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SEACells: Inference of transcriptional and epigenomic cellular states from single-cell genomics data

UID: 11157

Author(s): Persad, Sitara*, Choo, Zi-Ning, Dien, Christine, Masilionis, Ignas*, Chaligne, Ronan, Nawy, Tal*, Brown, Chrysothemis C.*, Pe'er, Itsik, Setty, Manu*, Pe'er, Dana* * MSK affiliated

Description
Summary from GEO:

"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; overcoming the sparsity of single-cell data, while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying accurate, compact, and well-separated metacells in both RNA and 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 measure gene accessibility in each metacell. Metacell-level analysis scales to large datasets and are particularly well suited for patient cohorts, including facilitation of 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 COVID-19 patient cohort."

Overall design from GEO:

"Cryopreserved bone marrow cells from healthy donor were purchased from AllCells, LLC and stored in vapor phase nitrogen. Each sample was processed and loaded into two 10X channels to increase the number of cells."
Subject of Study
Homo sapiens
Subject(s)
Genomics
Single-Cell Analysis
Transcription, Genetic
Access via GEO


Accession #: GSE200046

Access via BioProject


Accession #: PRJNA822790

Access Restrictions
Free to All
Access Instructions
The NCBI Gene Expression Omnibus and BioProject databases provide open access to these files.
Associated Publications
Persad S, Choo Z, Dien C, Sohail N, Masilionis I, Chaligné R, Nawy T, Brown C, Sharma R, Pe’er I, Setty M, Pe’er D. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data. Nature Biotechnology. 2023; 41(12):1746-1757.
Equipment Used
Illumina NovaSeq 6000
Dataset Format(s)
TSV, TAR, H5, H5AD
Dataset Size
10.6 GB
Data Catalog Record Updated
2024-02-15