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

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

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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.

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

• library_id: Unique identifier for this input GEM well. This will be used for labeling purposes only; it does not need to match any previous ID assigned to the GEM well.
• atac_fragments: Path to the atac_fragments.tsv.gz file produced by cellranger-arc count. For example, if you processed your GEM well by calling cellranger-arc count --id=ID in some directory /DIR, the atac_fragments would be /DIR/ID/outs/atac_fragments.tsv.gz. Note, we assume that the index for the atac_fragments file is located alongside at /DIR/ID/outs/atac_fragments.tsv.gz.tbi.
• per_barcode_metrics: Path to the per_barcode_metrics.csv file produced by cellranger-arc count. This file is used to identify the barcodes that are to be considered as cells for secondary analysis.
• gex_molecule_info: Path to the gex_molecule_info.h5 file produced by cellranger-arc count.
• (Optional) Additional custom columns containing library meta-data (e.g., lab or sample origin). These custom library annotations do not affect the analysis pipeline but can be visualized downstream in the Loupe Browser. Note that unlike other CSV inputs to Cell Ranger ARC, these custom columns may contain characters outside the ASCII range (e.g., non-Latin characters).

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:

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

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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

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

Additional optional parameters are available:

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

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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.

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:

• none: Do not normalize at all.

• depth (default): Subsample reads from higher-depth GEM wells until we equalize the

  • median number of unique fragments per cell for each ATAC library
  • mean number of reads that are confidently mapped to the transcriptome per cell for each gene expression library

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:

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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.

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.