10x Genomics
Chromium Single Cell Multiome ATAC + Gene Exp.

Cell Ranger ARC2.0 (latest), printed on 12/22/2024

HDF5 Feature Barcode Matrix Format

In addition to the MEX format, we also provide matrices in the Hierarchical Data Format (HDF5 or H5). H5 is a binary format that can compress and access data much more efficiently than text formats such as MEX, which is especially useful when dealing with large datasets. H5 files are supported in both Python and R.

For more information on the format, see the Introduction to HDF5.

File format

The top level of the file contains a single HDF5 group, called matrix, and metadata stored as HDF5 attributes. Within the matrix group are datasets containing the dimensions of the matrix, the matrix entries, as well as the features and cell-barcodes associated with the matrix rows and columns, respectively.

The matrix entries are stored in Compressed Sparse Column (CSC) format. For more details on the format, see this SciPy introduction. CSC represents the matrix in column-major order, such that each barcode is represented by a contiguous chunk of data values.

The feature reference is stored as an HDF5 group called features, within the matrix group. See the documentation for the Molecule Info HDF5 file for details.

HDF5 file hierarchy

(root)
└── matrix [HDF5 group]
    ├── barcodes
    ├── data
    ├── indices
    ├── indptr
    ├── shape
    └── features [HDF5 group]
        ├─ _all_tag_keys
        ├─ feature_type
        ├─ genome
        ├─ id
        ├─ interval [cellranger-arc only]
        └─ name

Loading matrices into python

There are two ways to load the H5 matrix into Python:

1. Using cellranger-arc

This method requires that you add cellranger-arc-2.0.2/lib/python to your $PYTHONPATH. For example, if you installed Cell Ranger ARC into /opt/cellranger-arc-2.0.2, then you can call the following script to set your PYTHONPATH call:

$ source cellranger-arc-2.0.2/sourceme.bash

Then in Python, the matrix can be loaded as follows:

import cellranger.matrix as cr_matrix
filtered_matrix_h5 = "/opt/sample345/outs/filtered_feature_bc_matrix.h5"
filtered_feature_bc_matrix = cr_matrix.CountMatrix.load_h5_file(filtered_matrix_h5)

2. Using PyTables

This method is a bit more involved, and requires the SciPy and PyTables libraries.

import collections
import scipy.sparse as sp_sparse
import tables
 
CountMatrix = collections.namedtuple('CountMatrix', ['feature_ref', 'barcodes', 'matrix'])
 
def get_matrix_from_h5(filename):
    with tables.open_file(filename, 'r') as f:
        mat_group = f.get_node(f.root, 'matrix')
        barcodes = f.get_node(mat_group, 'barcodes').read()
        data = getattr(mat_group, 'data').read()
        indices = getattr(mat_group, 'indices').read()
        indptr = getattr(mat_group, 'indptr').read()
        shape = getattr(mat_group, 'shape').read()
        matrix = sp_sparse.csc_matrix((data, indices, indptr), shape=shape)
         
        feature_ref = {}
        feature_group = f.get_node(mat_group, 'features')
        feature_ids = getattr(feature_group, 'id').read()
        feature_names = getattr(feature_group, 'name').read()
        feature_types = getattr(feature_group, 'feature_type').read()
        feature_ref['id'] = feature_ids
        feature_ref['name'] = feature_names
        feature_ref['feature_type'] = feature_types
        tag_keys = [key.decode() for key in getattr(feature_group, '_all_tag_keys').read()]
        for key in tag_keys:
            feature_ref[key] = getattr(feature_group, key).read()
         
        return CountMatrix(feature_ref, barcodes, matrix)
 
filtered_matrix_h5 = "/opt/sample345/outs/filtered_feature_bc_matrix.h5"
filtered_feature_bc_matrix = get_matrix_from_h5(filtered_matrix_h5)