<p>If you want the values by group then subset <code>y</code> to contain the samples of interest and then feed that to <code>aveLogCPM()</code>.</p>

CC BY-SA 4.0
2018-06-22T12:53:18.537Z
yes I took the matrix of only Tumor samples and genes with raw counts and applied aveLogCPM()
stack_learner
CC BY-SA 4.0
2018-06-22T12:30:23.773Z
2018-06-22T12:30:23.773Z
Devon Ryan
<p>This post on the Bioconductor mailing list explains the difference: <a href="https://support.bioconductor.org/p/100463/" rel="nofollow noreferrer">https://support.bioconductor.org/p/100463/</a></p>

CC BY-SA 4.0
2023-09-05T12:05:49.573Z
Thanks for your response. Could kindly provide a summary of the link here? Link answers are generally not encouraged.
M__
CC BY-SA 4.0
2024-02-12T09:43:52.077Z
In short, Aaron Lun explains that the output of aveLogCPM is closer to
`log2(rowMeans(cpm(y))` (where the log-transformation is done after the averaging), than to:
`rowMeans(cpm(y, log=TRUE))`
jul635
CC BY-SA 4.0
2023-09-05T08:07:19.447Z
2023-09-05T08:07:19.447Z
jul635
<p>Using edgeR for differential analysis between Tumor and Normal gave me differential expressed genes with logFC, logCPM, PValue and FDR. </p>

<p>From the details of glmTreat function I see that logCPM is average log2-counts per million, the average taken over all libraries.</p>

<p>The output table I got from glmTreat:</p>

<pre><code>Geneid     logFC  unshrunk.logFC    logCPM  PValue       FDR
Gene1   4.985298235 5.573931983 1.236660486 1.55E-54    1.87E-50
Gene2   4.613438634 5.52126484  0.920343233 2.59E-53    1.56E-49
Gene3   5.250601296 5.356432653 2.751294034 2.48E-50    9.95E-47
Gene4   4.943049159 5.379741165 1.361393757 2.06E-45    6.19E-42
Gene5   6.100121754 6.117580436 5.774385315 2.95E-43    7.11E-40
Gene6   4.722697891 5.320461275 1.120685402 3.05E-42    6.11E-39
Gene7   5.246129853 5.497012001 1.902992053 2.78E-40    4.78E-37
Gene8   3.878773277 4.956636276 0.776208741 8.66E-39    1.30E-35
Gene9   4.441752496 4.94930499  1.132652682 4.37E-38    5.83E-35
</code></pre>

<p>To calculate logCPM given in the above table manually I did like this:</p>

<pre><code>logCPM &lt;- cpm(y, prior.count=2,  log=TRUE)
</code></pre>

<p>I got the logCPM values for all the genes. Then I calculated Average across all the samples for each gene. I got like below:</p>

<pre><code>Geneid  Average
Gene1   0.686560246
Gene2   0.617115826
Gene3   1.075975225
Gene4   0.692050878
Gene5   1.277556065
Gene6   0.638358189
Gene7   0.689323163
Gene8   0.60700396
Gene9   0.662115092
</code></pre>

<p>Why there is so difference? Where I'm doing wrong?</p>

CC BY-SA 4.0
2018-06-22T11:15:18.873Z
Did you also try `aveLogCPM(y, normalized.lib.sizes=TRUE, prior.count=2, dispersion=NULL, ...)`?
benn
CC BY-SA 4.0
2018-06-22T11:18:44.683Z
`glmTreat()` literally uses `aveLogCPM` under the hood in fact.
Devon Ryan
CC BY-SA 4.0
2018-06-22T11:49:58.303Z
@DevonRyan Yes you are right. thanks. It is calculated across all samples. I want to calculate aveLogCPM for each condition differently (Tumor and Normal separately) How to do that? Just taking the raw counts of Tumor condition and using aveLogCPM function on it is fine?
stack_learner
<r><rna-seq><bioconductor><rna><edger>
How to calculate logCPM across all samples?
# How to calculate logCPM across all samples?

(Asked by: username_0 on 2018-06-22)

Using edgeR for differential analysis between Tumor and Normal gave me differential expressed genes with logFC, logCPM, PValue and FDR. 
From the details of glmTreat function I see that logCPM is average log2-counts per million, the average taken over all libraries.
The output table I got from glmTreat:
Geneid     logFC  unshrunk.logFC    logCPM  PValue       FDR
Gene1   4.985298235 5.573931983 1.236660486 1.55E-54    1.87E-50
Gene2   4.613438634 5.52126484  0.920343233 2.59E-53    1.56E-49
Gene3   5.250601296 5.356432653 2.751294034 2.48E-50    9.95E-47
Gene4   4.943049159 5.379741165 1.361393757 2.06E-45    6.19E-42
Gene5   6.100121754 6.117580436 5.774385315 2.95E-43    7.11E-40
Gene6   4.722697891 5.320461275 1.120685402 3.05E-42    6.11E-39
Gene7   5.246129853 5.497012001 1.902992053 2.78E-40    4.78E-37
Gene8   3.878773277 4.956636276 0.776208741 8.66E-39    1.30E-35
Gene9   4.441752496 4.94930499  1.132652682 4.37E-38    5.83E-35

To calculate logCPM given in the above table manually I did like this:
logCPM <- cpm(y, prior.count=2,  log=TRUE)

I got the logCPM values for all the genes. Then I calculated Average across all the samples for each gene. I got like below:
Geneid  Average
Gene1   0.686560246
Gene2   0.617115826
Gene3   1.075975225
Gene4   0.692050878
Gene5   1.277556065
Gene6   0.638358189
Gene7   0.689323163
Gene8   0.60700396
Gene9   0.662115092

Why there is so difference? Where I'm doing wrong?

**Tags:** r, rna-seq, bioconductor, rna, edger
> **Comments:**
> **username_1**: Did you also try `aveLogCPM(y, normalized.lib.sizes=TRUE, prior.count=2, dispersion=NULL, ...)`?  
> **username_2**: `glmTreat()` literally uses `aveLogCPM` under the hood in fact.  
> **username_0**: @DevonRyan Yes you are right. thanks. It is calculated across all samples. I want to calculate aveLogCPM for each condition differently (Tumor and Normal separately) How to do that? Just taking the raw counts of Tumor condition and using aveLogCPM function on it is fine?  

---

## 2 answers

### Answer by username_2 on 2018-06-22. Score: 3

If you want the values by group then subset `y` to contain the samples of interest and then feed that to `aveLogCPM()`.

> **Comments:**
> **username_0**: yes I took the matrix of only Tumor samples and genes with raw counts and applied aveLogCPM()  

---

### Answer by username_3 on 2023-09-05. Score: 0

This post on the Bioconductor mailing list explains the difference: <https://support.bioconductor.org/p/100463/>

> **Comments:**
> **username_4**: Thanks for your response. Could kindly provide a summary of the link here? Link answers are generally not encouraged.  
> **username_3**: In short, Aaron Lun explains that the output of aveLogCPM is closer to `log2(rowMeans(cpm(y))` (where the log-transformation is done after the averaging), than to: `rowMeans(cpm(y, log=TRUE))`  

---

