<p>Look at <a href="https://www.nature.com/articles/s41467-017-02554-5" rel="noreferrer">this recent paper</a> that uses ComBat on scRNA-seq data for batch effect removal and states that it "successfully does so".</p>

<p>I also suggest that you check out <a href="https://arxiv.org/pdf/1610.04181.pdf" rel="noreferrer">this publication</a> on Distribution Matching Residual-Nets. Authors evaluated their method also on scRNA-seq data and thus it may be something you are looking for. I personally played a bit with their code and found it straightforward to use. </p>

CC BY-SA 3.0
2018-04-07T10:10:49.977Z
2018-04-07T10:10:49.977Z
Martin
<p>There are competing claims regarding how well ComBat works on scRNA-seq datasets. A <a href="https://www.nature.com/articles/nbt.4091" rel="nofollow noreferrer">recent paper</a> from John Marioni's introduces a mutual nearest neighbors method that seems to outperform ComBat in at least some relevant scenarios. In general, it's best to look at tSNE or other diagnostic plots after batch correction to see if the results seem reasonable (this advice holds generally).</p>

CC BY-SA 3.0
2018-04-10T22:41:03.493Z
2018-04-10T22:41:03.493Z
Devon Ryan
<p>Is it possible to use <a href="https://www.bioconductor.org/packages/release/bioc/html/sva.html" rel="nofollow noreferrer">sva's ComBat</a> for batch effects removal on scRNA-seq data?</p>

<p>Is there any difference between RNA-seq and scRNA-seq data which doesn't allow to use ComBat on single-cell data? (I am thinking about extreme sparsity, > 80%). If so, are there alternatives?</p>

CC BY-SA 3.0
2018-02-02T19:43:09.593Z
see [here](https://bioinformatics.stackexchange.com/questions/3208/order-of-batch-effects-removal-data-imputation-and-library-size-normalization-i) for a simiar [unanswered] question.
gringer
CC BY-SA 3.0
2018-02-02T20:54:52.373Z
@gringer Indeed, that was another question of mine :)
gc5
<rna-seq><scrnaseq><combat><batch-effects>
ComBat for batch effects removal on scRNA-seq data
# ComBat for batch effects removal on scRNA-seq data

(Asked by: username_0 on 2018-02-02)

Is it possible to use [sva's ComBat](https://www.bioconductor.org/packages/release/bioc/html/sva.html) for batch effects removal on scRNA-seq data?

Is there any difference between RNA-seq and scRNA-seq data which doesn't allow to use ComBat on single-cell data? (I am thinking about extreme sparsity, > 80%). If so, are there alternatives?

**Tags:** rna-seq, scrnaseq, combat, batch-effects
> **Comments:**
> **username_1**: see [here](https://bioinformatics.stackexchange.com/questions/3208/order-of-batch-effects-removal-data-imputation-and-library-size-normalization-i) for a simiar [unanswered] question.  
> **username_0**: @gringer Indeed, that was another question of mine :)  

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## 2 answers

### Answer by username_2 on 2018-04-07. Score: 5 | Accepted answer

Look at [this recent paper](https://www.nature.com/articles/s41467-017-02554-5) that uses ComBat on scRNA-seq data for batch effect removal and states that it "successfully does so".

I also suggest that you check out [this publication](https://arxiv.org/pdf/1610.04181.pdf) on Distribution Matching Residual-Nets. Authors evaluated their method also on scRNA-seq data and thus it may be something you are looking for. I personally played a bit with their code and found it straightforward to use.


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### Answer by username_3 on 2018-04-10. Score: 4

There are competing claims regarding how well ComBat works on scRNA-seq datasets. A [recent paper](https://www.nature.com/articles/nbt.4091) from John Marioni's introduces a mutual nearest neighbors method that seems to outperform ComBat in at least some relevant scenarios. In general, it's best to look at tSNE or other diagnostic plots after batch correction to see if the results seem reasonable (this advice holds generally).


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