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New Paper "Robust and efficient single-cell Hi-C clustering with approximate k-nearest neighbor graphs"

Joachim Wolff and colleagues have published a paper providing a novel single-cell Hi-C clustering approach. Congratulations Joachim!



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.


The presented method can process a 10 kb single-cell Hi-C data set 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.


The presented clustering algorithm is part of the scHiCExplorer, is available on Githubhttps://github.com/joachimwolff/scHiCExplorer, and as a conda package via the bioconda channel. Theapproximate nearest neighbors implementation is available via https://github.com/joachimwolff/sparse-neighbors-search and as a conda package via the bioconda channel.