text: user9160: I am stuck on how to do correlation for two independent data sets with common row and column names.
  A and B are datasets that contain as many rows as genes and as many columns as samples.
  The rows in A and B represent a common set of genes but measured in two different tissues.
  The columns represent measurements in the same 5 samples in both A and B.
  I want to do a correlation between the set of genes in A and B. This is to see if the same genes in both tissues are correlated or not.
  Since the matrix would be big in my actual data, I only want to retain a correlation coefficient higher than 0.5.
  Here I simulate the data set.
  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("M_", 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("I_", 1:5)
  
  Thank you!
  
  user3541: You can use mapply(). ta and tb being transposed data frames of your A and B data frames respectively:
  > mapply(cor, ta, tb)[mapply(cor, ta, tb) > 0.5]
  
        G_3       G_5       G_9      G_10      G_11      G_15      G_20      G_23      G_25      G_26      G_33      G_40      G_43      G_48      G_57      G_60 
  0.5346591 0.8066507 0.8379777 0.6752681 0.7221359 0.5285787 0.7333045 0.5627962 0.6533379 0.7256878 0.5996492 0.6486557 0.5108215 0.7386332 0.6596823 0.6919915 
       G_63      G_72      G_76      G_80      G_81      G_90      G_97      G_98      G_99 
  0.5589583 0.8391917 0.7608801 0.8003665 0.6364557 0.5030968 0.7298439 0.5693024 0.5709411 
  
  
original_text: 9160: <p>I am stuck on how to do correlation for two independent data sets with common row and column names.
  A and B are datasets that contain as many rows as genes and as many columns as samples.
  The rows in A and B represent a common set of genes but measured in two different tissues.
  The columns represent measurements in the same 5 samples in both A and B.
  I want to do a correlation between the set of genes in A and B. This is to see if the same genes in both tissues are correlated or not.
  Since the matrix would be big in my actual data, I only want to retain a correlation coefficient higher than 0.5.</p>
  <p>Here I simulate the data set.</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;M_&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;I_&quot;, 1:5)
  </code></pre>
  <p>Thank you!</p>
   3541: <p>You can use <code>mapply()</code>. <code>ta</code> and <code>tb</code> being transposed data frames of your <code>A</code> and <code>B</code> data frames respectively:</p>
  <pre><code>&gt; mapply(cor, ta, tb)[mapply(cor, ta, tb) &gt; 0.5]
  
        G_3       G_5       G_9      G_10      G_11      G_15      G_20      G_23      G_25      G_26      G_33      G_40      G_43      G_48      G_57      G_60 
  0.5346591 0.8066507 0.8379777 0.6752681 0.7221359 0.5285787 0.7333045 0.5627962 0.6533379 0.7256878 0.5996492 0.6486557 0.5108215 0.7386332 0.6596823 0.6919915 
       G_63      G_72      G_76      G_80      G_81      G_90      G_97      G_98      G_99 
  0.5589583 0.8391917 0.7608801 0.8003665 0.6364557 0.5030968 0.7298439 0.5693024 0.5709411 
  </code></pre>
  