Several University of Michigan researchers, including faculty affiliated with MIDAS, recently received support from the Chan Zuckerberg Initiative under its Human Cell Atlas project.
Description: One of the modalities that scientists participating in the Human Cell Atlas will use to gather data is single cell RNA sequencing (scRNA-seq). The analysis, however, of scRNA-seq data poses novel biological and algorithmic challenges. The data are high dimensional and not necessarily in distinct clusters (indeed, some cell types are exist along a continuum or developmental trajectory). In addition, data values are missing. To analyze this data, we must adjust our dimension reduction algorithms accordingly and either fill in the values or determine quantitatively the impact of the missing values. Furthermore, none of these steps is performed in isolation; they are part of a principled data analysis pipeline. This work will leverage over a decade of modern, sparsity-based machine learning methods and apply them to dimension reduction, marker selection, and data imputation for scRNA-seq data. In one of our two feature selection methods, we adapt a 1-bit compressed sensing algorithm (1CS) introduced by Genzel and Conrad. In order to select markers, the algorithm finds optimal hyperplanes that separate the given clusters of cells and that depend only on a small number of genes. The second method is based on the mutual information (MI) framework developed in. This algorithm greedily builds a set of markers out of a set of statistically significant genes that maximizes information about the target clusters and minimizes redundancy between markers. The imputation algorithms use sparse data models to impute missing values and are tailored to integer counts.
Description: Single cell RNA sequencing (scRNAseq) has emerged as a powerful tool in genomics. Unlike previous bulk RNAseq that measures average expression levels across many cells, scRNAseq can measure gene expression at the single cell level. The high resolution of scRNAseq has thus far transformed genomics: scRNAseq has been applied to classify novel cell-subpopulations and states, quantify progressive gene expression, perform spatial mapping, identify differentially expressed genes, and investigate the genetic basis of expression variation. While many computational tools have been developed for analyzing scRNAseq data, tools for effective integrative analysis of scRNAseq with other existing genetic/genomic data types are underdeveloped. Here, we propose to extend our previous integrative methods and develop novel computational tools for integrating scRNAseq data with genome-wide association studies (GWASs). Our proposed tools will identify cell-subpopulations relevant to GWAS diseases or traits, facilitate the interpretation of association results, catalyze more powerful future association studies, and help understand disease etiology and the genetic basis of phenotypic variation. The proposed tools will be applied to integrate summary statistics from various GWASs with fine-scale cell-subpopulations identified from the Human Cell Atlas (HCA) project, to maximize the impact of HCA and facilitate our understanding of the genetic architecture of various human traits and diseases — a question of central importance to human health.
Description: Single cell RNA sequence (ssRNAseq) data is a recently developed platform that enables the measurement of thousands of gene expression levels across individual cells in a tissue sample of interest. The ability to quantify gene expression at the cell level has great potential for advancing our understanding of the cellular processes that characterize a broad range of biological phenomena. However, compared with older bulk RNA technology, which measures expression levels of large numbers of cells in aggregate, ssRNAseq data has higher levels of measurement noise, which complicates its analysis. Furthermore, the problem of inferring cell type from ssRNAseq data is an unsupervised machine learning problem, an already difficult problem even without high measurement noise. To address these issues, we propose a mathematical and algorithmic framework to infer cellular characteristics by analyzing single cell and bulk RNA data simultaneously, via an approach grounded in matrix factorization. The developed algorithms will be evaluated on real data gathered by researchers at the University of Michigan who study breast cancer and spermatogenesis.
Description: Integrating the variation underlying different types of single cell measurements is a critical step toward a comprehensive catalog of human cell types. The ideal approach to construct a cell type atlas would use high-throughput single cell multi-omic profiling to simultaneously measure all cellular modalities of interest within each cell. Although this approach is currently out of reach, it is possible to separately perform high-throughput transcriptomic, epigenomic, and proteomic measurements at the single cell level. Computationally integrating multiple data modalities measured on different individual cells can circumvent the experimental challenges of multi-omic profiling. If different types of single cell measurements are performed on distinct single cells from a common population, each modality will sample a similar set of cells. Matching up similar cells to infer multimodal profiles enables some analyses for which multi-omic profiling is desirable, including multimodal cell type definition and studying covariance among different data types. Manifold alignment is a powerful computational technique for integrating multiple sources of data that describe the same set of events by discovering the common manifold (general geometric shape) that underlies them. Previously, we showed that transcriptomic and epigenomic measurements performed on distinct single cells share underlying sources of variation. We developed a computational method, MATCHER, which uses manifold alignment to integrate cell trajectories constructed from these measurements and infer single cell multi-omic profiles. Here, we will extend this approach to match multimodal single cell profiles sampled from an entire tissue.
Description: With the advent of single-cell genomic technologies, Human Cell Atlas (HCA) seeks to create a reference maps of each individual cell type and to understand how they develop and maintain their functions, how they interact with each other, and which environmental and/or genetic changes trigger molecular dysfunction that leads to disease. To achieve these goals, it becomes increasingly important to creatively integrate single-cell genomic technologies with novel computational methods to maximize the potential of the new technological advances. Recently, our group has developed a computational tool demuxlet that enable population- scale multiplexing of droplet-based single-cell RNA-seq (dscRNA-seq) experiments. Our approach harnesses natural genetic variation carried within dscRNA-seq reads to multiplex cells from many samples in a single library prep, and statistically deconvolute the sample identity of each barcoded droplet while filtering out multiplets (droplets that contain two or more cells). In this proposal, we aim to further extend our method to increase the accuracy by harnessing cell-specific expression levels, and to eliminate the constraint requiring external genotype data. We will enable application of these methods through production, distribution, and support of efficient, well-documented, open-source software; and test these tools through analysis of simulated data and of real dscRNA-seq data.