SpatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics.

ABSTRACT:

Background

Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times.

Findings

We propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space.

Conclusions

In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in l

SUBMITTER: Zhang C 

PROVIDER: S-EPMC11258913 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature