<p>This is not a very simple problem. I saved the table and &quot;test.txt&quot; and visualize it below:</p>
<pre><code>library(pheatmap)
x = read.delim(&quot;test.txt&quot;)

head(x)
  Gene.Symbol            Composite_chr_coords  Proneural      Neural
1      CDKN1B   [36.1:12:12761576-12766570:+] 0.03353670  0.03353670
2       EPB41    [36.1:1:29086215-29319545:+] 0.03882308  0.03882308
3        CLGN  [36.1:4:141529057-141568232:-] 2.12796056  0.41999847
4      PDE10A  [36.1:6:165664525-165995575:-] 1.66012916  0.25349464
5     RALGPS2  [36.1:1:176960923-177152465:+] 0.78971500 -0.01945154
6        TAF5 [36.1:10:105117714-105138812:+] 1.08354870  0.09461930
    Classical Mesenchymal
1  0.03353670 -0.84379400
2 -0.84821990  0.03882308
3  0.41999847  0.41999847
4  0.25349464  0.25349464
5 -0.01945154 -0.01945154
6  0.09461930  0.09461930
</code></pre>
<p><a href="https://i.sstatic.net/WbklC.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/WbklC.png" alt="enter image description here" /></a></p>
<p>You can see there are no so called markers genes and if you look at the scale of the data, it was normalized in microarray and it will be very hard for you to get your RNA-seq to fall into that range, and apply the medoids to identify your clusters or type.</p>
<p>Since you did not provide any data, and it is not trivial to simulate data for this, I can suggest the approach in words here:</p>
<ol>
<li><p>Subset your count table to these exact set of 840 genes.</p>
</li>
<li><p>Normalize the data and do a PCA or hierarchical clustering to see if you indeed observe 4 or 3 or 2 subtypes</p>
</li>
<li><p>If you indeed see 4 or 3 clusters this would be straight forward, take the centres of means of these clusters, and correlate with each of the sub types here. Chances are the subtype that is most correlated with one cluster, is the subtype you are interested in.</p>
</li>
</ol>

CC BY-SA 4.0
2020-07-01T22:24:24.877Z
2020-07-01T22:24:24.877Z
StupidWolf
<p>I have some RNA-seq samples from multiple glioblastoma tumours that I'm now trying to classify according to a specific gene signature (from Verhaak et al., 2010) using R. The gene signature is reported as a gene list with specific centroids for each of the 4 clusters (<a href="https://api.gdc.cancer.gov/data/941f81a1-05d7-4f84-80ec-534b8dc1ebac" rel="nofollow noreferrer">https://api.gdc.cancer.gov/data/941f81a1-05d7-4f84-80ec-534b8dc1ebac</a>). I'm wondering how I can use this signature to classify my samples in R? Would it involve some sort of k-nearest neighbours method?</p>
<p>Additionally, the signature was identified using microarray data, but I am classifying RNA-seq data. Is there any sort of adjustment I should make to the signature to account for this?</p>
<p>Thanks in advance!</p>

CC BY-SA 4.0
2020-07-01T17:28:00.703Z
Hi inteeresting post and right site to post it :-)  Coudl you summarise the genetic signature here rather than use a weblink please?
M__
CC BY-SA 4.0
2020-07-01T17:48:46.000Z
Hi Michael! Thanks for the response. The authors of the paper analyzed 200 glioblastoma samples, and used consensus average linkage hierarchical clustering to identify 4 clusters in their dataset. Next, they used ClaNC, a nearest centroid-based classifier that balances the number of genes per class, and identified signature genes for all four subtypes. The gene signature is a collection of 840 genes (210 genes per subtype). I'm not familiar with gene signatures, but for each gene they have 4 centroid values corresponding to each of the subtypes.
gb0413
<r><rna-seq><clustering>
classifying samples by TCGA signature
# classifying samples by TCGA signature

(Asked by: username_0 on 2020-07-01)

I have some RNA-seq samples from multiple glioblastoma tumours that I'm now trying to classify according to a specific gene signature (from Verhaak et al., 2010) using R. The gene signature is reported as a gene list with specific centroids for each of the 4 clusters (<https://api.gdc.cancer.gov/data/941f81a1-05d7-4f84-80ec-534b8dc1ebac>). I'm wondering how I can use this signature to classify my samples in R? Would it involve some sort of k-nearest neighbours method?

Additionally, the signature was identified using microarray data, but I am classifying RNA-seq data. Is there any sort of adjustment I should make to the signature to account for this?

Thanks in advance!

**Tags:** r, rna-seq, clustering
> **Comments:**
> **username_1**: Hi inteeresting post and right site to post it :-)  Coudl you summarise the genetic signature here rather than use a weblink please?  
> **username_0**: Hi Michael! Thanks for the response. The authors of the paper analyzed 200 glioblastoma samples, and used consensus average linkage hierarchical clustering to identify 4 clusters in their dataset. Next, they used ClaNC, a nearest centroid-based classifier that balances the number of genes per class, and identified signature genes for all four subtypes. The gene signature is a collection of 840 genes (210 genes per subtype). I'm not familiar with gene signatures, but for each gene they have 4 centroid values corresponding to each of the subtypes.  

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## 1 answers

### Answer by username_2 on 2020-07-01. Score: 1

This is not a very simple problem. I saved the table and "test.txt" and visualize it below:
library(pheatmap)
x = read.delim("test.txt")

head(x)
  Gene.Symbol            Composite_chr_coords  Proneural      Neural
1      CDKN1B   [36.1:12:12761576-12766570:+] 0.03353670  0.03353670
2       EPB41    [36.1:1:29086215-29319545:+] 0.03882308  0.03882308
3        CLGN  [36.1:4:141529057-141568232:-] 2.12796056  0.41999847
4      PDE10A  [36.1:6:165664525-165995575:-] 1.66012916  0.25349464
5     RALGPS2  [36.1:1:176960923-177152465:+] 0.78971500 -0.01945154
6        TAF5 [36.1:10:105117714-105138812:+] 1.08354870  0.09461930
    Classical Mesenchymal
1  0.03353670 -0.84379400
2 -0.84821990  0.03882308
3  0.41999847  0.41999847
4  0.25349464  0.25349464
5 -0.01945154 -0.01945154
6  0.09461930  0.09461930


You can see there are no so called markers genes and if you look at the scale of the data, it was normalized in microarray and it will be very hard for you to get your RNA-seq to fall into that range, and apply the medoids to identify your clusters or type.
Since you did not provide any data, and it is not trivial to simulate data for this, I can suggest the approach in words here:

Subset your count table to these exact set of 840 genes.

Normalize the data and do a PCA or hierarchical clustering to see if you indeed observe 4 or 3 or 2 subtypes

If you indeed see 4 or 3 clusters this would be straight forward, take the centres of means of these clusters, and correlate with each of the sub types here. Chances are the subtype that is most correlated with one cluster, is the subtype you are interested in.


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