Optimal gene selection for cell type discrimination in single cell analyses
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Given single cell RNA-seq data and a set of cellular labels to discriminate, scGene-Fit selects gene transcript markers that jointly optimize cell label recovery using label-aware compressive classification methods, resulting in a substantially more robust and less redundant set of markers.Abstract:
Single-cell technologies characterize complex cell populations across multiple data modalities at un-precedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers to identify and differentiate specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGene-Fit selects gene transcript markers that jointly optimize cell label recovery using label-aware compressive classification methods, resulting in a substantially more robust and less redundant set of markers than existing methods. When applied to a data set given a hierarchy of cell type labels, the markers found by our method enable the recovery of the label hierarchy through a computationally efficient and principled optimization.read more
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References
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Solving Linear Programs in the Current Matrix Multiplication Time
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Solving linear programs in the current matrix multiplication time
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A Unified Statistical Framework for Single Cell and Bulk RNA Sequencing Data
TL;DR: In this article, a Unified RNA-Sequencing Model (URSM) is proposed for both single cell and bulk RNA-seq data, formulated as a hierarchical model, which can estimate cell type specific gene expression profile.