Defining cellular identity from single-cell genomic data with machine learning
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Human cell types vary widely in molecular and functional properties. Determining how these molecular and functional cell identities develop in healthy individuals and go wrong in disease is a fundamental problem in biomedical research. Single-cell genomic experiments measure the molecular properties of thousands of individual cells, yielding large and high-dimensional datasets analogous to image or text data. I will describe machine learning approaches we developed for several problems in single-cell genomic analysis, including identifying corresponding cells across datasets, predicting cell morphology from molecular information, modeling dynamics of stem cell development, and predicting cellular response to genetic or chemical perturbation.
Our research aims to address fundamental problems in both biomedical research and computer science by developing new tools tailored to rapidly emerging high-throughput sequencing technologies. Broadly, we seek to understand what genes define the complement of cell types and cell states within healthy tissue, how cells differentiate to their final fates, and how dysregulation of genes within specific cell types contributes to human disease. As computational method developers, we seek to both employ and advance the methods of machine learning, particularly for unsupervised analysis of high-dimensional data.
Most recently, I have focused on developing open-source software for the processing, analysis, and modeling of single cell sequencing data. Key contributions in this area include SingleSplice, the first computational method for single cell splicing analysis; SLICER, an algorithm for inferring developmental trajectories; and MATCHER, the first method for integrating single cell transcriptomic and epigenomic data. I have applied these methods in collaboration with biological scientists to study stem cell differentiation, somatic cell reprogramming, and the brain.