<p>No, you cannot. It is a basic principle of any experiment that you cannot add data from completely unrelated other experiments and expect any meaningful quantitative readout. There is a batch effect between the existing data and the newly-added public data, and since batch and experimental group is the same here you cannot correct for it. Don't do that.</p>

CC BY-SA 4.0
2024-04-29T11:16:13.603Z
2024-04-29T11:16:13.603Z
ATpoint
<p>I have recently sequenced a bulk dataset. However one of my conditions has a lot of contamination from other cell types. I was thus thinking of using a publicly available single-cell dataset of my cell type of intereset, perform a pseudobulk reduction and integrate the pseudobulk dataset into my bulk RNA seq data.</p>
<p>Is this possible ? Or would there be way too much transcriptome variation and batch to batch differences ? Can I normalize for batch somehow?</p>

<rna-seq><scrnaseq><rna><transcriptome><batch-effects>
Is it possible to integrate a bulk and a pseudobulk (previously scRNA or snRNA seq) dataset
# Is it possible to integrate a bulk and a pseudobulk (previously scRNA or snRNA seq) dataset

(Asked by: username_0 on 2024-04-29)

I have recently sequenced a bulk dataset. However one of my conditions has a lot of contamination from other cell types. I was thus thinking of using a publicly available single-cell dataset of my cell type of intereset, perform a pseudobulk reduction and integrate the pseudobulk dataset into my bulk RNA seq data.

Is this possible ? Or would there be way too much transcriptome variation and batch to batch differences ? Can I normalize for batch somehow?

**Tags:** rna-seq, scrnaseq, rna, transcriptome, batch-effects

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## 1 answers

### Answer by username_1 on 2024-04-29. Score: 1

No, you cannot. It is a basic principle of any experiment that you cannot add data from completely unrelated other experiments and expect any meaningful quantitative readout. There is a batch effect between the existing data and the newly-added public data, and since batch and experimental group is the same here you cannot correct for it. Don't do that.


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