This page describes the output file structure from the cellranger multi subcommand specifically for 3' Cell Multiplexing data. This subcommand was introduced in Cell Ranger 5.0 for joint analysis of 5' gene expression and V(D)J (GEX + VDJ) data, and in Cell Ranger 6.0 for 3' Cell Multiplexing 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 Cell Multiplexing 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:

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└─ multi
    ├── count
    │   ├── feature_reference.csv
    │   ├── 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
    │   ├── unassigned_alignments.bam
    │   └── unassigned_alignments.bam.bai
    └── multiplexing_analysis
        ├── assignment_confidence_table.csv
        ├── cells_per_tag.json
        ├── tag_calls_per_cell.csv
        └── tag_calls_summary.csv

The count directory contains raw files that include cells and background data:

The multiplexing_analysis directory contains:

The assignment_confidence_table.csv table provides a summary of all the information from the tag-assignment algorithm for each cell-associated barcode, including the probability that a given barcode belongs to a given state. The user may modify the confidence threshold for assigning tags to barcodes on their own in a data-science environment like Python or R to enable further downstream analysis.

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Row,CMO301,CMO302,Barcode,Multiplet,Blank,Assignment,Assignment_Probability,CMO301_cnts,CMO302_cnts
0,0.999721559521299,3.4331367105764475e-10,AAACCCAAGAGTGTGC-1,0.0002784107425317906,2.939285533268778e-08,CMO301,0.999721559521299,4.0744873049856905,2.444044795918076
1,2.1178952222935904e-09,0.9999759641250832,AAACCCAAGATGCTTC-1,2.3239920572975783e-06,2.1709764964310346e-05,CMO302,0.9999759641250832,2.6020599913279625,3.8196755199942927
2,0.999822627035718,3.287157579467574e-07,AAACCCAAGGTACAGC-1,8.871810165359213e-05,8.83261468704939e-05,CMO301,0.999822627035718,3.630834517828051,2.3404441148401185
3,0.8947739014808398,9.424785606470267e-09,AAACCCAAGTAGCTCT-1,0.10522608718358256,1.910792092383626e-09,Unassigned,0.8947739014808398,4.219767844658398,2.9916690073799486
4,0.033490762078236785,0.004751323355950833,AAACCCACACGGATCC-1,0.9617539697731167,3.944792695789878e-06,Multiplet,0.9617539697731167,3.6148972160331345,3.4896772916636984Column descriptions:

Column descriptions:

• CMO301, CMO302, etc.: one column per tag used in the experiment, indicates the probability that a given barcode belongs to each of those singlet states
• Barcodes: the cell-associated barcode
• Multiplet: the coarse-grained multiplet state, which contains the probability that the given barcode is some-type of multiplet. Obtained by summing the probabilities across each possible multiplet state in the experiment
• Blanks: contains the probability that the barcode contains un-tagged cells i.e., is considered to have too few tag counts for every tag
• Assignment: the state assigned by the tag-assignment algorithm for the given barcode. Values: one of the singlet states, Multiplet, Blanks, or Unassigned (if every state has probability less than 90%, which is the default minimum confidence threshold.)
• Assignment_Probability: the probability of the most-likely state
• CMO301_cnts, CMO302_cnts, etc.: (starting from Cell Ranger v7.1) one column per tag used in the experiment, indicates the log-transformed UMI count for each tag

The tag_calls_summary.csv summarizes multiplexing results by providing statistics about categories including the number of cells assigned no tag, one tag, more than one tag, etc. The category No tag assigned includes both cells that were considered Blanks and cells considered Unassigned.

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Category,num_cells,pct_cells,median_umis,stddev_umis
No tag molecules,0,0.0,None,None
No tag assigned,386,2.9,None,None
1 tag assigned,12465,93.6,None,None
More than 1 tag assigned,472,3.5,None,None
CMO301,6437,48.3,3442.0,7988.3
CMO302,6028,45.2,3515.5,5167.2
CMO301|CMO302,472,3.5,12414.0,9696.0

Column descriptions:

• num_cells: number of cells in the category
• pct_cells: percent of cells in the category
• median_umis: median UMI counts for the tag(s), amongst cells assigned those tag(s)
• stddev_umis: standard deviation of the UMI counts for the tag(s), amongst cells assigned those tag(s)

The tag_calls_per_cell.csv file contains tag calls per cell, one line for each barcode. It contains all singlet and multiplet cells; Unassigned or Blanks are not included.

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cell_barcode,num_features,feature_call,num_umis
AAACCCAAGCAACAGC-1,1,CMO301,16778
AAACCCAAGCTCGTGC-1,1,CMO301,1735
AAACCCACATGACTGT-1,1,CMO301,1625
AAACCCAGTCCACAGC-1,1,CMO301,19323
AAACCCAGTCGCGGTT-1,1,CMO301,1678

Column descriptions:

• cell_barcode: cell-barcode
• num_features: number of tags assigned to that cell
• feature_call: names of tag(s) assigned, delimited by "|"
• num_umis: number of molecules (UMIs) for each tag assigned, delimited by "|"

The per_sample_outs directory contains sample-level files with data from cells only (background data filtered out):

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├── count
│   ├── analysis
│   │   ├── clustering
│   │   ├── diffexp
│   │   ├── pca
│   │   ├── tsne
│   │   └── umap
│   ├── sample_cloupe.cloupe
│   ├── feature_reference.csv
│   ├── 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
├── metrics_summary.csv
└── web_summary.html

The per_sample_outs directory contains:

The count directory contains:

For Cell Multiplexing experiments with Antibody Capture libraries, there will be additional outputs. These files are described in the Antibody outputs overview. The output structure will include an additional antibody_analysis directory in the per_sample_outs/count directory:

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├── count
│   ├── analysis
│   ├── sample_cloupe.cloupe
│   ├── antibody_analysis
│   │     └── aggregate_barcodes.csv
│   ├── feature_reference.csv
│   ├── sample_alignments.bam
│   ├── sample_alignments.bam.bai
│   ├── sample_filtered_barcodes.csv
│   ├── sample_filtered_feature_bc_matrix
│   ├── sample_filtered_feature_bc_matrix.h5
│   └── sample_molecule_info.h5
├── metrics_summary.csv
└── web_summary.html

For Cell Multiplexing experiments with CRISPR Guide Capture libraries, there will be additional outputs. These files are described in the CRISPR outputs overview. The output structure will include an additional crispr_analysis directory in the per_sample_outs/count directory:

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├── count
│   ├── analysis
│   ├── sample_cloupe.cloupe
│   ├── crispr_analysis
│   │     ├── cells_per_protospacer.json
│   │     ├── feature_reference.csv
│   │     ├── perturbation_effects_by_feature
│   │     ├── perturbation_effects_by_target
│   │     ├── perturbation_efficiencies_by_feature.csv
│   │     ├── perturbation_efficiencies_by_target.csv
│   │     ├── protospacer_calls_per_cell.csv
│   │     ├── protospacer_calls_summary.csv
│   │     ├── protospacer_umi_thresholds.csv
│   │     └── protospacer_umi_thresholds.json
│   ├── feature_reference.csv
│   ├── sample_alignments.bam
│   ├── sample_alignments.bam.bai
│   ├── sample_filtered_barcodes.csv
│   ├── sample_filtered_feature_bc_matrix
│   ├── sample_filtered_feature_bc_matrix.h5
│   └── sample_molecule_info.h5
├── metrics_summary.csv
└── web_summary.html