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10x Genomics
Chromium Single Cell Gene Expression

Single Cell Analysis

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

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

  2. Run cellranger run on each sample that was demultiplexed by cellranger demux.

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

Run demultiplexing

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

Run cellranger

To run cellranger, simply specify the following arguments:

ArgumentDescription
--idA unique run ID string: e.g. sample345
--transcriptomePath to the Cell Ranger compatible transcriptome reference e.g.
  • For a human and mouse sample, use /opt/refdata-cellranger-1.1.0/hg19_and_mm10
  • For a human-only sample, use /opt/refdata-cellranger-1.1.0/hg19
--fastqsPath of the FASTQ folder generated by cellranger demux
e.g. /home/jdoe/runs/HAWT7ADXX/outs/fastq_path
--indices(optional) Sample indices associated with this sample. Comma-separated list of:
  1. index set plate well: SI-3A-A1
  2. index sequences: TCGCCATA,GTATACAC
--lanes(optional) Lanes associated with this sample
--fastqprefix(optional) Sample name as specified in the sample sheet supplied to bcl2fastq. See Demultiplexing with bcl2fastq for more information.
--cells(optional) Expected number of recovered cells
--localcoresRestricts cellranger run to use specified number of cores to execute pipeline stages. By default, cellranger will use all of the cores available on your system.
--localmemRestricts cellranger run to use specified amount of memory (in GB) to execute pipeline stages. By default, cellranger will use 90% of the memory available on your system. Please note that cellranger requires at least 16 GB of memory to run all pipeline stages.

After determining these input arguments, run cellranger:

$ cd /home/jdoe/runs
$ cellranger run --id=sample345 \
                 --transcriptome=/opt/refdata-cellranger-1.1.0/hg19 \
                 --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 run pipeline stages will begin to run:

Martian Runtime - 2.0.0
 
Running preflight checks (please wait)...
2016-02-22 14:23:52 [runtime] (ready)           ID.sample345.CELLRANGER_CS.CELLRANGER.SETUP_CHUNKS
2016-02-22 14:23:55 [runtime] (split_complete)  ID.sample345.CELLRANGER_CS.CELLRANGER.SETUP_CHUNKS
2016-02-22 14:23:55 [runtime] (run:local)       ID.sample345.CELLRANGER_CS.CELLRANGER.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 run should conclude with a message similar to this:

2016-02-23 12:10:09 [runtime] (join_complete)   ID.sample345.CELLRANGER_CS.CELLRANGER.SUMMARIZE_REPORTS
 
Outputs:
- Genome-aligned BAM: /opt/sample345/outs/possorted_genome_bam.bam
- Genome-aligned BAM index: /opt/sample345/outs/possorted_genome_bam_index.bam.bai
- Run summary HTML: /opt/sample345/outs/web_summary.html
- Run summary CSV: /opt/sample345/outs/metrics_summary.csv
- Unfiltered gene-barcode matrices: /opt/sample345/outs/raw_gene_bc_matrices
- Filtered gene-barcode matrices: /opt/sample345/outs/filtered_gene_bc_matrices
- Matrix analysis: /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:

File NameDescription
possorted_genome_bam.bamReads aligned to the genome and transcriptome annotated with barcode information
possorted_genome_bam_index.bam.baiIndex for possorted_genome_bam.bam
web_summary.htmlRun summary metrics and charts in HTML format
metrics_summary.csvRun summary metrics in CSV format
raw_gene_bc_matricesUnfiltered gene-barcode matrices containing all barcodes in MEX format
filtered_gene_bc_matricesFiltered gene-barcode matrices containing only cellular barcodes in MEX format
analysisInitial secondary analysis data including dimensionality reduction, cell clustering, and differential expression
molecule_info.h5Per-read molecule information used for downsampling

Once cellranger 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.