TIST Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics

Sequencing-based spatial transcriptomics (ST) is an emerging technique to study in situ gene expression patterns at whole-genome scale. However, this technique still has several limitations, especially high data dropouts and local molecular diffusion effects. Except the transcriptomic data, the technique usually generates matched histopathological images for the same tissue sample. The image data are of high spatial continuity and resolution, which can provide complementary cellular phenotypical information with the noisy ST data.

Here, we propose a novel ST data analysis method called TIST (Transcriptome and histopathological Image integrative analysis for Spatial Transcriptomics) by integrating the information from the sequencing-based ST data and the histopathological images. TIST uses Markov random field to learn the macroscopic cellular features from histopathological images, and devises a random-walk-based strategy to integrate the extracted image features, the transcriptomic features and the location information for spatial cluster (SC) identification and gene expression enhancement.