For a problem like this you want to look into the `apply`

function in R. This function will let you perform a function row-wise or column-wise on a dataframe or matrix.

From the help menu: `apply(X, MARGIN, FUN, ...)`

where `X`

is your dataframe/matrix, `MARGIN`

is either `1`

for row-wise or `2`

for column-wise, and `FUN`

is the function that you want to perform. Depending on what you want to do, you can have a base `FUN`

such as `median`

or `sum`

, or you can define your own `function(x)`

, where `x`

is each row (or column) in your dataframe.

So for the example of dataframe `df`

where columns 2-4 are Malignant and 5-7 are fibroblast you can run:

```
pValues <- apply(df, 1, function(x) t.test(x[2:4],x[5:7])$p.value)
```

This will take `df`

and for each row (indicated by the `1`

, as opposed to each column) it will perform `function(x)`

, whereby a t-test is performed on the elements 2-4 compared to 5-7, and the p-value is reported (hence the `$p.value`

). This will perform that function for each row, and store the p-values in the vector `pValues`

.

If you are getting this data from raw RNA-Seq data your best bet is to use a well established method like DESeq2 or edgeR or limma.

1.6kEven if you do not have the raw data it is a much better solution to use limma via its trend functionality! Those values you post are mostly likely not normal distributed so you should NOT use a t-test!

3.2kHi, I applied this line pValues <- apply(df, 1, function(x) t.test(x[2:4],x[5:7])$p.value)

But I got the following error Error in if (stderr < 10 * .Machine$double.eps * max(abs(mx), abs(my))) stop("data are essentially constant") : missing value where TRUE/FALSE needed

Can someone shed some light on this? Many Thanks Chris

Here is my my data https://drive.google.com/open?id=1LiJD7T6oR5MtABwYqkhUrJFfo7XRxJ_z

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