2021
DOI: 10.1101/2021.11.15.468416
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Explainability methods for differential gene analysis of single cell RNA-seq clustering models

Abstract: Single-cell RNA sequencing (scRNA-seq) produces transcriptomic profiling for individual cells. Due to the lack of cell-class annotations, scRNA-seq is routinely analyzed with unsupervised clustering methods. Because these methods are typically limited to producing clustering predictions (that is, assignment of cells to clusters of similar cells), numerous model agnostic differential expression (DE) libraries have been proposed to identify the genes expressed differently in the detected clusters, as needed in t… Show more

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Cited by 2 publications
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“…Thirdly, scTIE provides the means to extract interpretable features from the common embedding space by linking the developmental trajectories of cell representations to their measured features (genes and peaks). We formulate a trajectory prediction problem using the estimated transition probabilities from OT and use gradient-based saliency mapping [20,21] to identify genes and peaks that are potentially driving the cellular state changes.…”
Section: Introductionmentioning
confidence: 99%
“…Thirdly, scTIE provides the means to extract interpretable features from the common embedding space by linking the developmental trajectories of cell representations to their measured features (genes and peaks). We formulate a trajectory prediction problem using the estimated transition probabilities from OT and use gradient-based saliency mapping [20,21] to identify genes and peaks that are potentially driving the cellular state changes.…”
Section: Introductionmentioning
confidence: 99%
“…Third, scTIE provides the means to extract interpretable features from the common embedding space by linking the developmental trajectories of cell representations to their measured features (genes and peaks). We formulate a trajectory prediction problem using the estimated transition probabilities from OT and use gradient-based saliency mapping ( Ciortan and Defrance 2021 ; Yang et al 2021 ) to identify genes and peaks that are potentially driving the cellular state changes. Compared with most GRN inference methods, which focus on developing new ways to construct network relationships among features selected through DE/DA analysis, the main innovation of scTIE lies in selecting these informative features based on their ability to predict cellular changes.…”
mentioning
confidence: 99%