SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data

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…

• HH
  Howard Hughes Medical Institute

  Award: P30 CA008748
• MS
  Memorial Sloan-Kettering Cancer Center

  Awards: CA008748, Support Grant P30 CA008748
• AA
  Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center
• NC
  National Cancer Institute

  Awards: U2C CA233284, CA233284, CA008748, P30 CA008748, Grant P30 CA008748, U54 CA209975, CA209975, Support Grant P30 CA008748
• NI
  National Institute of General Medical Sciences

  Awards: P30 CA008748, R35 GM147125