<p>How it is define in terms if accessibility. For example in expression data when I compare A vs B either A or B as reference we define that genes are getting differentially expressed to A or B with our choice of cotrol.</p>
<p>Similarly in ATAC seq data when I'm comparing two stages how do i define the stage specific peaks? Such as set of peaks are only for A and other for B?</p>
<p>So far what i have done is I get a consensus peaks then from those peaks I get read-count and from those read count I do run deseq2 on those accessible data.</p>
<p>Can I do the same as I do for RNA seq data ,and compare peaks of A vs B using deseq2 and say if a peak is differentially accessible then it can be considered as that condition specific peaks.</p>
<p>Or</p>
<p>There is other way of finding out condition or stage specific peaks ?
&quot;Identification of tissue-specific chromatin accessible regions</p>
<blockquote>
<p>We used a strategy described previously based on the Shannon entropy
to compute a tissue specificity index for each peak4,36,37.
Specifically, for each peak, we defined its relative accessibility in
a tissue type i as Ri = Ei/ΣE, where Ei is the RPM value for the peak
in the tissue i, ΣE is the sum of RPM values in all tissues, and N is
the total number of tissues. The entropy score for each peak across
tissues can be defined as H = −1 * sum(Ri * log2Ri) (1 &lt; i &lt; N), where
the value of H ranges between 0 to log2(N). An entropy score close to
zero indicates the accessibility of this peak is highly
tissue-specific, while an entropy score close to log2(N) indicates
that this peak is ubiquitously accessible38. Based on the distribution
of entropy scores, peaks with score less than 3.5 were selected as
tissue-restricted peaks.&quot;</p>
</blockquote>
<p>The above is one of the way they saying tissue specific peaks from this <a href="https://www.nature.com/articles/s41597-019-0071-0" rel="nofollow noreferrer">paper</a></p>
<p>Any suggestion would be really appreciated</p>

CC BY-SA 4.0
2021-01-18T21:24:34.803Z
Differential analysis like DESeq2 and limma have been successfully applied to ATAC-seq data. The method you quoted above seem to be too fancy.
Phoenix Mu
CC BY-SA 4.0
2021-01-19T05:43:51.650Z
i would prefer simple one .  But my question regarding finding peak specific condition or loss or gain of peaks between two condition, can it be addressed through the differential analysis ? I'm just not clear how a loss or gain can be shown in accessibility data through differential analysis
kcm
CC BY-SA 4.0
2021-01-19T16:42:02.330Z
But you mentioned consensus peak set in your question above, so in this way loss or gain can also be defined by differential. condition-specific peaks do not have to be binary loss or gain. Loss or gain may be intuitive for ATAC-seq peaks but the concept itself is confounded by sequencing depth and peak-calling methods, for instance. May be you can do differential analysis first, and choose the peaks that show large fold change, and go back to check if the peak is called in both or one condition.
Phoenix Mu
CC BY-SA 4.0
2021-01-20T05:06:38.110Z
okay "so in this way loss or gain can also be defined by differential." so is similar what is done for RNA seq lets say A vs B , i did differential analysis so how do i say that some region are specific to A or specific to B  ?
kcm
<atac-seq>
Peak Specific to condition in ATAC seq data
# Peak Specific to condition in ATAC seq data

(Asked by: username_0 on 2021-01-18)

How it is define in terms if accessibility. For example in expression data when I compare A vs B either A or B as reference we define that genes are getting differentially expressed to A or B with our choice of cotrol.

Similarly in ATAC seq data when I'm comparing two stages how do i define the stage specific peaks? Such as set of peaks are only for A and other for B?

So far what i have done is I get a consensus peaks then from those peaks I get read-count and from those read count I do run deseq2 on those accessible data.

Can I do the same as I do for RNA seq data ,and compare peaks of A vs B using deseq2 and say if a peak is differentially accessible then it can be considered as that condition specific peaks.

Or

There is other way of finding out condition or stage specific peaks ?
"Identification of tissue-specific chromatin accessible regions

> We used a strategy described previously based on the Shannon entropy
> to compute a tissue specificity index for each peak4,36,37.
> Specifically, for each peak, we defined its relative accessibility in
> a tissue type i as Ri = Ei/ΣE, where Ei is the RPM value for the peak
> in the tissue i, ΣE is the sum of RPM values in all tissues, and N is
> the total number of tissues. The entropy score for each peak across
> tissues can be defined as H = −1 \* sum(Ri \* log2Ri) (1 < i < N), where
> the value of H ranges between 0 to log2(N). An entropy score close to
> zero indicates the accessibility of this peak is highly
> tissue-specific, while an entropy score close to log2(N) indicates
> that this peak is ubiquitously accessible38. Based on the distribution
> of entropy scores, peaks with score less than 3.5 were selected as
> tissue-restricted peaks."

The above is one of the way they saying tissue specific peaks from this [paper](https://www.nature.com/articles/s41597-019-0071-0)

Any suggestion would be really appreciated

**Tags:** atac-seq
> **Comments:**
> **username_1**: Differential analysis like DESeq2 and limma have been successfully applied to ATAC-seq data. The method you quoted above seem to be too fancy.  
> **username_0**: i would prefer simple one .  But my question regarding finding peak specific condition or loss or gain of peaks between two condition, can it be addressed through the differential analysis ? I'm just not clear how a loss or gain can be shown in accessibility data through differential analysis  
> **username_1**: But you mentioned consensus peak set in your question above, so in this way loss or gain can also be defined by differential. condition-specific peaks do not have to be binary loss or gain. Loss or gain may be intuitive for ATAC-seq peaks but the concept itself is confounded by sequencing depth and peak-calling methods, for instance. May be you can do differential analysis first, and choose the peaks that show large fold change, and go back to check if the peak is called in both or one condition.  
> **username_0**: okay "so in this way loss or gain can also be defined by differential." so is similar what is done for RNA seq lets say A vs B , i did differential analysis so how do i say that some region are specific to A or specific to B  ?  

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## 0 answers

