Robust and efficient single-cell Hi-C clustering with approximate k-nearest neighbor graphs.

ABSTRACT:

Motivation

Hi-C technology provides insights into the 3D organization of the chromatin, and the single-cell Hi-C method enables researchers to gain knowledge about the chromatin state in individual cell levels. Single-cell Hi-C interaction matrices are high dimensional and very sparse. To cluster thousands of single-cell Hi-C interaction matrices, they are flattened and compiled into one matrix. Depending on the resolution, this matrix can have a few million or even billions of features; therefore, computations can be memory intensive. We present a single-cell Hi-C clustering approach using an approximate nearest neighbors method based on locality-sensitive hashing to reduce the dimensions and the computational resources.

Results

The presented method can process a 10 kb sing

SUBMITTER: Wolff J 

PROVIDER: S-EPMC9502147 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature