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

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

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$ cd /home/jdoe/runs/sample345/outs
$ tree filtered_feature_bc_matrix

filtered_feature_bc_matrix
├── barcodes.tsv.gz
├── features.tsv.gz
└── matrix.mtx.gz

0 directories, 3 files

Features correspond to row indices. For each matrix, the components of features.tsv.gz are:

• Column 1: feature ID
• Column 2: feature name
• Column 3: type of feature i.e. Gene Expression or Antibody Capture

Below is a minimal example features.tsv.gz file showing data collected for three genes and antibodies.

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$ gzip -cd filtered_feature_bc_matrix/features.tsv.gz

ENSG00000187634	SAMD11	Gene Expression
ENSG00000188976	NOC2L	Gene Expression
ENSG00000187961	KLHL17	Gene Expression

For Gene Expression (GEX) data, the ID corresponds to gene_id in the annotation field of the reference GTF. Correspondingly, 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 Protein Expression (PEX) data, the feature 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.

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$ gzip -cd  filtered_feature_bc_matrices/barcodes.tsv  | head -10

AACACTTGGCAAGGAA-1
AACAGGATTCATAGTT-1
AACAGGTTATTGCACC-1
AACAGGTTCACCGAAG-1
AACAGTCAGGCTCCGC-1
AACAGTCCACGCGGTG-1
AACATAGTCTATCTAC-1
AACATCTTAAGGCTCA-1
AACCAATCTGGTTGGC-1
AACCACTGCCATAGCC-1

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

AACACTTGGCAAGGAA-1

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.

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.

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# load package
 library(Matrix)

# set the different file paths of the filtered matrix
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")

# load the matrix.mtx.gz
mat_filtered <- readMM(file = matrix.path)

# load the feature.tsv.gz
feature.names = read.delim(features.path,
                           header =  FALSE,
                           stringsAsFactors =  FALSE)

# load the barcodes.tsv.gz
barcode.names = read.delim(barcode.path,
                           header =  FALSE,
                           stringsAsFactors =  FALSE)

# set the matrix column and row names
colnames(mat_filtered) = barcode.names$V1
rownames(mat_filtered) = feature.names$V1

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

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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_filtered = scipy.io.mmread(os.path.join(matrix_dir, "matrix.mtx.gz"))

# list of transcript ids, e.g. 'ENSG00000187634'
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. 'SAMD11'
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")]

# list of barcodes, e.g. 'AAACATACAAAACG-1'
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")]

Space 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 format:

Method 1: Python

Follow the steps in the Loading matrices into Python section to get the MEX data into a matrix format. To view the matrix as a data table and save as a CSV file, convert the matrix into a pandas dataframe with the following code:

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import pandas as pd

# transform table to pandas dataframe and label rows and columns

matrix = pd.DataFrame.sparse.from_spmatrix(mat_filtered)
matrix.columns = barcodes
matrix.insert(loc=0, column="feature_id", value=feature_ids)
matrix.insert(loc=1, column="gene", value=gene_names)
matrix.insert(loc=2, column="feature_type", value=feature_types)

# display matrix

print(matrix)

# save the table as a CSV (note the CSV will be a very large file)

matrix.to_csv("mex_matrix_filtered.csv", index=False)

The output should look similar to

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feature_id         gene     feature_type  AAACAACGAATAGTTC-1  ...

0 ENSG00000243485 MIR1302-2HG Gene Expression 0
1 ENSG00000237613 FAM138A Gene Expression 0
2 ENSG00000186092 OR4F5 Gene Expression 0
3 ENSG00000238009 AL627309.1 Gene Expression 0
4 ENSG00000239945 AL627309.3 Gene Expression 0
...

Method 2: mat2csv

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

This command takes two arguments - an input matrix generated by Space 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:

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# convert from MEX

$ spaceranger mat2csv sample123/outs/filtered_feature_bc_matrix sample123.csv

# or, convert from HDF5

$ spaceranger 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).