression Data

Capturing neutrophils

Capturing Neutrophils

Analyze scRNA-Seq Data From a Publication Using 10x Software

Data analysis

Example Data Analysis

Analysis and visualization of data in the tumor microenvironment application note using the Loupe and Loupe V(D)J Browsers

Characterize tumor cells

Tumor Microenvironment Data Analysis and Visualization

View all tutorials

Learn more about input files required to run Cell Ranger

Generating FASTQs (mkfastq)

Specifying FASTQs

FASTQs with Illumina software

Custom Reference (mkref)

Running Pipelines

Choosing a pipeline

GEX (count)

Antibody/CRISPR (count)

3' Cell Multiplexing (multi)

VDJ-T/B only (vdj)

VDJ + GEX + FB/BEAM (multi)

Fixed RNA Profiling (multi)

Aggregate Multiple Samples (aggr)

Custom Analysis (reanalyze)

Learn more about the output files generated by Cell Ranger

Web Summary

Metrics

Feature-Barcode Matrices

Molecule Info (H5)

Secondary Analysis

Results of aggr

Gene Expression

Antibody Capture

CRISPR Guide Capture

Cell Multiplexing

VDJ-T/B

VDJ + GEX + BEAM

Fixed RNA Profiling

What is Cell Ranger?

We provide resources from protocols, user guides, and free-to-use analysis tools

Algorithms overview

Advanced pipeline content for Cell Ranger.

Browse Q&As from the 10x Genomics support team.

View terms and definitions relevant to Single Cell products.

Access free public Chromium data from 10x Genomics.

A collection of introductions, tutorials, and blogs for data analysis beyond software.

Analysis Guides

Loupe Browser is a desktop application that allows you to interactively visualize data.