Closed danrlu closed 6 years ago
Thanks for opening this issue! When time allows, I will take a closer look at this.
It's definitely possible that my code has bugs. I'd be very grateful if you can write a minimal example that demonstrates any problems.
Thanks again! I looked more closely right now, and I think you're correct about the calculations.
After making your change, I noticed that my vignette fails to build. I guess there's another problem now with my read counts, because the fitCentipede()
fails to complete:
Quitting from lines 350-362 (centipede-tutorial.Rmd)
Error in quantile.default(x, c(TrimP, 1 - TrimP)) :
missing values and NaN's not allowed if 'na.rm' is FALSE
Calls: <Anonymous> ... clipExtremes -> quantile -> quantile -> quantile.default
Execution halted
When time allows, I'll revisit this again and see if I can fix it.
Maybe I just have a low number of read counts in my matrix? I don't know.
Hi Kamil,
Thank you for looking into this!
Sadly I get the same error message really frequently. Some motif/datasets work, some don't. As you suspected, larger amount of reads and more FIMO locations seemed to help (for example, loosening the p-value cutoff from FIMO results). But it's quite challenging to deal with this error as the data is the limiting factor here. I had to try out other packages in the end :(
Your package is super helpful regardless, otherwise I would have spent a week to get the data format right for CENTIPEDE, only to find out that it only works half of the time...
Thanks again!! Dan
Thanks for letting me know that you're also running into this problem!
Also, I'm glad my code could serve some purpose. I originally wrote it when I was struggling to figure out how to get the data in the right format, and I thought maybe someone might save some time if I share it publicly. Too bad I didn't even get it right! Thanks again for fixing my mistake! Contributions like this are the best part of writing open source software 😄
Hi,
Thank you for the tutorial and package, it's extremely helpful!! I was troubleshooting with my dataset and came across this issue. (I'm very sorry for moving the post around, thought it might be different from the previously reported issue.)
Here's an example with the original code:
I wonder whether this is what is intended. This does get rid of the peak in the left edge of the cut probability plot (where position==min(position) and on "+" strand will put a 1 in the matrix).
Thanks, Dan