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10x Genomics
Chromium Single Cell Gene Expression

Aggregating Multiple GEM Wells with cellranger aggr

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

The cellranger aggr command takes a CSV file specifying a list of cellranger count output files (specifically the molecule_info.h5 from each run), 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, measured in terms of reads that are confidently mapped to the transcriptome or assigned to the feature IDs per cell. However, it is possible to change the depth normalization mode (see Depth Normalization).


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

If Feature Barcode analysis is included, then the feature reference CSV file provided to cellranger count should be the same for each GEM well. Targeted Gene Expression data is supported by cellranger aggr and can be aggregated with whole transcriptome Gene Expression data, provided that all GEM wells have matching chemistries and that the same target panel CSV file is used for all targeted samples.

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

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

Now you can aggregate these three runs to get a single feature-barcode 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 would look like this:


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


In addition to the CSV columns expected by cellranger aggr, you may optionally supply additional 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 (see below). Note that unlike other CSV inputs to Cell Ranger, these custom columns may contain characters outside the ASCII range (e.g., non-Latin characters).

Creating Categories

When combining multiple samples into a single dataset with the cellranger aggr pipeline, you can assign categories and values to individual samples by adding columns to the cellranger aggr input spreadsheet. These category assignments propagate into Loupe Browser, where you can view them, and determine genes that drive differences within samples. For example, the following spreadsheet was used to aggregate the tutorial dataset:


Any columns in addition to 'library_id' and 'molecule_h5' will be converted into categories, and the cells in each sample will be assigned to one of the values in that category.

Aggregating Libraries With Different Chemistry Versions

If you are aggregating libraries generated by different chemistry versions of the Single Cell Gene Expression Reagents, you might observe systematic differences in gene expression profiles between libraries. The cellranger aggr pipeline incorporates batch effect correction (algorithm details) to overcome this. To enable this module, you should include the following column in your aggregation CSV file:

For example, if the LV123 sample in the previous example is a v2 library, and the LB456 and LP789 samples are v3 libraries, you would set up the aggregation CSV file like this:


The v2_lib and v3_lib identifiers are just example identifiers. Every sample from a given batch has to have the same batch identifier, but otherwise the identifier itself is arbitrary.

About Chemistry Batch Correction

Aggregating 5' and 3' Gene Expression Data

The cellranger aggr pipeline uses Chemistry Batch Correction when aggregating results from a combination of 5' and 3', or 3' v2 and 3' v3 Gene Expression data. Enabling Chemistry Batch Correction in this scenario improves the mixing of the batches in the t-SNE visualization and clustering results. Therefore we recommend using Chemistry Batch correction. However, residual batch effects may still be present, and we advise careful validation of the results. In particular for the V(D)J genes, the 5' assay will generally count the V gene segments of the immune receptor (e.g. TRBV12-1 or IGH4-2), while the 3' assay will count the C gene segments (e.g. TRBC or IGHA), which may pose additional analysis challenges.

Aggregating Targeted Gene Expression Data

The cellranger aggr pipeline can aggregate results that include Targeted Gene Expression analysis provided that the requirements above are met. Secondary analysis for all samples is done with the non-targeted genes excluded from the feature-barcode matrices. Aggregated feature-barcode matrices follow the same convention as Targeted Gene Expression analysis: the filtered feature-barcode matrices do not include non-targeted genes, whereas the raw feature-barcode matrices still include all genes.

Command Line Interface

These are the most common command line arguments (run cellranger aggr --help for a full list):

--id=IDA unique run ID string: e.g. AGG123
--csv=CSVPath of a CSV file containing a list of cellranger count outputs (see Setting up a CSV).
--normalize=MODE(Optional) String specifying how to normalize depth across the input libraries. Valid values: mapped (default), or none (see Depth Normalization).
--nosecondary(Optional) Add this flag to skip secondary analysis which includes dimensionality reduction, clustering and visualization. This is applicable if you plan to use cellranger reanalyze or your own custom analysis.

After specifying these input arguments, run cellranger aggr:

$ cd /home/jdoe/runs
$ cellranger aggr --id=AGG123 \
                  --csv=AGG123_libraries.csv \

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

Pipeline Outputs

The cellranger 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 aggr follows the format described in the Understanding Output section of the documentation, but includes the union of all the relevant barcodes from each input jobs.

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

- Aggregation metrics summary HTML:         /home/jdoe/runs/AGG123/outs/web_summary.html
- Aggregation metrics summary JSON:         /home/jdoe/runs/AGG123/outs/summary.json
- Secondary analysis output CSV:            /home/jdoe/runs/AGG123/outs/analysis
- Filtered feature-barcode matrices MEX:    /home/jdoe/runs/AGG123/outs/filtered_feature_bc_matrix
- Filtered feature-barcode matrices HDF5:   /home/jdoe/runs/AGG123/outs/filtered_feature_bc_matrix.h5
- Unfiltered feature-barcode matrices MEX:  /home/jdoe/runs/AGG123/outs/raw_feature_bc_matrix
- Unfiltered feature-barcode matrices HDF5: /home/jdoe/runs/AGG123/outs/raw_feature_bc_matrix.h5
- Copy of the input aggregation CSV:        /home/jdoe/runs/AGG123/outs/aggregation.csv
- Loupe Browser file:                       /home/jdoe/runs/AGG123/outs/cloupe.cloupe
Pipestance completed successfully!

Once cellranger 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 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 tells us which GEM well this 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.

Depth Normalization

When combining data from multiple GEM wells, the cellranger aggr pipeline automatically equalizes the average read depth per cell between groups before merging. This approach avoids artifacts that may be introduced due to differences in sequencing depth. 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 in a downstream step.

There are two normalization modes: