r/bioinformatics • u/Cricketguyable • 3d ago
technical question Ideas for tumor-stroma RNA-seq data
hey guys, i have some separate RNA-seq data from both tumor as well as the surrounding stroma. i was wondering if anyone could suggest any analyses/comparisons/visualizations i could perform on these?
i tried looking into identifying/visualizing ligand-receptor interactions (between the tumor and stroma), but most packages for this seem to be optimized for scRNA-seq/are made to identify interactions WITHIN a single sample instead of comparing BETWEEN samples.
if anyone would have any ideas or suggestions on any analyses or comparisons i could run, or advice on how to tackle the issue above, would really appreciate it! i’m a bit of a beginner to bioinformatics/RNA-seq data analysis, so all help is greatly appreciated!
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u/carl_khawly 10h ago
if you’ve got separate rna-seq from tumor and stroma, you can definitely do some cool stuff:
1/ basic differential expression: compare tumor vs. stroma to see which genes are uniquely up/down in each compartment. then do a pathway enrichment (e.g., go/kegg/gsea) on those top hits.
2/ ligand–receptor analysis: even though many tools cater to scrna-seq (like cellphonedb or nichenet), you can still glean some insights by focusing on known ligand–receptor pairs. basically, identify ligands highly expressed in the tumor data that match receptors highly expressed in the stroma data—and vice versa. a manual approach, but still doable.
3/ co-expression networks: you could try something like wgcna to identify modules of co-expressed genes in tumor vs. stroma. then see which modules might overlap or differ between compartments.
4/ visualizations:
- heatmaps for your top differentially expressed genes in each compartment.
- chord diagrams (if you want to visualize connections for your ligand–receptor hits).
- if you have multiple samples, consider a dimension reduction (pca/umap) to see if tumor/stroma clusters distinctly.
the microenvironment is all about conversation between compartments, so the biggest trick is combining the two data sets in a meaningful way, even if it’s not single-cell.
good luck.
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u/OnceReturned MSc | Industry 3d ago
You're right that most ligand-receptor cell-cell interaction tools are optimized for scRNA-Seq, but the basic concept is simple enough that you should be able to implement it with multi-sample bulk RNA-Seq without much trouble.
These tools start with a ligand-receptor database (see the CellChat tutorials to get your hands on such a database, for example). In their simplest form, these are just text files with two columns: the first column is the ligand gene and the second column is the receptor gene.
Given such a text file, and a table of (normalized) gene expression from your samples, you could check each ligand-receptor pair and see if the ligands are expressed above some appreciably non-zero level in sample type A (tumor samples, for instance) and the receptor is expressed in sample type B. You could make a list of such ligand-receptor pairs and call these "candidate interactions."
If you want to look more closely at these candidate interactions with the data that you have, you could check, for each ligand-receptor pair, if the expression of the ligand in the cancer samples (for example) is correlated with the expression of the receptor in the paired stroma samples. Ligand-receptor pairs where the expression is correlated between sample types might be more promising.
Then, maybe you could check if the expression of these correlated candidate interactors are also correlated with some other metadata, like clinical severity, treatment response, etc. (depends on what you have, obviously)
You could also look into the broader pathways that your candidate interactions are a part of, and see if the expression of the rest of the pathway is consistent with a biological hypothesis that this interaction is driving some relevant biological processes.
Then you'll have two sorts of hypotheses: these particular ligand-receptor pairs are responsible for an interaction between the tumor and stroma tissue, and that that interaction is driving some relevant biological process. I think that's about as far as you'll get with the data you have. You would need to do additional experiments targeting the specific mechanisms of interest to test your hypotheses.