TY - JOUR AU - Chen, Huidong AU - Lareau, Caleb AU - Andreani, Tommaso AU - Vinyard, Michael E. AU - Garcia, Sara P. AU - Clement, Kendell AU - Andrade-Navarro, Miguel A. AU - Buenrostro, Jason D. AU - Pinello, Luca PY - 2019 DA - 2019/11/18 TI - Assessment of computational methods for the analysis of single-cell ATAC-seq data JO - Genome Biology SP - 241 VL - 20 IS - 1 AB - Recent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans), lead to inherent data sparsity (1–10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (10–45% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level. SN - 1474-760X UR - https://doi.org/10.1186/s13059-019-1854-5 DO - 10.1186/s13059-019-1854-5 ID - Chen2019 ER -