# Imputing single-cell protein abundance in multiplex tissue imaging

## TL;DR

This paper introduces a machine learning approach to estimate missing protein levels in tissue imaging data, improving accuracy by using spatial information.

## Contribution

The novel use of spatial context in machine learning models to impute missing protein abundance in multiplex tissue imaging.

## Key findings

- Machine learning models achieved mean absolute errors of 0.05–0.3 in imputing protein abundance.
- Incorporating spatial context significantly improved imputation accuracy.
- Imputed data successfully classified cells as pre- or post-treatment, showing biological relevance.

## Abstract

Multiplex tissue imaging enables single-cell spatial proteomics and transcriptomics but remains limited by incomplete molecular profiling, tissue loss, and probe failure. Here, we apply machine learning to impute single-cell protein abundance using multiplex tissue imaging data from a breast cancer cohort. We evaluate regularized linear regression, gradient-boosted trees, and deep learning autoencoders, incorporating spatial context to enhance imputation accuracy. Our models achieve mean absolute errors between 0.05–0.3 on a [0,1] scale, closely approximating ground truth values. Using imputed data, we classify single cells as pre- or post-treatment, demonstrating their biological relevance. These findings establish the feasibility of imputing missing protein abundance, highlight the advantages of spatial information, and support machine learning as a powerful tool for improving single-cell tissue imaging.

Current tools for single-cell spatial omics still face barriers with regard to incomplete molecular profiling, tissue loss, and probe failure. Here, the authors use machine learning for the imputation of protein abundance in tissue-based cyclic immunofluorescence, showing that the spatial context can improve the accuracy of the imputation outputs.