<p>I would be very hesitant to use SES normalization in a case like this. We recommend that people run plotFingerprint first and see if there is good separation between the samples and only then using SES. I would not expect there to be drastic separation between the samples for ATAC-seq data, so I don't think it will work well. Our standard ATAC-seq normalization is RPGC (aka, "1x normalization"). That should work well for your use-case as well.</p>

<p>As an aside, if you really want to make statistically-backed comparisons between groups I can recommend the CSAW package.</p>

CC BY-SA 4.0
2019-10-07T15:54:34.690Z
Thanks Devon! multiBamSummary tool produces a scalingFactor table for respective BAMs. Does it make sense to first scale with these scaling factors, create bigwig using bamConverge and then use these bigwigs for comparison using bigwigcompare? I am wondering if scaled files go to bigwigcompare, will it make for a better comparison across comparisons.
zerotimer
CC BY-SA 4.0
2019-10-07T15:56:15.393Z
Yes, that's another good way of approaching things. `bigwigCompare` does assume that the input bigWigs are scaled appropriately and the procedure you suggest will create a nicely robust comparison.
Devon Ryan
CC BY-SA 4.0
2019-10-07T17:53:24.427Z
Thanks! You mentioned plotFingerprint for seeing good separation. I did the plotCorrelation heatmap. Is there some approximate threshold for "good separation"? Let's say I have a correlation between 0.5 and 0.7 across different samples. Will this make them ok for SES?
zerotimer
CC BY-SA 4.0
2019-10-07T20:25:24.743Z
There's no substitute for plotFingerprint here.
Devon Ryan
CC BY-SA 4.0
2019-10-15T19:12:01.383Z
Devon, 

Can we use scalingFactor table (from multiBamSummary) to create new scaled BAMs instead of bigwig/bedgraph? I want to perform peak calling only on normalized/scaled BAMs so I can properly compare peaks/footprints across different samples. I know I can downsample using samtools but some scaling factors are greater than 1 so not sure how to "upscale" it. 

Thanks again!
zerotimer
CC BY-SA 4.0
2019-10-15T19:27:29.867Z
So  I found a post where you suggest not to upsample:
https://www.biostars.org/p/365825/

But lets say after running multiBamSummary, i get a table that looks like this:
```
A     1.1215
B     1.4657
C      1.3783
D      1.0529
E      0.6131
F    0.5919
G       0.9988
H   1.9739
I   1.0636
```

In order to make these samples comparable, do you have any suggestions on how do I go about downsampling everything without upsampling?
zerotimer
CC BY-SA 4.0
2019-10-15T20:53:16.823Z
At least some peak callers can accept bedGraph files, which you can normalize. The issue with normalizing BAM files is that you're quite limited in terms of fidelity (e.g., you can't remove a tenth of a read from a region).
Devon Ryan
CC BY-SA 4.0
2019-10-07T06:37:50.597Z
2019-10-07T06:37:50.597Z
Devon Ryan
<p>We are trying to use <a href="https://deeptools.readthedocs.io/en/develop/content/tools/bamCompare.html" rel="nofollow noreferrer">deeptools</a> for analysis of ATAC Seq datasets. We have datasets with different sequencing depths and are wondering if bamCompare's SES based normalization is appropriate for comparing two ATACSeq datasets as opposed to ChipSeq datasets. If such normalization is okay to perform, does it make sense to compare difference in values across comparisons? Let's say I do bamCompare on A and B using SES, and then I do bamCompare (using SES) on A and C. Will it then make sense to compare the two comparisons (A-B &amp; A-C)?</p>

<p>If such normalization isn't okay, what are the best tools to normalize ATAC-Seq datasets with different sequencing depths so that one can perform comparisons like above? 
Thanks for any suggestions or comments.</p>

<normalization><atac-seq>
Is it okay to use deeptools bamCompare (SES normalization) for comparisons across different ATAC-Seq datasets?
# Is it okay to use deeptools bamCompare (SES normalization) for comparisons across different ATAC-Seq datasets?

(Asked by: username_0 on 2019-10-07)

We are trying to use [deeptools](https://deeptools.readthedocs.io/en/develop/content/tools/bamCompare.html) for analysis of ATAC Seq datasets. We have datasets with different sequencing depths and are wondering if bamCompare's SES based normalization is appropriate for comparing two ATACSeq datasets as opposed to ChipSeq datasets. If such normalization is okay to perform, does it make sense to compare difference in values across comparisons? Let's say I do bamCompare on A and B using SES, and then I do bamCompare (using SES) on A and C. Will it then make sense to compare the two comparisons (A-B & A-C)?

If such normalization isn't okay, what are the best tools to normalize ATAC-Seq datasets with different sequencing depths so that one can perform comparisons like above?
Thanks for any suggestions or comments.

**Tags:** normalization, atac-seq

---

## 1 answers

### Answer by username_1 on 2019-10-07. Score: 1 | Accepted answer

I would be very hesitant to use SES normalization in a case like this. We recommend that people run plotFingerprint first and see if there is good separation between the samples and only then using SES. I would not expect there to be drastic separation between the samples for ATAC-seq data, so I don't think it will work well. Our standard ATAC-seq normalization is RPGC (aka, "1x normalization"). That should work well for your use-case as well.

As an aside, if you really want to make statistically-backed comparisons between groups I can recommend the CSAW package.

> **Comments:**
> **username_0**: Thanks Devon! multiBamSummary tool produces a scalingFactor table for respective BAMs. Does it make sense to first scale with these scaling factors, create bigwig using bamConverge and then use these bigwigs for comparison using bigwigcompare? I am wondering if scaled files go to bigwigcompare, will it make for a better comparison across comparisons.  
> **username_1**: Yes, that's another good way of approaching things. `bigwigCompare` does assume that the input bigWigs are scaled appropriately and the procedure you suggest will create a nicely robust comparison.  
> **username_0**: Thanks! You mentioned plotFingerprint for seeing good separation. I did the plotCorrelation heatmap. Is there some approximate threshold for "good separation"? Let's say I have a correlation between 0.5 and 0.7 across different samples. Will this make them ok for SES?  
> **username_1**: There's no substitute for plotFingerprint here.  
> **username_0**: Devon,   Can we use scalingFactor table (from multiBamSummary) to create new scaled BAMs instead of bigwig/bedgraph? I want to perform peak calling only on normalized/scaled BAMs so I can properly compare peaks/footprints across different samples. I know I can downsample using samtools but some scaling factors are greater than 1 so not sure how to "upscale" it.   Thanks again!  
> **username_0**: So  I found a post where you suggest not to upsample: https://www.biostars.org/p/365825/  But lets say after running multiBamSummary, i get a table that looks like this: ``` A     1.1215 B     1.4657 C      1.3783 D      1.0529 E      0.6131 F    0.5919 G       0.9988 H   1.9739 I   1.0636 ```  In order to make these samples comparable, do you have any suggestions on how do I go about downsampling everything without upsampling?  
> **username_1**: At least some peak callers can accept bedGraph files, which you can normalize. The issue with normalizing BAM files is that you're quite limited in terms of fidelity (e.g., you can't remove a tenth of a read from a region).  

---

