I'm running the pathifier approach against C2 pathway curated database for a specific microarray dataset.

As I was reading the documentation of the pathifier, in order to configure it properly for my dataset, I saw that for the min_std argument they suggest to use the technical noise.

min_std: The minimal allowed standard deviation of each gene. Genes with lower standard deviation are divided by min_std instead of their actual standard deviation. (Recommended: set min_std to be the technical noise).

But how am I going to calculate it ? At first run I just used the value 0.2254005 from the example they provide with that package, but this might not be the right for my dataset.


So from here it says: The minimal standard error (min_std) was set as the first quartile of the standard deviation in the data. How am I supposed to calculate this first quartile of the std? Any idea? Should I calculate the sd() for each gene and then find the first quartile?

What do you think about this function?


1 Answer 1


Answer from @llrs, converted from comment:

Probably is a question for the maintainer but I would guess that is a variance of the gene in the control dataset. So, if in normal samples the gene has a standard deviation of 0.5 this is the expected too in the tumoral cells. calculate the sd only on the control samples. So if the healthy/control samples have such variation the altered ones should have these or more according to the article (if I understand it correctly).

std <- apply(data[, controls], 1, sd, na.rm = TRUE); quantile(std, 0.25) should be much faster and efficient

But I would check it in support.bioconductor with the authors/maintainers.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.