<p>I am studying about tissue cross-talk using gene expression data. My first goal is to identify the genes responsible for communication signaling(cross talk).  Let say the tissues are fat and muscle.
One thing that came to my mind was doing correlation analysis and visualizing using a heatmap.
Here I would like to get your help with the following points:
How to extract the one where both tissues showing a possible correlation from the four possible quadrants of the correlation matrix?
How best I can visualize that quadrant using heatmap or any other plotting methods.
Here I simulated the data as follows. Rows are genes and columns as samples.</p>
<pre><code>set.seed(1)
A &lt;- data.frame(rnorm(100), 
                rnorm(100),
                rnorm(100), 
                rnorm(100),
                rnorm(100))
row.names(A) &lt;- paste0(&quot;G_&quot;, 1:100)
colnames(A) &lt;- paste0(&quot;S_&quot;, 1:5)

set.seed(42)  
B &lt;- data.frame(rnorm(100), 
                rnorm(100),
                rnorm(100), 
                rnorm(100),
                rnorm(100))
row.names(B) &lt;- paste0(&quot;G_&quot;, 1:100)
colnames(B) &lt;- paste0(&quot;S_&quot;, 1:5)
</code></pre>
<p>Tank you!</p>

CC BY-SA 4.0
2020-10-16T09:49:11.280Z
@ATpoint; I know it is tricky. I plan to do proteomics, but this is to get an idea for proteomics experiments.
data
<r><gene><correlation>
tissue cross talk using gene expression data
# tissue cross talk using gene expression data

(Asked by: username_0 on 2020-10-16)

I am studying about tissue cross-talk using gene expression data. My first goal is to identify the genes responsible for communication signaling(cross talk).  Let say the tissues are fat and muscle.
One thing that came to my mind was doing correlation analysis and visualizing using a heatmap.
Here I would like to get your help with the following points:
How to extract the one where both tissues showing a possible correlation from the four possible quadrants of the correlation matrix?
How best I can visualize that quadrant using heatmap or any other plotting methods.
Here I simulated the data as follows. Rows are genes and columns as samples.
set.seed(1)
A <- data.frame(rnorm(100), 
                rnorm(100),
                rnorm(100), 
                rnorm(100),
                rnorm(100))
row.names(A) <- paste0("G_", 1:100)
colnames(A) <- paste0("S_", 1:5)

set.seed(42)  
B <- data.frame(rnorm(100), 
                rnorm(100),
                rnorm(100), 
                rnorm(100),
                rnorm(100))
row.names(B) <- paste0("G_", 1:100)
colnames(B) <- paste0("S_", 1:5)

Tank you!

**Tags:** r, gene, correlation
> **Comments:**
> **username_0**: @ATpoint; I know it is tricky. I plan to do proteomics, but this is to get an idea for proteomics experiments.  

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## 0 answers

