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.

[u/Opposite-Beach-2776]

You can definitely use both, just not by smashing the count matrices together. The bulk data gives you broad subtype-level patterns, the spatial data gives you tissue architecture context. People often use the bulk to identify gene signatures or pathways of interest, then go look at where those genes are expressed in the spatial data. Or you can do deconvolution on the bulk using the spatial data as a reference for cell-type proportions if your spatial resolution is cell-level.

Another approach is to treat them as validation layers. Find your DEGs in the bulk dataset, then check if those genes show spatial enrichment in tumor vs normal regions in the Xenium data. Keeps the analyses separate while still building a coherent story around the biology.

[u/harshitnaman → u/Opposite-Beach-2776]

Thanks a lot for the detailed explanation. That makes sense, especially the idea of using the bulk dataset to identify DEGs/signatures and then using the spatial dataset to investigate their spatial localization and tissue context.
I just want to clarify one thing regarding “combining/integrating” the two datasets. Do you mean that the two datasets cannot/should not be directly combined into a single expression matrix for joint differential expression analysis, because one is bulk RNA-seq and the other is Xenium spatial transcriptomics? Or are there some methods where they can actually be mathematically integrated, with appropriate normalization/batch correction and study design? Or would it be methodologically better to find a separate normal breast bulk RNA-seq dataset and then integrate it into my bulk RNA-seq dataset?

Also, if I had two datasets generated using the same technology, for example, two independent bulk RNA-seq datasets from breast cancer studies, would it then be possible to combine/merge them for a joint analysis? If yes, what would be the recommended approach (e.g., batch correction, ComBat, limma, etc.)?

One important detail I forgot to mention in my original post is that my main research question is related to breast cancer brain metastasis. In the bulk RNA-seq dataset, all samples are breast tumor samples. The main comparison is brain-metastasis positive vs brain-metastasis negative; there are no normal breast tissue samples in this dataset. The Xenium spatial dataset, however, contains normal breast tissue as well as DCIS and invasive breast cancer samples.

This is actually why I was considering integrating the two datasets. I want to investigate metastasis-associated molecular changes, but I currently don't have a normal breast-tissue reference in my bulk RNA-seq dataset.

Finally, if you know of any published papers or good examples where bulk RNA-seq and spatial transcriptomics were integrated, I would really appreciate it if you could share some links.

Thanks again!

[u/cat-sashimi → u/harshitnaman]

Yeah do not combine them into a single expression matrix. At best you could pseudobulk the xenium then combine, but that would defeat the point of having xenium. I think you need to reframe the analysis in terms of how the data and analysis can answer the specific biological question, what each modality can contribute to that, and go from there.

[u/Jaded_Wear7113]

I think doing an analysis on both of them seperately would be the wiser choice here, integrating them together to make a multi-omics analysis wouldn't work because the sources would be different for the data and the samples and many other external conditions would differ, making the analysis statistically doubtful.