Cell Ranger6.1 (latest), printed on 10/22/2021
The cellranger pipeline outputs two types of feature-barcode matrices described in the table below. Each element of the matrix is the number of UMIs associated with a feature (row) and a barcode (column).
|Unfiltered feature-barcode matrix||Contains every barcode from the fixed list of known-good barcode sequences that has at least 1 read. This includes background and cell associated barcodes.|
|Filtered feature-barcode matrix||Contains only detected cellular barcodes. For Targeted Gene Expression samples, non-targeted genes are removed from the filtered matrix.|
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 filtered_feature_bc_matrix ├── barcodes.tsv.gz ├── features.tsv.gz └── matrix.mtx.gz
0 directories, 3 files
|Prior to Cell Ranger 3.0 the output matrix file format was different. In particular, the file genes.csv has been replaced by features.csv.gz to account for Feature Barcode technology, and the matrix and barcode files are now gzipped.|
Features correspond to row indices. For each feature, its 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, or CUSTOM, depending on the feature type. Below is a minimal example features.tsv.gz file showing data collected for 3 genes and 2 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 data, the id and name are taken from the first two columns of the Feature Barcode Reference 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_gene_bc_matrices/hg19/barcodes.tsv AAACATACAAAACG-1 AAACATACAAAAGC-1 AAACATACAAACAG-1 AAACATACAAACGA-1 AAACATACAAAGCA-1 AAACATACAAAGTG-1 AAACATACAACAGA-1 AAACATACAACCAC-1 AAACATACAACCGT-1 AAACATACAACCTG-1
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.
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.
library(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") 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
The csv, os, gzip and scipy.io modules can be used to load a feature-barcode matrix into Python as shown below.
import csv import gzip import os import scipy.io
matrix_dir = "/opt/sample345/outs/filtered_feature_bc_matrix" mat = scipy.io.mmread(os.path.join(matrix_dir, "matrix.mtx.gz"))
features_path = os.path.join(matrix_dir, "features.tsv.gz") feature_ids = [row for row in csv.reader(gzip.open(features_path), delimiter="\t")] gene_names = [row for row in csv.reader(gzip.open(features_path), delimiter="\t")] feature_types = [row for row in csv.reader(gzip.open(features_path), delimiter="\t")] barcodes_path = os.path.join(matrix_dir, "barcodes.tsv.gz") barcodes = [row for row in csv.reader(gzip.open(barcodes_path), delimiter="\t")]
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). 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 H5 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 H5 $ cellranger mat2csv sample123/outs/filtered_feature_bc_matrix.h5 sample123.csv
You can then load sample123.csv into Excel.
WARNING: dense files can be very large and may cause Excel to crash, or even fail in mat2csv if your computer doesn't have enough memory. For example, a feature-barcode matrix from a human reference (~33k genes) with ~3k barcodes uses at least 200MB of disk space. Our 1.3 million mouse neuron dataset, if converted to this format, would use more than 60GB of disk space.