HOME  ›   pipelines
If your question is not answered here, please email us at:  ${email.software}

10x Genomics
Chromium Single Cell Multiome ATAC + Gene Exp.

Aggregating Multiple GEM Wells with cellranger-arc aggr

Table of Contents

When conducting large studies involving multiple GEM wells, run cellranger-arc count on FASTQ data from each of the GEM wells individually, then pool the results using cellranger-arc aggr, as described here.

The cellranger-arc aggr command takes as input a CSV file specifying a list of cellranger-arc count output files for each GEM well being aggregated and produces a single feature-barcode matrix containing all the data.

When combining multiple GEM wells, the barcode sequences for each channel are distinguished by a GEM well suffix appended to the barcode sequence (see GEM wells).

By default, the reads from each GEM well are subsampled such that all GEM wells have the same effective sequencing depth for both ATAC and gene expression modalities; for the ATAC data it is measured in terms of median unique fragments per cell and for gene expression it is measured in terms of the average number of reads that are confidently mapped to the transcriptome per cell. However, it is possible to turn off this normalization altogether (see Depth Normalization).

Requirements

The first step is to run a single instance of cellranger-arc count on each individual GEM well prepared using the 10x Chromiumâ„¢ platform, as described in Single-GEM Well Analysis.

For example, suppose you ran three count pipelines as follows:

$ cd /opt/runs
$ cellranger-arc count --id=LV123 ...
... wait for pipeline to finish ...
$ cellranger-arc count --id=LB456 ...
... wait for pipeline to finish ...
$ cellranger-arc count --id=LP789 ...
... wait for pipeline to finish ...

You can aggregate these three runs to get an aggregated matrix and analysis. In order to do so, you need to create an Aggregation CSV.

Setting up an Aggregation CSV

Create a CSV file with a header line containing the following columns:

You can either make the CSV file in a text editor, or create it in Excel and export to CSV. Continuing the example from the previous section, your Excel spreadsheet should look like this:

ABCD
1library_idatac_fragmentsper_barcode_metricsgex_molecule_info
2LV123/opt/runs/LV123/outs/atac_fragments.tsv.gz/opt/runs/LV123/outs/per_barcode_metrics.csv/opt/runs/LV123/outs/gex_molecule_info.h5
3LB456/opt/runs/LB456/outs/atac_fragments.tsv.gz/opt/runs/LB456/outs/per_barcode_metrics.csv/opt/runs/LB456/outs/gex_molecule_info.h5
4LP789/opt/runs/LP789/outs/atac_fragments.tsv.gz/opt/runs/LP789/outs/per_barcode_metrics.csv/opt/runs/LP789/outs/gex_molecule_info.h5

When you save it as a CSV, the result looks like this:

library_id,atac_fragments,per_barcode_metrics,gex_molecule_info
LV123,/opt/runs/LV123/outs/atac_fragments.tsv.gz,/opt/runs/LV123/outs/per_barcode_metrics.csv,/opt/runs/LV123/outs/gex_molecule_info.h5
LB456,/opt/runs/LB456/outs/atac_fragments.tsv.gz,/opt/runs/LB456/outs/per_barcode_metrics.csv,/opt/runs/LB456/outs/gex_molecule_info.h5
LP789,/opt/runs/LP789/outs/atac_fragments.tsv.gz,/opt/runs/LP789/outs/per_barcode_metrics.csv,/opt/runs/LP789/outs/gex_molecule_info.h5

Command line interface

These are the required command line arguments (also available through cellranger-arc aggr --help):

ArgumentDescription
--id=IDA unique run id and output folder name [a-zA-Z0-9_-]+ of maximum length 64 characters.
--csv=CSVPath to CSV file enumerating cellranger-arc count outputs (see Setting up a CSV).
--reference=PATHPath to folder containing cellranger-arc-compatible reference. Reference packages can be downloaded from support.10xgenomics.com or constructed using the `cellranger-arc mkref` command. Note this reference must match the reference used for the initial `cellranger-arc count` run.

Additional optional parameters are available:

OptionDescription
--description=TEXTSample description to embed in output files [default: ]
--peaks=BEDOverride peak caller: specify peaks to use in downstream analyses from supplied 3-column BED file. The supplied peaks file must be sorted by position and not contain overlapping peaks; comment lines beginning with # are allowed
--normalize=MODELibrary depth normalization mode [default: depth] [possible values: none, depth]
--jobmode=MODEJob manager to use. Valid options: local (default), sge, lsf, slurm or path to a .template file. Search for help on "Cluster Mode" at support.10xgenomics.com for more details on configuring the pipeline to use a compute cluster [default: local]
--localcores=NUMSet max cores the pipeline may request at one time. Only applies to local jobs
--localmem=NUMSet max memory (GB) the pipeline may request at one time. Only applies to local jobs
--localvmem=NUMSet max virtual address space in GB for the pipeline. Only applies to local jobs
--mempercore=NUMReserve enough threads for each job to ensure enough memory will be available, assuming each core on your cluster has at least this much memory available. Only applies to cluster jobmodes
--maxjobsSet max jobs submitted to cluster at one time. Only applies to cluster jobmodes
--jobintervalSet delay between submitting jobs to cluster, in ms. Only applies to cluster jobmodes
--overrides=PATHThe path to a JSON file that specifies stage-level overrides for cores and memory. Finer-grained than --localcores, --mempercore and --localmem. Consult https://support.10xgenomics.com/ for an example override file
--uiport=PORTServe web UI at http://localhost:PORT

After specifying input arguments and options, run cellranger-arc aggr:

$ cd /home/jdoe/runs
$ cellranger-arc aggr --id=AGG123 \
                  --csv=AGG123_libraries.csv \
                  --normalize=depth \
                  --reference=/home/jdoe/refs/hg19

The pipeline will begin to run, creating a new folder named with the aggregation ID specified with the --id argument (e.g. /home/jdoe/runs/AGG123). If this output folder already exists, cellranger-arc will assume it is an existing pipestance and attempt to resume running it.

Depth normalization

When combining data from multiple GEM wells, the cellranger-arc aggr pipeline automatically equalizes the average read depth per cell between groups before merging. Note that does this for both assay modalities - ATAC and gene expression. When libraries are sequenced to very different read depth per cell you may observe that cells cluster by library of origin rather than cell type. This is commonly referred to as a batch effect in the literature. A multitude of factors can cause batch effects in single cell data and sequencing depth is only one of them. The downsampling normalization in cellranger-arc aggr specifically addresses sequencing depth batch effects but not others. It is possible to turn off normalization or change the way normalization is done. The none option may be appropriate if you want to maximize sensitivity and plan to deal with depth normalization or more general batch correction in a downstream step.

There are two normalization modes:

Pipeline outputs

The cellranger-arc aggr pipeline generates output files that contain all of the data from the individual input jobs, aggregated into single output files, for convenient multi-sample analysis. The GEM well suffix of each barcode is updated to prevent barcode collisions, as described below.

Each output file produced by cellranger-arc aggr follows the format described in the Understanding Output section, but includes the union of all the relevant barcodes from each input job.

A successful run will conclude with a message like this:

2021-04-26 05:16:01 [runtime] (update)          ID.AGG123.SC_ATAC_GEX_AGGREGATOR_CS.ATAC_GEX_CLOUPE_PREPROCESS.fork0 join_running
2021-04-26 05:20:28 [runtime] (join_complete)   ID.AGG123.SC_ATAC_GEX_AGGREGATOR_CS.ATAC_GEX_CLOUPE_PREPROCESS
 
Outputs:
- Barcoded and aligned fragment file:           /home/jdoe/runs/AGG123/outs/atac_fragments.tsv.gz
- Fragment file index:                          /home/jdoe/runs/AGG123/outs/atac_fragments.tsv.gz.tbi
- Bed file of all called peak locations:        /home/jdoe/runs/AGG123/outs/atac_peaks.bed
- Filtered peak barcode matrix in hdf5 format:  /home/jdoe/runs/AGG123/outs/raw_feature_bc_matrix.h5
- Filtered peak barcode matrix in mex format:   /home/jdoe/runs/AGG123/outs/raw_feature_bc_matrix
- Filtered peak barcode matrix in hdf5 format:  /home/jdoe/runs/AGG123/outs/filtered_feature_bc_matrix.h5
- Filtered peak barcode matrix in mex format:   /home/jdoe/runs/AGG123/outs/filtered_feature_bc_matrix
- Secondary analysis outputs:
    clustering:
      atac: {
        ...
      }
      gex:  {
        ...
      }
    dimensionality_reduction:
      atac: {
        ...
      }
      gex:  {
        ...
      }
    feature_linkage:
      ...
    tf_analysis:
      ...
- Loupe Browser input file:                     /home/jdoe/runs/AGG123/outs/cloupe.cloupe
- csv summarizing important metrics and values: /home/jdoe/runs/AGG123/outs/summary.csv
- Annotation of peaks with genes:               /home/jdoe/runs/AGG123/outs/atac_peak_annotation.tsv
- HTML summary:                                 /home/jdoe/runs/AGG123/outs/web_summary.html
- Input data supplied for aggregation:          [
    {
        "atac_fragments": "/home/jdoe/runs/LV123/outs/atac_fragments.tsv.gz",
        "gex_molecule_info": "/home/jdoe/runs/LV123/outs/gex_molecule_info.h5",
        "library_id": "LV123",
        "metadata": {},
        "per_barcode_metrics": "/home/jdoe/runs/LV123/outs/per_barcode_metrics.csv"
    },
    {
        "atac_fragments": "/home/jdoe/runs/LB456/outs/atac_fragments.tsv.gz",
        "gex_molecule_info": "/home/jdoe/runs/LB456/outs/gex_molecule_info.h5",
        "library_id": "LB456",
        "metadata": {},
        "per_barcode_metrics": "/home/jdoe/runs/LB456/outs/per_barcode_metrics.csv"
    },
    {
        "atac_fragments": "/home/jdoe/runs/LP789/outs/atac_fragments.tsv.gz",
        "gex_molecule_info": "/home/jdoe/runs/LP789/outs/gex_molecule_info.h5",
        "library_id": "LP789",
        "metadata": {},
        "per_barcode_metrics": "/home/jdoe/runs/LP789/outs/per_barcode_metrics.csv"
    }
  ]
- Input data supplied for aggregation as CSV:   /home/jdoe/runs/AGG123/outs/aggr.csv
 
Pipestance completed successfully!

Once cellranger-arc aggr has successfully completed, you can browse the resulting summary HTML file in any supported web browser, open the .cloupe file in Loupe Browser, or refer to the Understanding Output section to explore the data by hand. For machine-readable versions of the summary metrics, refer to the cellranger-arc aggr section of the Summary Metrics page.

Understanding GEM wells

Each GEM well is a physically distinct set of GEM partitions, but draws barcode sequences randomly from the pool of valid barcodes, known as the barcode whitelist. To keep the barcodes unique when aggregating multiple libraries, we append a small integer identifying the GEM well to the barcode nucleotide sequence, and use that nucleotide sequence plus ID as the unique identifier in the feature-barcode matrix. For example, AGACCATTGAGACTTA-1 and AGACCATTGAGACTTA-2 are distinct cell barcodes from different GEM wells, despite having the same barcode nucleotide sequence.

This number, which indicates which GEM well the barcode sequence came from, is called the GEM well suffix. The numbering of the GEM wells will reflect the order that the GEM wells were provided in the Aggregation CSV.