A Literature-Derived Knowledge Graph Augments the Interpretation of Single Cell RNA-seq Datasets.

ABSTRACT: Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann W

SUBMITTER: Doddahonnaiah D 

PROVIDER: S-EPMC8229796 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature