Scalable and unbiased sequence-informed embedding of single-cell ATAC-seq data with CellSpace Journal Article
| Authors: | Tayyebi, Z.; Pine, A. R.; Leslie, C. S. |
| Article Title: | Scalable and unbiased sequence-informed embedding of single-cell ATAC-seq data with CellSpace |
| Abstract: | Standard scATAC sequencing (scATAC-seq) analysis pipelines represent cells as sparse numeric vectors relative to an atlas of peaks or genomic tiles and consequently ignore genomic sequence information at accessible loci. Here we present CellSpace, an efficient and scalable sequence-informed embedding algorithm for scATAC-seq that learns a mapping of DNA k-mers and cells to the same space, to address this limitation. We show that CellSpace captures meaningful latent structure in scATAC-seq datasets, including cell subpopulations and developmental hierarchies, and can score transcription factor activities in single cells based on proximity to binding motifs embedded in the same space. Importantly, CellSpace implicitly mitigates batch effects arising from multiple samples, donors or assays, even when individual datasets are processed relative to different peak atlases. Thus, CellSpace provides a powerful tool for integrating and interpreting large-scale scATAC-seq compendia. © The Author(s) 2024. |
| Keywords: | human cell; genetics; protein motif; mouse; animal; metabolism; animals; mice; gene expression; erythroid precursor cell; dendritic cell; protein binding; transcription factor; cell differentiation; cell population; algorithms; transcription factors; gene mapping; dna; algorithm; cell subpopulation; hematopoietic stem cell; multipotent stem cell; fluorescence activated cell sorting; myeloid progenitor cell; transcription factor gata 1; sequence analysis, dna; microglia; single cell analysis; single-cell analysis; consensus sequence; granulocyte precursor; procedures; dimensionality reduction; natural language processing; embedding; dna sequencing; humans; human; article; megakaryocyte erythroid progenitor; oligodendrocyte precursor cell; single cell rna seq; chromatin immunoprecipitation sequencing; atac sequencing; cellspace algorithm; position weight matrix |
| Journal Title: | Nature Methods |
| Volume: | 21 |
| Issue: | 6 |
| ISSN: | 1548-7091 |
| Publisher: | Nature Publishing Group |
| Date Published: | 2024-06-01 |
| Start Page: | 1014 |
| End Page: | 1022 |
| Language: | English |
| DOI: | 10.1038/s41592-024-02274-x |
| PUBMED: | 38724693 |
| PROVIDER: | scopus |
| PMCID: | PMC11166566 |
| DOI/URL: | |
| Notes: | Article -- Source: Scopus |
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