Imputing Single-Cell Protein Abundance in Multiplex Tissue Imaging.

ABSTRACT: Multiplex tissue imaging are a collection of increasingly popular single-cell spatial proteomics and transcriptomics assays for characterizing biological tissues both compositionally and spatially. However, several technical issues limit the utility of multiplex tissue imaging, including the limited number of RNAs and proteins that can be assayed, tissue loss, and protein probe failure. In this work, we demonstrate how machine learning methods can address these limitations by imputing protein abundance at the single-cell level using multiplex tissue imaging datasets from a breast cancer cohort. We first compared machine learning methods' strengths and weaknesses for imputing single-cell protein abundance. Machine learning methods used in this work include regularized linear regression, gra

SUBMITTER: Kirchgaessner R 

PROVIDER: S-EPMC10723289 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature