Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data

TL;DR

This paper reviews how machine learning, especially deep learning, is transforming the analysis of spatial transcriptomics data, helping to interpret complex biological patterns and guide research in health and disease.

Contribution

It summarizes key analysis goals in spatial transcriptomics and provides heuristics for selecting appropriate machine learning tools for biological questions.

Findings

ML tools enhance signal extraction from noisy spatial transcriptomics data

Four data science concepts guide the choice of analysis methods

Review of current trends and challenges in ST data analysis

Abstract

Development and homeostasis in multicellular systems both require exquisite control over spatial molecular pattern formation and maintenance. Advances in spatially-resolved and high-throughput molecular imaging methods such as multiplexed immunofluorescence and spatial transcriptomics (ST) provide exciting new opportunities to augment our fundamental understanding of these processes in health and disease. The large and complex datasets resulting from these techniques, particularly ST, have led to rapid development of innovative machine learning (ML) tools primarily based on deep learning techniques. These ML tools are now increasingly featured in integrated experimental and computational workflows to disentangle signals from noise in complex biological systems. However, it can be difficult to understand and balance the different implicit assumptions and methodologies of a rapidly expanding toolbox of analytical tools in ST. To address this, we summarize major ST analysis goals that ML can help address and current analysis trends. We also describe four major data science concepts and related heuristics that can help guide practitioners in their choices of the right tools for the right biological questions.