<p>If we look at <a href="https://hbctraining.github.io/DGE_workshop/lessons/05_DGE_DESeq2_analysis2.html" rel="nofollow noreferrer">documentation of DESEQ2</a> and search &quot;adjusted p-value&quot;, we find the section &quot;Multiple test correction&quot;. In this section they discuss Benjamini-Hochberg (&quot;BH&quot;) false discovery rate (FDR) correction procedures:</p>
<blockquote>
<p>FDR/Benjamini-Hochberg: Benjamini and Hochberg (1995) defined the concept of FDR and created an algorithm to control the expected FDR below a specified level given a list of independent p-values. <strong>An interpretation of the BH method for controlling the FDR is implemented in DESeq2 in which we rank the genes by p-value, then multiply each ranked p-value by m/rank.</strong></p>
</blockquote>
<p>Earlier, they write:</p>
<blockquote>
<p>m = total number of tests</p>
</blockquote>
<p>They further add:</p>
<blockquote>
<p>So what does FDR &lt; 0.05 mean? By setting the FDR cutoff to &lt; 0.05, we’re saying that the proportion of false positives we expect amongst our differentially expressed genes is 5%. For example, if you call 500 genes as differentially expressed with an FDR cutoff of 0.05, you expect 25 of them to be false positives.</p>
</blockquote>
<p>I would suggest reading Benjamini and Hochberg (1995) to learn more details if desired.</p>

CC BY-SA 4.0
2021-08-12T03:50:14.363Z
2021-08-12T03:50:14.363Z
Maximilian Press
<p>What does the padj really mean? I believe it's a BH method, where it attempts to quantify the likelihood of a &quot;false positive&quot; but I am not sure. Also, what does it mean if the resulting calculations nearly all have the same padj values despite having different p-values?</p>

CC BY-SA 4.0
2021-08-12T03:45:56.150Z
It would help to see the data themselves (p-values and adjusted p-values), or at least a general idea of how many hypotheses and the part of the p-value spectrum. For high p-values FDRs ("adjusted p-values", https://en.wikipedia.org/wiki/False_discovery_rate) will be very similar as they will mostly indicate support for the null hypothesis, i.e. very high false discovery rates. I would suggest reading about DESeq2 stats (section "Multiple test correction"): https://hbctraining.github.io/DGE_workshop/lessons/05_DGE_DESeq2_analysis2.html
Maximilian Press
<r><deseq2><p-values>
Adjusted P-value from DESeq2
# Adjusted P-value from DESeq2

(Asked by: username_0 on 2021-08-11)

What does the padj really mean? I believe it's a BH method, where it attempts to quantify the likelihood of a "false positive" but I am not sure. Also, what does it mean if the resulting calculations nearly all have the same padj values despite having different p-values?

**Tags:** r, deseq2, p-values
> **Comments:**
> **username_1**: It would help to see the data themselves (p-values and adjusted p-values), or at least a general idea of how many hypotheses and the part of the p-value spectrum. For high p-values FDRs ("adjusted p-values", https://en.wikipedia.org/wiki/False_discovery_rate) will be very similar as they will mostly indicate support for the null hypothesis, i.e. very high false discovery rates. I would suggest reading about DESeq2 stats (section "Multiple test correction"): https://hbctraining.github.io/DGE_workshop/lessons/05_DGE_DESeq2_analysis2.html  

---

## 1 answers

### Answer by username_1 on 2021-08-12. Score: 1

If we look at [documentation of DESEQ2](https://hbctraining.github.io/DGE_workshop/lessons/05_DGE_DESeq2_analysis2.html) and search "adjusted p-value", we find the section "Multiple test correction". In this section they discuss Benjamini-Hochberg ("BH") false discovery rate (FDR) correction procedures:

> FDR/Benjamini-Hochberg: Benjamini and Hochberg (1995) defined the concept of FDR and created an algorithm to control the expected FDR below a specified level given a list of independent p-values. **An interpretation of the BH method for controlling the FDR is implemented in DESeq2 in which we rank the genes by p-value, then multiply each ranked p-value by m/rank.**

Earlier, they write:

> m = total number of tests

They further add:

> So what does FDR < 0.05 mean? By setting the FDR cutoff to < 0.05, we’re saying that the proportion of false positives we expect amongst our differentially expressed genes is 5%. For example, if you call 500 genes as differentially expressed with an FDR cutoff of 0.05, you expect 25 of them to be false positives.

I would suggest reading Benjamini and Hochberg (1995) to learn more details if desired.


---

