Hi,

Given,I have a gene expression dataset. I want to find out, all the genes that are highly correlated with the given gene/ have the same expression pattern.

Example, if there would be an R package, it should take one input as expression data matrix and another input as gene of interest and should provide an output of a list of genes, that could have same expression pattern or are highly correlated.

I know that I could write a function in R regarding this and filter the genes on the basis of their correlation values and take into account the cor value more than 0.5. But, it takes so much of time, when you have a data with more than 20,000 rows and 1,000 columns.
Using the function below is pretty fast using 1500 columns and 30k rows

```
mat <- replicate(1500, rnorm(30000)) 
gene <- mat[sample(1:30000,1),]

system.time({
resp <- c()
est <- c()
for (i in 1:30000){
  x <- cor.test(gene, mat[i,])
  if (abs(x$estimate[[1]]) > 0.5){
    resp <- c(resp, x$p.value)
    est <- c(est, x$estimate[[1]])
  }
}
})

 user  system elapsed 
   7.69    0.02    7.71
```