Inferring spatial and signaling relationships between cells from single cell transcriptomic data.

Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however, crucial spatial information is often lost. We present SpaOTsc, a method relying on structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively small number of genes. A spatial metric for individual cells in scRNA-seq data is first established based on a map connecting it with the spatial measurements. The cell-cell communications are then obtained by "optimally transporting" signal senders to target signal receivers in space. Using partial information decomposition, we next compute the intercellular gene-gene information flow to estimate the spatial regulations between genes across cells. Four datasets are employed for cross-validation of spatial gene expression prediction and comparison to known cell-cell communications. SpaOTsc has broader applications, both in integrating non-spatial single-cell measurements with spatial data, and directly in spatial single-cell transcriptomics data to reconstruct spatial cellular dynamics in tissues

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells

Single-cell RNA-seq allows quantification of biological heterogeneity across both discrete cell types and continuous cell differentiation transitions. We present approximate graph abstraction (AGA), an algorithm that reconciles the computational analysis strategies of clustering and trajectory inference by explaining cell-to-cell variation both in terms of discrete and continuous latent variables (https://github.com/theislab/graph_abstraction). This enables to generate cellular maps of differentiation manifolds with complex topologies - efficiently and robustly across different datasets. Approximate graph abstraction quantifies the connectivity of partitions of a neighborhood graph of single cells, thereby generating a much simpler abstracted graph whose nodes label the partitions. Together with a random walk-based distance measure, this generates a topology preserving map of single cells - a partial coordinatization of data useful for exploring and explaining its variation. We use the abstracted graph to assess which subsets of data are better explained by discrete clusters than by a continuous variable, to trace gene expression changes along aggregated single-cell paths through data and to infer abstracted trees that best explain the global topology of data. We demonstrate the power of the method by reconstructing differentiation processes with high numbers of branchings from single-cell gene expression datasets and by identifying biological trajectories from single-cell imaging data using a deep-learning based distance metric. Along with the method, we introduce measures for the connectivity of graph partitions, generalize random-walk based distance measures to disconnected graphs and introduce a path-based measure for topological similarity between graphs. Graph abstraction is computationally efficient and provides speedups of at least 30 times when compared to algorithms for the inference of lineage trees