Chromatin accessibility (scATAC-seq data)

We show an example of scRNA-seq data produced by 10X Chromium. We are using scATAC-seq data 500 Peripheral blood mononuclear cells (PBMCs) from a healthy donor (Next GEM v1.1) (484 cells and 65,908 peaks) from 10X Genomics Datasets. The test data is directly available from Peak by cell matrix HDF5 (filtered) in here (registration required).

We use scanpy to read/write 10X data. Import numpy and scanpy in addlition to screcode.

import screcode
import numpy as np
import scanpy as sc
import warnings
warnings.simplefilter('ignore')

Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)

Read in the count matrix into an AnnData object.

input_filename = 'data/atac_pbmc_500_nextgem_filtered_peak_bc_matrix.h5'
adata = sc.readwrite._read_v3_10x_h5(input_filename)
adata

AnnData object with n_obs × n_vars = 484 × 65908
    var: 'gene_ids', 'feature_types', 'genome'

Apply RECODE

Apply RECODE to the count matrix. The anndata or ndarray data format is available.

recode = screcode.RECODE(seq_target='ATAC')
adata = recode.fit_transform(adata)

start RECODE for scATAC-seq data
end RECODE for scATAC-seq
log: {'seq_target': 'ATAC', '#significant peaks': 55207, '#non-significant peaks': 10697, '#silent peaks': 4, 'ell': 27, 'Elapsed time': '0h 0m 5s 022ms', 'solver': 'full'}

With anndata format, outputs of RECODE are included in anndata objects: - denoised matrix -> adata.obsm[‘RECODE’] - noise variance -> adata.var[‘noise_variance_RECODE’] - normalized variance (NVSN variance) -> adata.var[‘normalized_variance_RECODE’] - clasification of genes (significant/non-significant/silent) -> adata.var[‘significance_RECODE’]

adata

AnnData object with n_obs × n_vars = 484 × 65908
    var: 'gene_ids', 'feature_types', 'genome', 'noise_variance', 'normalized_variance', 'significance_RECODE'
    uns: 'RECODE_essential'
    layers: 'RECODE', 'RECODE_NVSN'

Performance verification

Show report:

recode.report()