Representing cells as sentences enables natural-language processing for single-cell transcriptomics
Abstract
AO_SCPLOWBSTRACTC_SCPLOWGene expression matrices commonly used in single-cell transcriptomics, cannot be directly analyzed with tools developed for natural languages. By restructuring these matrices as abundance-ordered sequences of genes, we generate cell sentences: rank-normalized, positionally encoded expression data. We show that these cell sentences can be analyzed using existing tools from natural language processing to unify cell and gene representations across species.
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