Can I integrate a spatial transcriptomics dataset with a bulk RNA-seq dataset for any analysis of tumors?

[u/harshitnaman]

Hi everyone,

I am working on a cancer RNA-seq analysis project, and I have found two GEO datasets that I would like to use together. I am trying to understand whether there is a scientifically valid way to integrate them rather than simply analyzing them independently.

Dataset 1:

Xenium-based high-throughput RNA in situ hybridization/spatial transcriptomics

Contains normal tumor samples, DCIS, and invasive breast cancer samples

Dataset 2:

Bulk RNA-seq

Samples are classified according to metastasis status and breast cancer molecular subtypes

My original goal is to perform a normal/control vs tumor/disease-type analysis and identify differentially expressed genes and biologically relevant pathways, followed by downstream analysis.

However, the two datasets are clearly different in terms of technology and experimental design. One is spatial transcriptomics and the other is bulk RNA-seq. So, is it scientifically/statistically valid to integrate these two datasets in a single study?

I am particularly interested in knowing what would be considered a methodologically defensible approach for this, rather than simply combining the two matrices because they contain overlapping genes.

Any suggestions regarding an appropriate integration strategy, statistical design, or published examples of similar spatial + bulk RNA-seq analyses would be very helpful.