A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes.

ABSTRACT: While there are >2 million publicly-available human microarray gene-expression profiles, these profiles were measured using a variety of platforms that each cover a pre-defined, limited set of genes. Therefore, key to reanalyzing and integrating this massive data collection are methods that can computationally reconstitute the complete transcriptome in partially-measured microarray samples by imputing the expression of unmeasured genes. Current state-of-the-art imputation methods are tailored to samples from a specific platform and rely on gene-gene relationships regardless of the biological context of the target sample. We show that sparse regression models that capture sample-sample relationships (termed SampleLASSO), built on-the-fly for each new target sample to be imputed, outperform

SUBMITTER: Mancuso CA 

PROVIDER: S-EPMC7708069 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature