10x Genomics
Chromium Single Cell Gene Expression

Cell Ranger1.2, printed on 12/21/2024

Single-Library Analysis with cellranger count

Cell Ranger's pipelines analyze sequencing data produced from Chromium single cell 3’ RNA-seq libraries. This involves the following steps:

1. Run cellranger mkfastq on the Illumina BCL output folder to generate FASTQ files.

2. Run cellranger count on each library that was demultiplexed by cellranger mkfastq.

3. Optionally, run cellranger aggr to aggregate multiple libraries from a single experiment that were analyzed by cellranger count.

4. Optionally run cellranger reanalyze to re-run the secondary analysis on a library or aggregated set of libraries (i.e., PCA, t-SNE, and clustering).

For the following example, assume that the Illumina BCL output is in a folder named /sequencing/140101_D00123_0111_AHAWT7ADXX.

Run cellranger mkfastq

First, follow the instructions on running cellranger mkfastq to generate FASTQ files. For example, if the flowcell serial number was HAWT7ADXX, then cellranger mkfastq will output FASTQ files in HAWT7ADXX/outs/fastq_path.

Run cellranger count

To generate single-cell gene counts for a single library, run cellranger count with the following arguments. For a complete list of command-line arguments, run cellranger count --help.

After determining these input arguments, run cellranger:

$ cd /home/jdoe/runs
$ cellranger count --id=sample345 \
                   --transcriptome=/opt/refdata-cellranger-GRCh38-1.2.0 \
                   --fastqs=/home/jdoe/runs/HAWT7ADXX/outs/fastq_path \
                   --indices=SI-3A-A1 \
                   --cells=1000

Following a set of preflight checks to validate input arguments, cellranger count pipeline stages will begin to run:

Martian Runtime - 2.1.2
 
Running preflight checks (please wait)...
2016-11-10 14:23:52 [runtime] (ready)           ID.sample345.SC_RNA_COUNTER_CS.SC_RNA_COUNTER.SETUP_CHUNKS
2016-11-10 14:23:55 [runtime] (split_complete)  ID.sample345.SC_RNA_COUNTER_CS.SC_RNA_COUNTER.SETUP_CHUNKS
2016-11-10 14:23:55 [runtime] (run:local)       ID.sample345.SC_RNA_COUNTER_CS.SC_RNA_COUNTER.SETUP_CHUNKS.fork0.chnk0.main
...

By default, cellranger will use all of the cores available on your system to execute pipeline stages. You can specify a different number of cores to use with the --localcores option; for example, --localcores=16 will limit cellranger to using up to sixteen cores at once. Similarly, --localmem will restrict the amount of memory (in GB) used by cellranger.

The pipeline will create a new folder named with the sample ID you specified (e.g. /home/jdoe/runs/sample345) for its output. If this folder already exists, cellranger will assume it is an existing pipestance and attempt to resume running it.

Output Files

A successful cellranger count run should conclude with a message similar to this:

2016-11-10 16:10:09 [runtime] (join_complete)   ID.sample345.SC_RNA_COUNTER_CS.SC_RNA_COUNTER.SUMMARIZE_REPORTS
 
Outputs:
- Run summary HTML:                      /opt/sample345/outs/web_summary.html
- Run summary CSV:                       /opt/sample345/outs/metrics_summary.csv
- BAM:                                   /opt/sample345/outs/possorted_genome_bam.bam
- BAM index:                             /opt/sample345/outs/possorted_genome_bam.bam.bai
- Filtered gene-barcode matrices MEX:    /opt/sample345/outs/filtered_gene_bc_matrices
- Filtered gene-barcode matrices HDF5:   /opt/sample345/outs/filtered_gene_bc_matrices_h5.h5
- Unfiltered gene-barcode matrices MEX:  /opt/sample345/outs/raw_gene_bc_matrices
- Unfiltered gene-barcode matrices HDF5: /opt/sample345/outs/raw_gene_bc_matrices_h5.h5
- Secondary analysis output CSV:         /opt/sample345/outs/analysis
- Per-molecule read information:         /opt/sample345/outs/molecule_info.h5
 
Pipestance completed successfully!

The output of the pipeline will be contained in a folder named with the sample ID you specified (e.g. sample345). The subfolder named outs will contain the main pipeline output files:

Once cellranger count has successfully completed, you can browse the resulting summary HTML file in any supported web browser, or refer to the Understanding Output section to explore the data by hand.