Single-cell ATAC-seq clustering and differential analysis by convolution-based approach

Lin, L.; Zhang, L.

doi:10.1101/2020.02.13.947242

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bioRxiv · 10.1101/2020.02.13.947242

Single-cell ATAC-seq clustering and differential analysis by convolution-based approach

Lin, L.·Zhang, L.

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Abstract

Technical improvement in ATAC-seq makes it possible to profile the chromatin states of single cells at high throughput, but currently no method is available to integrate datasets from multiple sources (different batches of same protocol or multiple experimental protocols). Here we present an algorithm to perform joint analyses on scATAC-seq datasets from multiple sources. In addition to batch correction, we also demonstrate that epiConv is capable of aligning co-assay data (simultaneous profiling of transcriptome and chromatin) onto high-quality ATAC-seq reference or integrating cells in different biological conditions (malignant vs. normal), which increases the statistical power of downstream analyses and reveals hidden hierarchy of malignant cells.

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Lin, L., Zhang, L.. 2020-02-14. Single-cell ATAC-seq clustering and differential analysis by convolution-based approach. https://doi.org/10.1101/2020.02.13.947242

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