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copied!<p><strong>UPDATED BELOW TO SHOW PARALLEL PROCESSING - ABOUT A 60% SAVING</strong></p> <p>Using <code>apply()</code> might not be quick enough for you. Here's how to do it, though. Will have a think about performance since this example (1M output correlations in 1000x1000 grid) takes over a minute on laptop. </p> <pre><code>miRdf=matrix(rnorm(13000,10,1),ncol=13) mRNAdf=matrix(rnorm(13000,10,1),ncol=13) miRdf[,1]<-1:nrow(miRdf) # using column 1 as indices since they're not in the calc. mRNAdf[,1]<-1:nrow(mRNAdf) corRow<-function(y){ apply(miRdf,1,function(x)cor(as.numeric(x[2:13]), as.numeric(mRNAdf[y,2:13]), use ="complete.obs")) } system.time(apply(mRNAdf,1,function(x)corRow(x[1]))) # user system elapsed # 72.94 0.00 73.39 </code></pre> <p>And with <code>parallel::parApply</code> on a 4 core Win64 laptop</p> <pre><code>require(parallel) ## Library to allow parallel processing miRdf=matrix(rnorm(13000,10,1),ncol=13) mRNAdf=matrix(rnorm(13000,10,1),ncol=13) miRdf[,1]<-1:nrow(miRdf) # using column 1 as indices since they're not in the calc. mRNAdf[,1]<-1:nrow(mRNAdf) corRow<-function(y){ apply(miRdf,1,function(x)cor(as.numeric(x[2:13]), as.numeric(mRNAdf[y,2:13]), use ="complete.obs")) } # Make a cluster from all available cores cl=makeCluster(detectCores()) # Use clusterExport() to distribute the function and data.frames needed in the apply() call clusterExport(cl,c("corRow","miRdf","mRNAdf")) # time the call system.time(parApply(cl,mRNAdf,1,function(x)corRow(x[[1]]))) # Stop the cluster stopCluster(cl) # time the call without clustering system.time(apply(mRNAdf,1,function(x)corRow(x[[1]]))) ## WITH CLUSTER (4) user system elapsed 0.04 0.03 29.94 ## WITHOUT CLUSTER user system elapsed 73.96 0.00 74.46 </code></pre>

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