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Add chromPeakSummary function and a fix #772

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Add chromPeakSummary function and a fix #772

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jorainer
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This PR adds the chromPeakSummary() method and a first implementation to calculate the peak shape quality from @wkumler on chrom peak results (thanks to @pablovgd for contributing).

In addition, it fixes a bug in the calculation of the beta scores during gap filling.

jorainer and others added 15 commits September 16, 2024 13:49
- Report also a chromatogram's m/z values if `findChromPeaks` is run on a
  `Chromatogram` or `Chromatograms` object (issue #765).
- Peak shape quality (similarity to gaussian shape) calculation is now performed
  using the EIC representation of a chromatographic peak, i.e. with intensities
  of mass peaks for the same retention time (but different m/z) summed.
Add internal function to calculate beta metrics
added chromPeakSummary method
Added peak quality section to xcms vignette.
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This is great ! Also thanks for the small vignette ! The code looks good to me, thank @pablovgd, excited to incorporate it in the overall workflow and see how to integrate it with the other preprocessing steps to improve them.

PS: I think the GHA needs to be updated, i they are failing because of warnings.

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Looks great, thanks for implementing this! I haven't checked it for bugs but the logic looks sound.

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@jorainer
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jorainer commented Oct 1, 2024

Thanks @wkumler for your review! I have now addressed all your suggestions.

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@sneumann sneumann left a comment

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Hi, thanks for the PR !
What I couldn't attach to a specific line, would it make sense to add the paper to https://github.com/sneumann/xcms/blob/devel/inst/CITATION ?
Yours, Steffen

#' @param skews A numeric vector of the skews to try, corresponding to the
#' shape1 of dbeta with a shape2 of 5. Values less than 5 will be increasingly
#' right-skewed, while values greater than 5 will be left-skewed.
#' shape1 of dbeta with a shape2 of 5. Values less than 5 will be
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Hi @wkumler , I needed to read that a few times. So a value of 5 means symmetric ? Why is it not zero centered, so that abs() tells you the skewedness (regardless in what direction) and you can <0 or >0 if you only want the direction ?

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That's a good point, thank you for the review. I'm using the language inherent to the dbeta() function where the values of 2-5 correspond to the "positive parameters" alpha and beta (or, in R, shape1 and shape2). I'm absolutely open to rescaling these and I agree that a positive/negative skew would be more intuitive. If we're open to rescaling and we have a parameter to pass now in chromPeakSummary then we also may want to allow a wider variety of numbers - the values of 5 matched my intuition for peak shape corners but other folks may want more/less tail and more/less skew.

@@ -279,6 +279,7 @@ dropGenericProcessHistory <- function(x, fun) {
valsPerSpect = valsPerSpect, rtrange = rtr,
mzrange = mzr)
if (length(mtx)) {
## mtx: time, mz, intensity
if (any(!is.na(mtx[, 3]))) {
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I usually recommend avoiding hardcoded column numbers, imagine someone came up with (time, ccs, mz, intensity). Might need that mtx is created with named columns somewhere above. Is that the only occurance of a hardcoded column number in the PR ?

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I agree - generally. Here, we load the mtx matrix with a function that is supposed to return a 3 column matrix with the columns exactly in the expected order. Thus, IMHO for this case (as long as there is no user input involved) it's save to use access-by-index. Also, because here we're calling this in a loop thousands of times, the way how we subset could affect the performance.

I did some timings on that:

mtx <- cbind(time = 1:5, mz = 1:5, intensity = c(2342.2, 123.1, 231.1, 23.1, 123.23))
int_col <- 3L

library(microbenchmark)
> microbenchmark(mtx[, 3L], mtx[, "intensity"], mtx[, int_col])
Unit: nanoseconds
               expr min    lq   mean median    uq  max neval cld
          mtx[, 3L] 354 363.5 437.30  380.5 477.5  948   100   a
 mtx[, "intensity"] 389 404.0 543.07  412.0 474.0 5036   100   a
     mtx[, int_col] 381 399.5 469.45  407.0 484.0 1135   100   a

((rtr[2] - rtr[1]) /
max(1, (sum(rtim >= rtr[1] & rtim <= rtr[2]) - 1)))
maxi <- which.max(mtx[, 3])
maxi <- which.max(mtx[, 3L])
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No, above was not the only hardcoded 3 :-) and more below ...

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jorainer commented Oct 8, 2024

Regarding the CITATIONS - I added the reference to the respective man page. I think adding that as a CITATION for xcms itself might be bit too much, as the paper deals more with peak shape quality, independently of xcms?

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5 participants