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Cell Ranger


10x Genomics
Chromium Single Cell Gene Expression

Feature-Barcode Matrices

The cellranger pipeline outputs unfiltered (raw) and filtered feature-barcode matrices in two file formats: the Market Exchange Format (MEX), which is described on this page, and Hierarchical Data Format (HDF5), which is described in detail here.

Each element of the feature-barcode matrix is the number of UMIs associated with a feature (row) and a barcode (column):

Type Description
Unfiltered feature-barcode matrixContains every barcode from the fixed list of known-good barcode sequences that has at least one read. This includes background and cell-associated barcodes.
count: outs/raw_feature_bc_matrix/
multi: outs/multi/count/raw_feature_bc_matrix/
Filtered feature-barcode matrixContains only detected cell-associated barcodes. For Targeted Gene Expression samples, non-targeted genes are removed from the filtered matrix.
count: outs/filtered_feature_bc_matrix/
multi: outs/per_sample_outs/count/sample_filtered_feature_bc_matrix/

For sparse matrices, the matrix is stored in the Market Exchange Format (MEX). It contains gzipped TSV files with feature and barcode sequences corresponding to row and column indices respectively. For example, the matrices output may look like:

$ cd /home/jdoe/runs/sample345/outs
$ tree filtered_feature_bc_matrix
├── barcodes.tsv.gz
├── features.tsv.gz
└── matrix.mtx.gz
0 directories, 3 files

Features correspond to row indices. For each feature, the feature ID and name are stored in the first and second column of the (unzipped) features.tsv.gz file, respectively. The third column identifies the type of feature, which will be one of Gene Expression, Antibody Capture, CRISPR Guide Capture, Multiplexing Capture, or CUSTOM, depending on the feature type. Below is a minimal example features.tsv.gz file showing data collected for three genes and two antibodies.

$ gzip -cd filtered_feature_bc_matrix/features.tsv.gz
ENSG00000141510       TP53         Gene Expression
ENSG00000012048       BRCA1        Gene Expression
ENSG00000139687       RB1          Gene Expression
CD3_GCCTGACTAGATCCA   CD3          Antibody Capture
CD19_CGTGCAACACTCGTA  CD19         Antibody Capture

For Gene Expression data, the ID corresponds to gene_id in the annotation field of the reference GTF. Similarly, the name corresponds to gene_name in the annotation field of the reference GTF. If no gene_name field is present in the reference GTF, gene name is equivalent to gene ID. Similarly, for Antibody Capture and CRISPR Guide Capture data, the id and name are taken from the first two columns of the Feature Reference CSV file.

For multi-species experiments, gene IDs and names are prefixed with the genome name to avoid name collisions between genes of different species e.g. GAPDH becomes hg19_GAPDH and Gm15816 becomes mm10_Gm15816.

Barcode sequences correspond to column indices:

$ gzip -cd filtered_feature_bc_matrices/barcodes.tsv.gz

Each barcode sequence includes a suffix with a dash separator followed by a number:


More details on the barcode sequence format are available in the barcoded BAM section.

R and Python support the MEX format, and sparse matrices can be used for more efficient manipulation, as described below:

Loading matrices into R

The R package Matrix supports loading MEX format data, and can be easily used to load the sparse feature-barcode matrix, as shown in the example code below (edit file path to your matrix directory).

matrix_dir = "/opt/sample345/outs/filtered_feature_bc_matrix/"
barcode.path <- paste0(matrix_dir, "barcodes.tsv.gz")
features.path <- paste0(matrix_dir, "features.tsv.gz")
matrix.path <- paste0(matrix_dir, "matrix.mtx.gz")
mat <- readMM(file = matrix.path)
feature.names = read.delim(features.path,
                           header = FALSE,
                           stringsAsFactors = FALSE)
barcode.names = read.delim(barcode.path,
                           header = FALSE,
                           stringsAsFactors = FALSE)
colnames(mat) = barcode.names$V1
rownames(mat) = feature.names$V1

Converting matrix files to CSV format

Cell Ranger represents the feature-barcode matrix using sparse formats (only the nonzero entries are stored) in order to minimize file size. All of our programs, and many other programs for gene expression analysis, support sparse formats.

However, certain programs (e.g. Excel) only support dense formats (where every row-column entry is explicitly stored, even if it's a zero). Here are a few methods for converting feature-barcode matrices to CSV:

Method 1: Load matrices into Python

The csv, os, gzip and scipy.io modules can be used to load a feature-barcode matrix into Python as shown below (edit file path to your matrix directory).

import csv
import gzip
import os
import scipy.io
# define MEX directory
matrix_dir = "/opt/sample345/outs/filtered_feature_bc_matrix"
# read in MEX format matrix as table
mat = scipy.io.mmread(os.path.join(matrix_dir, "matrix.mtx.gz"))
# list of transcript ids, e.g. 'ENSG00000243485'
features_path = os.path.join(matrix_dir, "features.tsv.gz")
feature_ids = [row[0] for row in csv.reader(gzip.open(features_path, mode="rt"), delimiter="\t")]
# list of gene names, e.g. 'MIR1302-2HG'
gene_names = [row[1] for row in csv.reader(gzip.open(features_path, mode="rt"), delimiter="\t")]
# list of feature_types, e.g. 'Gene Expression'
feature_types = [row[2] for row in csv.reader(gzip.open(features_path, mode="rt"), delimiter="\t")]
barcodes_path = os.path.join(matrix_dir, "barcodes.tsv.gz")
barcodes = [row[0] for row in csv.reader(gzip.open(barcodes_path, mode="rt"), delimiter="\t")]

To view the matrix as a data table and save as a CSV file with Python, we can convert it into a pandas dataframe with the following code:

import pandas as pd
# transform table to pandas dataframe and label rows and columns
matrix = pd.DataFrame.sparse.from_spmatrix(mat)
matrix.columns = barcodes
matrix.insert(loc=0, column="feature_id", value=feature_ids)
matrix.insert(loc=0, column="gene", value=gene_names)
matrix.insert(loc=0, column="feature_type", value=feature_types)
# display matrix
# save the table as a CSV (note the CSV will be a very large file)
matrix.to_csv("mex_matrix.csv", index=False)

The output should look similar to:

feature_type     gene         feature_id      AAACCCAAGGAGAGTA-1  AAACGCTTCAGCCCAG-1  ...
Gene Expression  MIR1302-2HG  ENSG00000243485 0                   0
Gene Expression      FAM138A  ENSG00000237613 0                   0
Gene Expression        OR4F5  ENSG00000186092 0                   0
Gene Expression   AL627309.1  ENSG00000238009 0                   0
Gene Expression   AL627309.3  ENSG00000239945 0                   0

Method 2: mat2csv

You can convert a feature-barcode matrix to dense CSV format using the cellranger mat2csv command.

This command takes two arguments - an input matrix generated by Cell Ranger (either an HDF5 file or a MEX directory), and an output path for the dense CSV. For example, to convert a matrix from a pipestance named sample123 in the current directory, either of the following commands would work:

# convert from MEX
$ cellranger mat2csv sample123/outs/filtered_feature_bc_matrix sample123.csv
# or, convert from HDF5
$ cellranger mat2csv sample123/outs/filtered_feature_bc_matrix.h5 sample123.csv

You can then load sample123.csv into Excel.

Method 3: Shell commands

Please see this Q&A article for shell commands to convert MEX files to CSV. This method creates a single file that is sparse (zeroes are ignored).