SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data Journal Article
| Authors: | Persad, S.; Choo, Z. N.; Dien, C.; Sohail, N.; Masilionis, I.; Chaligné, R.; Nawy, T.; Brown, C. C.; Sharma, R.; Pe’er, I.; Setty, M.; Pe’er, D. |
| Article Title: | SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data |
| Abstract: | Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene–peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort. © 2023, The Author(s). |
| Keywords: | human cell; genetics; phenotype; cytology; metabolism; gene; genes; gene expression; cohort analysis; genetic transcription; cell differentiation; algorithms; assay; rna; lymphocyte differentiation; disease severity; algorithm; epigenetics; chromatin; cd4+ t lymphocyte; cd4-positive t-lymphocytes; hematopoietic cell; genomics; cell aggregation; t lymphocyte activation; cells; t-cells; single cell analysis; single-cell analysis; transposase; epigenomics; clustering algorithms; single cells; genomic data; humans; human; article; data integration; cell state; cells by body anatomy; large dataset; single cell rna seq; coronavirus disease 2019; cellular state; cell clustering; cell data; cell grouping; assay for transposase accessible chromatin; metacell; single cell aggregation of cell state |
| Journal Title: | Nature Biotechnology |
| Volume: | 41 |
| Issue: | 12 |
| ISSN: | 1087-0156 |
| Publisher: | Nature Publishing Group |
| Date Published: | 2023-12-01 |
| Start Page: | 1746 |
| End Page: | 1757 |
| Language: | English |
| DOI: | 10.1038/s41587-023-01716-9 |
| PUBMED: | 36973557 |
| PROVIDER: | scopus |
| PMCID: | PMC10713451 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PubMed record and PDF. Corresponding MSK author is Dana Pe'er -- Source: Scopus |
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
Related MSK Work