Single-cell spatial (scs) omics: Recent developments in data analysis

TL;DR

This paper reviews recent advances in the analysis of single-cell spatial omics data, highlighting the challenges and opportunities across various omics modalities for biological research.

Contribution

It provides a comprehensive overview of data analysis methods specifically designed for the emerging field of single-cell spatial omics.

Findings

Identification of key challenges in data integration and analysis.

Summary of recent methodological developments.

Discussion of future research directions.

Abstract

Over the past few years, technological advances have allowed for measurement of omics data at the cell level, creating a new type of data generally referred to as single-cell (sc) omics. On the other hand, the so-called spatial omics are a family of techniques that generate biological information in a spatial domain, for instance, in the volume of a tissue. In this survey, we are mostly interested in the intersection between sc and spatial (scs) omics and in the challenges and opportunities that this new type of data pose for downstream data analysis methodologies. Our goal is to cover all major omics modalities, including transcriptomics, genomics, epigenomics, proteomics and metabolomics.