This page describes the output file structure from the cellranger multi subcommand specifically for Fixed RNA Profiling (FRP) data.

Upon completion, the cellranger multi subcommand will produce an outs/ directory with the following structure:

Using the tree Linux command, the file structure looks like this:

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├── config.csv
├── multi
│   ├── count
│   └── multiplexing_analysis
└── per_sample_outs
    ├── Sample1
    └── Sample2

The first section of the outputs contains the config.csv file, a duplicate of the input config CSV file. The files in the multi/ folder are generic to the entire FRP experiment, while the files in the per_sample_outs/ directory have been demultiplexed to single samples.

Within the multi/ directory, there are count and multiplexing_analysis directories. The multiplexing_analysis sub-directory is only generated in the context of multiplex Fixed RNA Profiling inputs.

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└─ multi
    ├── count
    │   ├── raw_cloupe.cloupe
    │   ├── raw_feature_bc_matrix
    │   │   ├── barcodes.tsv.gz
    │   │   ├── features.tsv.gz
    │   │   └── matrix.mtx.gz
    │   ├── raw_feature_bc_matrix.h5
    │   ├── raw_molecule_info.h5
    │   ├── raw_probe_bc_matrix.h5
    │   ├── unassigned_alignments.bam
    │   └── unassigned_alignments.bam.bai
    └── multiplexing_analysis
        ├── cells_per_tag.json
        └── frp_gem_barcode_overlap.csv

The count directory contains raw files that include cell-associated and background data from all samples within an experiment:

The multiplexing_analysis directory contains:

The cells_per_tag.json file looks like this, for each Probe Barcode (e.g., BC001) the cell-associated barcodes (e.g., "AACAAGCTCCCTCAAAACTTTAGG-1", etc.) are listed below it:

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{
    "BC001":[
        "AACAAGCTCCCTCAAAACTTTAGG-1",
        "AACATAGTCCCATAGCACTTTAGG-1",
        "AACCAGGTCATGGTCCACTTTAGG-1",
...
    "BC002":[
        "AAACTGTCAGGAGCAAAACGGGAA-1",
        "AAAGGGATCTAATCGTAACGGGAA-1",
        "AACCAAATCGGTCAAGAACGGGAA-1",
...
}

The frp_gem_barcode_overlap.csv file can be used to troubleshoot scenarios where, for example, two different Probe Barcodes were accidentally added to the same hybridization reaction. For multiplexed GEX + Antibody Capture experiments, overlap between incorrect BC+AB barcode pairs may result from contamination or using the same Probe Barcode for two antibody panels. An alert in the web summary will be triggered if the overlap coefficient is ≥ 60%. Please contact [email protected] if you have questions.

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barcode1_id,barcode2_id,barcode1_gems,barcode2_gems,common_gems,overlap
BC009,BC010,2333,2608,81,0.034719245606515216
[ … ]
BC012,AB011,2461,4419,188,0.07639171068671272

Column descriptions:

• barcode1_id: First barcode identifier
• barcode2_id: Second barcode identifier
• barcode1_gems: Number of 10x GEM Barcodes for barcode1
• barcode2_gems: Number of 10x GEM Barcodes for barcode2
• common_gems: Number of 10x GEM Barcodes in common
• overlap: The overlap coefficient of these two barcodes (either pair of Probe Barcodes (BC), Probe Barcode (BC) and Antibody Multiplexing Barcode (AB), or AB-AB for Antibody Capture only analysis).

Overlap coefficient = $\frac{{\text{Common GEMS}}} {\text{min(Barcode 1 GEMS, Barcode 2 GEMS)}}$

The per_sample_outs directory contains sample-level files with any data associated with a valid Probe Barcode that could be assigned to a sample, including both cells and background.

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├── count
│   ├── analysis
│   │   ├── clustering
│   │   ├── diffexp
│   │   ├── pca
│   │   ├── tsne
│   │   └── umap
│   ├── probe_set.csv
│   ├── sample_cloupe.cloupe
│   ├── sample_alignments.bam
│   ├── sample_alignments.bam.bai
│   ├── sample_filtered_barcodes.csv
│   ├── sample_filtered_feature_bc_matrix
│   │   ├── barcodes.tsv.gz
│   │   ├── features.tsv.gz
│   │   └── matrix.mtx.gz
│   ├── sample_filtered_feature_bc_matrix.h5
│   ├── sample_raw_feature_bc_matrix
│   │   ├── barcodes.tsv.gz
│   │   ├── features.tsv.gz
│   │   └── matrix.mtx.gz
│   ├── sample_raw_feature_bc_matrix.h5
│   ├── sample_molecule_info.h5
|   └── sample_raw_probe_bc_matrix.h5
├── metrics_summary.csv
└── web_summary.html

The per_sample_outs directory contains:

The count directory contains:

Upon completion, the cellranger multi subcommand will produce an outs/ directory with the following structure:

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├── config.csv
├── multi
│   └── count
└── per_sample_outs
    └── Sample1

Within the multi/ directory, there is a count/ directory. This directory contains the same files as described above for the multiplex Fixed RNA Profiling count/ directory. There is no multiplexing_analysis/ directory for singleplex experiments.

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└─ multi
   └── count
   ├── raw_cloupe.cloupe
   ├── raw_feature_bc_matrix
   │   ├── barcodes.tsv.gz
   │   ├── features.tsv.gz
   │   └── matrix.mtx.gz
   ├── raw_feature_bc_matrix.h5
   ├── raw_molecule_info.h5
   ├── raw_probe_bc_matrix.h5
   ├── unassigned_alignments.bam
  └── unassigned_alignments.bam.bai

The per_sample_outs directory contains data associated with all valid barcodes in the singleplex Fixed RNA Profiling library. This directory contains the same files as described above for the multiplex Fixed RNA Profiling per_sample_outs directory, except for the sample_raw_feature_bc_matrix/ and sample_raw_feature_bc_matrix.h5 files. The web_summary.html is described on the multi web summary page.

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├── count
│   ├── analysis
│   │   ├── clustering
│   │   ├── diffexp
│   │   ├── pca
│   │   ├── tsne
│   │   └── umap
│   ├── probe_set.csv
│   ├── sample_cloupe.cloupe
│   ├── sample_alignments.bam
│   ├── sample_alignments.bam.bai
│   ├── sample_filtered_barcodes.csv
│   ├── sample_filtered_feature_bc_matrix
│   │   ├── barcodes.tsv.gz
│   │   ├── features.tsv.gz
│   │   └── matrix.mtx.gz
│   ├── sample_filtered_feature_bc_matrix.h5
│   ├── sample_molecule_info.h5
│   └── sample_raw_probe_bc_matrix.h5
├── metrics_summary.csv
└── web_summary.html

The Fixed RNA Profiling analyses with an Antibody Capture library and/or CRISPR Guide Capture library have all the same outputs described in the per_sample_outs/count directory of singleplex and multiplex GEX analyses. In addition, they contain a feature_reference.csv file, which is a duplicate of the input Feature Reference CSV file.

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├── count
│   ├── analysis
│   │   ├── clustering
│   │   ├── diffexp
│   │   ├── pca
│   │   ├── tsne
│   │   └── umap
│   ├── aggregate_barcodes.csv
│   ├── feature_reference.csv
│   ├── ...

The /multi/count directory also contains a copy of the feature_reference.csv file:

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└─ multi
├── count
│   ├── raw_cloupe.cloupe
│   ├── raw_feature_bc_matrix
│   │   ├── barcodes.tsv.gz
│   │   ├── features.tsv.gz
│   │   └── matrix.mtx.gz
│   ├── feature_reference.csv
│   ├── raw_feature_bc_matrix.h5
│   ├── raw_molecule_info.h5
│   ├── unassigned_alignments.bam
│   └── unassigned_alignments.bam.bai

The web_summary.html is described on the multi web summary page.