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10x Genomics
Visium Spatial Gene Expression

Feature-Barcode Matrices

The spaceranger pipeline outputs unfiltered (raw_feature_bc_matrix) and filtered feature-barcode (filtered_feature_bc_matrix) 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 matrix is the number of UMIs associated with a feature (row) and a barcode (column).

Type Description
Unfiltered feature-barcode matrixContains every barcode from fixed list of known-good barcode sequences that has at least one read. This includes background and tissue-associated barcodes.
Filtered feature-barcode matrixContains only tissue-associated barcodes. For Targeted Gene Expression samples, non-targeted genes are removed from the filtered matrix. For Visium FFPE samples, genes not in the filtered probe set are removed from the filtered matrix by default.

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:

$ 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 matrix, the components of features.tsv.gz are:

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

$ gzip -cd filtered_feature_bc_matrix/features.tsv.gz
ENSG00000187634	SAMD11	Gene Expression
ENSG00000188976	NOC2L	Gene Expression
ENSG00000187961	KLHL17	Gene Expression

For Gene Expression 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.

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  | head -10

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 path to the matrix directory in red).

# load package
# 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

Loading 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 path to the matrix directory in red).

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")]

Converting to CSV Format

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 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:

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
# 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

            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:

# 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).