|
Abstract:
|
New single-cell genomic technologies are generating data from individual cells on a diverse set of genomic measurements. These assays offer enormous promise to researchers seeking to understand cellular heterogeneity, to identify and characterise novel cell types, and to study rare subpopulations of cells, with application to cancer research, developmental biology, and much more.
The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) can be used to measure the chromatin accessibility from individual cells. The data from each cell are very sparse, have a limited dynamic range (typically, only 0, 1, or 2 reads may be observed at any position in the genome), and are zero-inflated. Additionally, the number of cells profiled is small (in the tens or hundreds), and, like all single-cell genomics assays, scATAC-seq data display a large amount of cell-to-cell heterogeneity and technical artefacts that may confound biological signals.
I will present our work addressing some of these challenges in the statistical analysis of scATAC-seq data.
|
