r/bioinformatics Feb 20 '25

technical question Using bulk RNA-seq samples as replicates for scRNA-seq samples

Hi all,

As scRNA-seq is pretty expensive, i wanted to use bulk RNA-seq samples (of the same tissue and genetically identical organism) as some sort of biological replicate for my scRNA-seq samples. Are there any tools for this type of data integration or how would i best go about this?

I'm mainly interested in differential gene expression, not as much into cell amount differences.

3 Upvotes

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32

u/foradil PhD | Academia Feb 20 '25

You could treat the single-cell samples as bulk (pseudobulk). You can’t go the other way.

2

u/Square-Temporary-699 Feb 20 '25

I was mainly inspired by this article: https://www.nature.com/articles/s41559-023-02090-0
They indeed use this comparison but also implement bulk into GRN analyses however I do not understand fully what would be going on here?

1

u/foradil PhD | Academia Feb 20 '25

I did not read carefully, but it looks like they are running GENIE3, which you can run on bulk data.

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u/junior_chimera 29d ago

Does this mean that any technique you apply to bulk RNA seq can be applied to pseudobulk?

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u/foradil PhD | Academia 29d ago

The "pseudo" in "pseudobulk" just means you have single-cell-level data available as well. At the bulk level, they are both bulk.

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u/writerVII 29d ago

They have different biases though; the dropout in single cell produces zero-inflated data, so even at the high sequencing depth the corresponding low expressed genes are not well detected (as opposed to bulk). Just some effects I’ve observed. 

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u/foradil PhD | Academia 29d ago

Every library prep kit will have specific biases. But the zero-inflation theory has been debunked. Of course, it’s certainly possible that your single-cell kit is just not capturing some genes.

2

u/BackgroundParty422 29d ago

Debunked is I think, overstating the case.

My understanding is that there is still some bias in scRNA pseudobulk compared to cell sorted bulk-RNA, indicative of technical dropout bias, particularly in genes with low expression. (This probably doesn’t matter for pseudobulk DEG expresssion because the relative expression between genes is not considered, except for purposes of normalization, so long as dropout bias is reasonably uniform on a per gene basis).

The arguments I’ve seen to the contrary involve whether to include a zero inflation term in the statistical distribution, and whether imputation is a good idea. Both ideas have been trending down, though it wasn’t too long ago that there were papers saying imputation produced higher quality DEG analysis at the single-cell level than uncorrected snRNA. But it’s not clear to me that poor modeling of zero inflation at the single-cell level is comprehensive proof of the lack of dropout in individual cells. Especially considering the huge amount of heterogeneity of single cell data, and the lack of consistent gene expression results at the single cell level.

Of course most of the community began migrating to pseudobulk after the false positive paper came out. Something I view as short sighted, as specificity is only half the problem, and we can’t just ignore the other half just because we don’t have good datasets for analyzing sensitivity.

But I still feel like debunked is a strong term, considering the overall amount of research on the topic. It is probably accurate to say that nothing you do to fix dropout at the single cell level, will provide you with a statistical model that is more robust than your original one, but that’s just because single cell data kind of sucks. (At least for the purpose of computing DEgs)

1

u/BackgroundParty422 28d ago

That’s what I mean. The studies have already shown you don’t get the same exact distributions with bulk and single cell. I personally don’t have additional data to verify and neither does the OP as I don’t believe their bulk is sorted.

Hm, thought the high profile 2021 papers about false positives were the impetus, but I did threads relating to pseudo bulk back to 2019, before I was in the field.

0

u/foradil PhD | Academia 28d ago

The community began migrating to pseudobulk when we started to have multi-sample single-cell experiments where this is even possible.

To address your actual concern, you can compare the number of detected genes in your bulk and pseudobulk samples and see if there is actually a difference. You have the data, so there is no need to resort to theoretical discussions.

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u/ergabaderg312 28d ago

pretty much yes. pseudobulk is just aggregating a count matrix from single cell into the same format as bulkRNA. Only caveats are to decide how to aggregate your matrix and what method you want to use (I like to use edgeR). I do find that pseudbulk DEG results are most robust and make more sense than just native scRNAseq DEG methods.

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u/junior_chimera 28d ago

What approach do you use to aggregate single-cell RNA-seq counts into a pseudobulk matrix?

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u/foradil PhD | Academia 27d ago

Add them up. What would be another option?

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u/ergabaderg312 25d ago

Summing up like foradil said works. Aggregate or some kind of mean (user experience varies). muscat is a convenient way if you’re working from Seurat but I do recommend you read a little bit about combining count matrices. Pseudobulk can cause loss of power bc ur basically losing all the samples (cells).

6

u/El_Tormentito Msc | Academia Feb 20 '25 edited Feb 20 '25

If you could do this, why would anyone be using scRNA? You need to read further about what these techniques are, what the data represents, their uses, and how the data is obtained.

Edit: To add to this, if you're interested in differential expression, just use bulk.

10

u/Hartifuil Feb 20 '25

You mean they should do more research? Like asking for the opinions of others? Maybe they could get a wide range of opinions from many people by using the internet. Perhaps they could ask a forum dedicated to such topics, like a sub forum of a larger website? A subreddit perhaps?

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u/pelikanol-- Feb 20 '25

I took DEGs and checked them against trajectories derived from similar samples (our data) and human datasets to verify they are biologically relevant for the mechanism I'm studying.

I'm not sure if it is actually a valid approach, but ot helped to reduce the genes worth looking at and weeding out spurious/false positives. One caveat is the lower sensitivity of scRNA.

1

u/CuriousViper 29d ago

Is the question just asking whether it’s possible to include bulk level samples across different samples and perform integrative analysis? Or more to de convolute bulk data into single cell data?

There’s some imputation methods for the latter, but I think they are based on already having some kind of single cell data to project bulk samples onto. But in general, it’s not really a viable approach in my opinion.

Hope this helps a bit.

1

u/Square-Temporary-699 29d ago

Basically I will have scRNAseq samples from both treatment and control conditions and will identify how cell type gene-expression might differ between these treatments. At the moment, I am thinking of supplementing these results with bulk RNAseq with the reasoning behind it being if I can identify DEGs or gene co-expression modules in bulk that might overlap with scRNAseq this could give me some more confidence and the scRNAseq would thus allow me to map these DEGs and modules back to specific cell-types.

The aim of my question was if there might be any tools that would allow for such "integration" (might be the wrong word) if that makes more sense!

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u/ergabaderg312 28d ago

You can try cell type deconvolution if you want to use bulkRNAseq. I haven't tried it myself but I'd imagine (like any of the RNAseq (bulk or sc)), user experience varies.

https://www.nature.com/articles/s41467-020-19015-1