Article ; Online: Robust and efficient single-cell Hi-C clustering with approximate k-nearest neighbor graphs.
Bioinformatics (Oxford, England)
2021 Volume 37, Issue 22, Page(s) 4006–4013
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 ...
| 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 single-cell Hi-C dataset with 2600 cells and needs 40 GB of memory, while competitive approaches are not computable even with 1 TB of memory. It can be shown that the differentiation of the cells by their chromatin folding properties and, therefore, the quality of the clustering of single-cell Hi-C data is advantageous compared to competitive algorithms. Availability and implementation: The presented clustering algorithm is part of the scHiCExplorer, is available on Github https://github.com/joachimwolff/scHiCExplorer, and as a conda package via the bioconda channel. The approximate nearest neighbors implementation is available via https://github.com/joachimwolff/sparse-neighbors-search and as a conda package via the bioconda channel. Supplementary information: Supplementary data are available at Bioinformatics online. |
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| MeSH term(s) | Software ; Chromosomes ; Chromatin ; Algorithms ; Cluster Analysis |
| Chemical Substances | Chromatin |
| Language | English |
| Publishing date | 2021-05-18 |
| Publishing country | England |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 1422668-6 |
| ISSN | 1367-4811 ; 1367-4803 |
| ISSN (online) | 1367-4811 |
| ISSN | 1367-4803 |
| DOI | 10.1093/bioinformatics/btab394 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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