Barratt, J. | bioRxiv Science

bioRxiv2022

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell and pathomics data to find sample level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics and pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell and pathomics data. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression, and tissue structures related to the disease trajectories supporting interpretation of predictions.

bioinformatics↗

bioRxiv2019

From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL

While gene expression profiling is commonly used to gain an overview of cellular processes, the identification of upstream processes that drive expression changes remains a challenge. To address this issue, we introduce CARNIVAL, a causal network building tool which derives network architectures from gene expression footprints. CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) integrates different sources of prior knowledge, including signed and directed protein-protein interactions, transcription factor targets, and pathway signatures. The use of prior knowledge in CARNIVAL allows the capture of a broad set of upstream cellular processes and regulators, which in turn delivered results with higher accuracy when benchmarked against related tools. Implementation as an integer linear programming (ILP) problem also guarantees efficient computation. As a case study, we applied CARNIVAL to contextualize signaling networks from gene expression data in IgA nephropathy, a chronic kidney disease. CARNIVAL identified specific signaling pathways and associated mediators with important bioactivity in IgAN including WNT and TGF-{beta}, that we subsequently validated experimentally. In summary, we demonstrated how CARNIVAL generates hypotheses on potential upstream alterations that propagate through signaling networks, providing valuable insights into disease processes. CARNIVAL, freely available as an R-package at https://saezlab.github.io/CARNIVAL, can be applied to any field of biomedical research to contextualize signaling networks and identify the causal relationships between downstream gene expression and upstream regulators.

systems biology↗