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filtered_feature_bc_matrix.h5
└── matrix [HDF5 group]
    ├── barcodes
    ├── data
    ├── features [HDF5 group]
    │   ├── _all_tag_keys
    │   ├── feature_type
    │   ├── genome
    │   ├── id
    │   ├── name
    │   └── target_sets  [Present when --probe-set or --target-panel is specified]
    │       └── [target set name]
    ├── indices
    ├── indptr
    └── shape

The hierarchy is same for raw_feature_bc_matrix.h5 file.

The contents of the .h5 file can be examined using HDFView software or the h5dump command.

• View all the file contents in .h5

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  h5dump -n filtered_feature_bc_matrix.h5ls
  
  HDF5 "./filtered_feature_bc_matrix.h5" {
  FILE_CONTENTS {
   group      /
   group      /matrix
   dataset    /matrix/barcodes
   dataset    /matrix/data
   group      /matrix/features
   dataset    /matrix/features/_all_tag_keys
   dataset    /matrix/features/feature_type
   dataset    /matrix/features/genome
   dataset    /matrix/features/id
   dataset    /matrix/features/name
   group      /matrix/features/target_sets
   dataset    /matrix/features/target_sets/[target set name]
   dataset    /matrix/indices
   dataset    /matrix/indptr
   dataset    /matrix/shape
   }
  }
  

• View the top few lines of a specific part of .h5 (e.g. the /matrix/barcodes dataset)

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  h5dump -d matrix/barcodes filtered_feature_bc_matrix.h5 | head -n 15
  
  HDF5 "filtered_feature_bc_matrix.h5" {
  DATASET "matrix/barcodes" {
     DATATYPE  H5T_STRING {
        STRSIZE 18;
        STRPAD H5T_STR_NULLPAD;
        CSET H5T_CSET_ASCII;
        CTYPE H5T_C_S1;
     }
     DATASPACE  SIMPLE { ( 2965 ) / ( 2965 ) }
     DATA {
     (0): "AACACTTGGCAAGGAA-1", "AACAGGATTCATAGTT-1", "AACAGGTTATTGCACC-1",
     (3): "AACAGGTTCACCGAAG-1", "AACAGTCAGGCTCCGC-1", "AACAGTCCACGCGGTG-1",
     (6): "AACATAGTCTATCTAC-1", "AACATCTTAAGGCTCA-1", "AACCAATCTGGTTGGC-1",
     (9): "AACCACTGCCATAGCC-1", "AACCAGAATCAGACGT-1", "AACCGCCAGACTACTT-1",
     (12): "AACCGTGCTTATGTTG-1", "AACCTAAGATACTGAG-1", "AACCTACTGTAACTCA-1",
  

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 spot-barcodes associated with the matrix rows and columns, respectively.

The matrix entries are stored in Compressed Sparse Column (CSC) format. CSC represents the matrix in column-major order, so 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. Note that for Targeted Gene Expression samples, the features dataset in the filtered matrix H5 file will not contain non-targeted genes, and the feature indices in target_sets are updated accordingly.

There are multiple packages that allow import of HDF5 file into R as shown in the example code below (edit path to the highlighted H5 file).

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# set path to the h5 file
h5_path = "/opt/sample345/outs/filtered_feature_bc_matrix.h5"

# Method 1
# load the Bioconductor rhdf5 package
library(rhdf5)

# read in the file and examine its contents
filtered_hs <- H5Fopen(h5_path)
h5ls(filtered_hs)

# Method 2
# load package
library(Seurat)

# read in the file and examine its contents
filtered_hs <- Read10X_h5(h5_path)
head(filtered_hs, 10)

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

Method 1: Using cellranger.matrix module

This method requires adding spaceranger/lib/python to your $PYTHONPATH. For example, if you installed Space Ranger into /opt/spaceranger-2.0.1, then you can call the following script to set your PYTHONPATH:

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$ source spaceranger-2.0.1/sourceme.bash

Then in Python, the matrix can be loaded using the cellranger.matrix module as follows (edit the path to the H5 file):

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import cellranger.matrix as cr_matrix
filtered_h5 = "/opt/sample345/outs/filtered_feature_bc_matrix.h5"
filtered_matrix_h5 = cr_matrix.CountMatrix.load_h5_file(filtered_hs)

Method 2: Using PyTables

This method is more involved, and requires the SciPy and PyTables libraries. Edit the path to the highlighted H5 file.

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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 = getattr(feature_group, '_all_tag_keys').read()
        for key in tag_keys:
            feature_ref[key] = getattr(feature_group, key.decode()).read()

        return CountMatrix(feature_ref, barcodes, matrix)



filtered_h5 = "/opt/sample345/outs/filtered_feature_bc_matrix.h5"
filtered_matrix_h5 = get_matrix_from_h5(filtered_hs)

Refer to the SciPy Documentation for help.