<p>I don't think is a good idea to apply the gene selection method on the ATAC-seq genomic regions because of the high sparsity of ATAC-seq data.</p>
<p>A common way is to select the most frequent regions (like the <a href="https://satijalab.org/signac/reference/findtopfeatures" rel="nofollow noreferrer">Signac</a> does), or to discard the house-keeping regions (like the <a href="https://github.com/r3fang/SnapATAC/blob/master/examples/10X_brain_5k/README.md" rel="nofollow noreferrer">SnapATAC</a> does).</p>
<p>And you can also use the <a href="https://episcanpy.readthedocs.io/en/latest/api/episcanpy.pp.select_var_feature.html" rel="nofollow noreferrer">episcanpy</a>, a epigenomics version of scanpy.</p>

CC BY-SA 4.0
2022-08-16T16:57:39.287Z
Thanks for your answer. I guess in my case the cells are grouped per cell type (the data is aggregated per cell type), so sparsity shouldn't be an issue. Given this, is it still inappropriate to use gene selection methods?
An Ignorant Wanderer
CC BY-SA 4.0
2022-08-17T12:05:17.483Z
If you think the sparsity is not an issue for your data, then `scanpy.pp.highly_variable_genes` may be applicable. Just be aware that the default thresholds for `mean` and `disp` are for single-cell RNA-seq data.
Janus
CC BY-SA 4.0
2022-08-16T14:04:13.520Z
2022-08-16T14:09:34.267Z
2022-08-16T14:09:34.267Z
Janus
<p>I'm working with ATACSeq data from multiple tissue/cell types. The data is binned in 1 megabase bins. I'd like to identify the bins that are &quot;highly variable&quot; across the different tissue types. I can't seem to find a tool to do this, like there exists for identifying highly variables genes in a gene expression setting (e.g Scanpy's <code>scanpy.pp.highly_variable_genes</code>). I was wondering if I could use the gene selection tools for my data, and if there are any other suggestions?</p>

<atac-seq>
Can I use a tool for highly variable gene selection for selecting highly variable "bins" from an ATACSeq dataset?
# Can I use a tool for highly variable gene selection for selecting highly variable "bins" from an ATACSeq dataset?

(Asked by: username_0 on 2022-08-10)

I'm working with ATACSeq data from multiple tissue/cell types. The data is binned in 1 megabase bins. I'd like to identify the bins that are "highly variable" across the different tissue types. I can't seem to find a tool to do this, like there exists for identifying highly variables genes in a gene expression setting (e.g Scanpy's `scanpy.pp.highly_variable_genes`). I was wondering if I could use the gene selection tools for my data, and if there are any other suggestions?

**Tags:** atac-seq

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## 1 answers

### Answer by username_1 on 2022-08-16. Score: 0 | Accepted answer

I don't think is a good idea to apply the gene selection method on the ATAC-seq genomic regions because of the high sparsity of ATAC-seq data.

A common way is to select the most frequent regions (like the [Signac](https://satijalab.org/signac/reference/findtopfeatures) does), or to discard the house-keeping regions (like the [SnapATAC](https://github.com/r3fang/SnapATAC/blob/master/examples/10X_brain_5k/README.md) does).

And you can also use the [episcanpy](https://episcanpy.readthedocs.io/en/latest/api/episcanpy.pp.select_var_feature.html), a epigenomics version of scanpy.

> **Comments:**
> **username_0**: Thanks for your answer. I guess in my case the cells are grouped per cell type (the data is aggregated per cell type), so sparsity shouldn't be an issue. Given this, is it still inappropriate to use gene selection methods?  
> **username_1**: If you think the sparsity is not an issue for your data, then `scanpy.pp.highly_variable_genes` may be applicable. Just be aware that the default thresholds for `mean` and `disp` are for single-cell RNA-seq data.  

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