Read/write a 10x feature matrix

Read/write a 10x feature matrix

Usage

open_matrix_10x_hdf5(path, feature_type = NULL, buffer_size = 16384L)

write_matrix_10x_hdf5(
  mat,
  path,
  barcodes = colnames(mat),
  feature_ids = rownames(mat),
  feature_names = rownames(mat),
  feature_types = "Gene Expression",
  feature_metadata = list(),
  buffer_size = 16384L,
  chunk_size = 1024L,
  gzip_level = 0L,
  type = c("uint32_t", "double", "float", "auto")
)

Arguments

path

Path to the hdf5 file on disk

feature_type

Optional selection of feature types to include in output matrix. For multiome data, the options are "Gene Expression" and "Peaks". This option is only compatible with files from cellranger 3.0 and newer.

buffer_size

For performance tuning only. The number of items to be buffered in memory before calling writes to disk.

mat

IterableMatrix

barcodes

Vector of names for the cells

feature_ids

Vector of IDs for the features

feature_names

Vector of names for the features

feature_types

String or vector of feature types

feature_metadata

Named list of additional metadata vectors to store for each feature

chunk_size

For performance tuning only. The chunk size used for the HDF5 array storage.

gzip_level

Gzip compression level. Default is 0 (no compression)

type

Data type of the output matrix. Default is uint32_t to match a matrix of 10x UMI counts. Non-integer data types include float and double. If auto, will use the data type of mat.

Value

BPCells matrix object

Details

The 10x format makes use of gzip compression for the matrix data, which can slow down read performance. Consider writing into another format if the read performance is important to you.

Input matrices must be in column-major storage order, and if the rownames and colnames are not set, names must be provided for the relevant metadata parameters. Some of the metadata parameters are not read by default in BPCells, but it is possible to export them for use with other tools.