<p>The figure caption says "the top 40 genes per celltype" - so we can simply use the code you provided for the top 40 genes for a single celltype, repeat this for all celltypes, and then remove any duplicate genes. Here's an example:</p>

<pre><code>&gt; expr &lt;- matrix(runif(9*24000), nrow=24000, ncol=9)
&gt; top_genes_each_celltype &lt;- sapply(1:ncol(expr), function(i) order(expr[,i],decreasing =T)[1:40])
&gt; dim(top_genes_each_celltype)
[1] 40 9
&gt; top_genes_all_celltypes &lt;- unique(as.vector(top_genes_each_celltype))
&gt; length(top_genes_all_celltypes)
[1] 358
</code></pre>

<p>Then you can build a biclustered heatmap with <code>expr[top_genes_all_celltypes,]</code>, for example, using <code>heatmap2</code>.</p>

CC BY-SA 4.0
2018-10-23T15:07:38.283Z
2018-10-23T15:07:38.283Z
Scott Gigante
<p>I was wondering if some one can help me with pseudo r code for my approach here based on this <a href="https://www.researchgate.net/publication/265550961_An_RNA-Sequencing_Transcriptome_and_Splicing_Database_of_Glia_Neurons_and_Vascular_Cells_of_the_Cerebral_Cortex" rel="noreferrer">paper</a> to plot a heatmap</p>
<h1>Figure 3</h1>
<p><a href="https://i.sstatic.net/Hna8E.png" rel="noreferrer"><img src="https://i.sstatic.net/Hna8E.png" alt="enter image description here" /></a></p>
<p><strong>Figure description</strong>
The top 40 enriched genes percelltypeare shown in a heat map.Onlyhighlyexpressed genes with FPKM ⬎20 areincludedin this analysis. Fold enrichment iscalculatedasFPKM of one cell type divided by the average FPKM of all other cell types. The majority of these genes showed specific expression by only one cell type, with the exception that some are expressed during more than one maturation stage in the oligodendrocyte lineage.</p>
<p><strong>Similar description in their methods:</strong>
Differential expression was calculated as the FPKM of a given cell type
divided by the average FPKM of all other cell types. Genes were ranked by
their fold enrichment in each cell type, and top enriched genes for each
cell type were identified</p>
<p>In my case I have 9 cell types(columns) and ~24000 genes(rows) from mouse and the values are TPMs.</p>
<p>I know to order celltype only based on one column like this</p>
<pre><code>expr.order = expr[order(expr[,1],decreasing =T),]
</code></pre>
<p>Which clearly not the right way and would need some help with code in r</p>

CC BY-SA 3.0
2017-09-20T15:54:17.540Z
It’s unclear from your text what you actually want to do: *there’s no question*. For ordering the expression values based on one column you already found the solution. If that’s not what you mean you need to be more specific.
Konrad Rudolph
CC BY-SA 3.0
2017-09-20T16:37:11.010Z
I do not know how to achieve this *Genes were ranked by their fold enrichment in each cell type*  which I guess means rank individual column independently not sure how to do this
novicebioinforesearcher
CC BY-SA 3.0
2017-09-21T11:23:41.567Z
You can't rank individual columns independently (of the same data), but you can order them by several factors.
llrs
CC BY-SA 3.0
2017-09-21T12:03:33.910Z
Well in that case you first need to compute fold changes using one of the existing differential expression packages, e.g. DESeq2 or edgeR.
Konrad Rudolph
CC BY-SA 3.0
2017-09-24T20:52:49.630Z
BTW, what else have you tried with R? Did you compute the fold change and found the top 40 as described in the image?
llrs
<r><rna-seq>
cell type specific genes - heatmap using rank based approach
# cell type specific genes - heatmap using rank based approach

(Asked by: username_0 on 2017-09-20)

I was wondering if some one can help me with pseudo r code for my approach here based on this paper to plot a heatmap
Figure 3

Figure description
The top 40 enriched genes percelltypeare shown in a heat map.Onlyhighlyexpressed genes with FPKM ⬎20 areincludedin this analysis. Fold enrichment iscalculatedasFPKM of one cell type divided by the average FPKM of all other cell types. The majority of these genes showed specific expression by only one cell type, with the exception that some are expressed during more than one maturation stage in the oligodendrocyte lineage.
Similar description in their methods:
Differential expression was calculated as the FPKM of a given cell type
divided by the average FPKM of all other cell types. Genes were ranked by
their fold enrichment in each cell type, and top enriched genes for each
cell type were identified
In my case I have 9 cell types(columns) and ~24000 genes(rows) from mouse and the values are TPMs.
I know to order celltype only based on one column like this
expr.order = expr[order(expr[,1],decreasing =T),]

Which clearly not the right way and would need some help with code in r

**Tags:** r, rna-seq
> **Comments:**
> **username_1**: It’s unclear from your text what you actually want to do: *there’s no question*. For ordering the expression values based on one column you already found the solution. If that’s not what you mean you need to be more specific.  
> **username_0**: I do not know how to achieve this *Genes were ranked by their fold enrichment in each cell type*  which I guess means rank individual column independently not sure how to do this  
> **username_2**: You can't rank individual columns independently (of the same data), but you can order them by several factors.  
> **username_1**: Well in that case you first need to compute fold changes using one of the existing differential expression packages, e.g. DESeq2 or edgeR.  
> **username_2**: BTW, what else have you tried with R? Did you compute the fold change and found the top 40 as described in the image?  

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## 1 answers

### Answer by username_3 on 2018-10-23. Score: 2 | Accepted answer

The figure caption says "the top 40 genes per celltype" - so we can simply use the code you provided for the top 40 genes for a single celltype, repeat this for all celltypes, and then remove any duplicate genes. Here's an example:
> expr <- matrix(runif(9*24000), nrow=24000, ncol=9)
> top_genes_each_celltype <- sapply(1:ncol(expr), function(i) order(expr[,i],decreasing =T)[1:40])
> dim(top_genes_each_celltype)
[1] 40 9
> top_genes_all_celltypes <- unique(as.vector(top_genes_each_celltype))
> length(top_genes_all_celltypes)
[1] 358

Then you can build a biclustered heatmap with expr[top_genes_all_celltypes,], for example, using heatmap2.


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