Recent breakthroughs in high-throughput sequencing of single cells are revolutionizing the biological and biomedical sector. Among the different -omics layers that can be measured at the single-cell level, single-cell epigenomic measurements present a rich layer of regulatory information that stands between the genome and the transcriptome. These measurements can be obtained for large heterogeneous samples of single cells to profile tissues, organs and whole organisms, and to study dynamic processes like cellular differentiation, reprogramming or cancer evolution. These data types provide an unprecedented level of measurement resolution. In this talk, I will discuss how single-cell ATAC-seq data, which measures chromatin openness at the single-cell level, can be used to study cell types. I will present how differential openness at the noncoding part of the genome, measured by scATAC-seq data, can be exploited to study cell identity. I will then introduce a new method, based on geometry regularized autoencoders, to embed the single-cell data into a low dimensional space. I will explain how we exploit this low dimensional representation to classify cells and learn new drivers of variation in the population. Another level of genomic information that can be extracted from singleResearch cell data are single-cell copy number variations (CNVs). I will present an algorithm that we have developed, epiAneufinder, which exploits the read count information from scATAC-seq data to extract genome-wide CNVs for individual single-cells, and I will show how the obtained CNVs are comparable to the ones obtained from single-cell whole genome sequencing data. Thanks to epiAneufinder it is possible to add a relevant extra layer of genomic information, namely single-cell copy number variation, to every scATAC-seq dataset without the need of additional experiments.
Maria Colomé-Tatché (Tue,) studied this question.