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IsoBayes #3084

Closed SimoneTiberi closed 1 year ago

SimoneTiberi commented 1 year ago

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bioc-issue-bot commented 1 year ago

Hi @SimoneTiberi

Thanks for submitting your package. We are taking a quick look at it and you will hear back from us soon.

The DESCRIPTION file for this package is:

Package: IsoBayes
Type: Package
Title: IsoBayes: Single Isoform protein inference Method via Bayesian Analyses
Version: 0.99.0
Description: IsoBayes is a Bayesian method to perform inference on single protein isoforms.
  Our approach infers the presence/absence of protein isoforms, and also estimates their abundance;
  additionally, it provides a measure of the uncertainty of these estimates, via:
  i) the posterior probability that a protein isoform is present in the sample;
  ii) a posterior credible interval of its abundance.
  IsoBayes inputs liquid cromatography mass spectrometry (MS) data,
  and can work with both PSM counts, and intensities.
  When available, trascript isoform abundances (i.e., TPMs) are also incorporated:
  TPMs are used to formulate an informative prior for the respective protein isoform relative abundance.
  We further identify isoforms where the relative abundance of proteins and transcripts significantly differ.
  We use a two-layer latent variable approach to model two sources of uncertainty typical of MS data:
  i) peptides may be erroneously detected (even when absent);
  ii) many peptides are compatible with multiple protein isoforms.
  In the first layer, we sample the presence/absence of each peptide based on its estimated probability 
  of being mistakenly detected, also known as PEP (i.e., posterior error probability).
  In the second layer, for peptides that were estimated as being present, 
  we allocate their abundance across the protein isoforms they map to.
  These two steps allow us to recover the presence and abundance of each protein isoform.
Authors@R: c(person(given = "Jordy",
  family = "Bollon",
  role = c("aut", "cre"),
  email = "jordy.bollon@iit.it"),
  person(given = "Simone",
  family = "Tiberi",
  role = c("aut", "cre"),
  email = "simone.tiberi@unibo.ch",
  comment = c(ORCID = "0000-0002-3054-9964")))
biocViews: StatisticalMethod, Bayesian, Proteomics, MassSpectrometry, AlternativeSplicing,
  Sequencing, RNASeq, GeneExpression, Genetics, Visualization, Software
License: GPL-3
Depends: R (>= 4.3.0)
Imports: methods, Rcpp, Biostrings, data.table, glue, xml2, DescTools, stats, doParallel, parallel, doRNG, foreach, iterators, ggplot2, HDInterval
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, BiocStyle
SystemRequirements: C++11
VignetteBuilder: knitr
RoxygenNote: 7.2.3
ByteCompile: true
URL: https://github.com/SimoneTiberi/IsoBayes
BugReports: https://github.com/SimoneTiberi/IsoBayes/issues
SimoneTiberi commented 1 year ago

Hi Bioc reviewers, we submitted IsoBayes, a Bayesian inference method to perform inference on single-isoform proteins from proteomics data.

Looking forward to your feedback.

Kind regards, Simone and Jordy

bioc-issue-bot commented 1 year ago

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bioc-issue-bot commented 1 year ago

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

On one or more platforms, the build results were: "ERROR". This may mean there is a problem with the package that you need to fix. Or it may mean that there is a problem with the build system itself.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: Linux (Ubuntu 22.04.2 LTS): IsoBayes_0.99.2.tar.gz macOS 12.6.5 Monterey: IsoBayes_0.99.2.tar.gz

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SimoneTiberi commented 1 year ago

Hi Laurent, I see there are a few errors which (I think!) we didn't have locally. I'll fix them and push an update.

Thanks, Simone

bioc-issue-bot commented 1 year ago

Received a valid push on git.bioconductor.org; starting a build for commit id: 588eff69573ec6915c120215d2096be755cc5020

bioc-issue-bot commented 1 year ago

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

On one or more platforms, the build results were: "ERROR". This may mean there is a problem with the package that you need to fix. Or it may mean that there is a problem with the build system itself.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: macOS 12.6.5 Monterey: IsoBayes_0.99.3.tar.gz Linux (Ubuntu 22.04.2 LTS): IsoBayes_0.99.3.tar.gz

Links above active for 21 days.

Remember: if you submitted your package after July 7th, 2020, when making changes to your repository push to git@git.bioconductor.org:packages/IsoBayes to trigger a new build. A quick tutorial for setting up remotes and pushing to upstream can be found here.

bioc-issue-bot commented 1 year ago

Received a valid push on git.bioconductor.org; starting a build for commit id: a65913093266e0ae6866cb42ca1f37503fce370e

bioc-issue-bot commented 1 year ago

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

Congratulations! The package built without errors or warnings on all platforms.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: macOS 12.6.5 Monterey: IsoBayes_0.99.4.tar.gz Linux (Ubuntu 22.04.2 LTS): IsoBayes_0.99.4.tar.gz

Links above active for 21 days.

Remember: if you submitted your package after July 7th, 2020, when making changes to your repository push to git@git.bioconductor.org:packages/IsoBayes to trigger a new build. A quick tutorial for setting up remotes and pushing to upstream can be found here.

bioc-issue-bot commented 1 year ago

Received a valid push on git.bioconductor.org; starting a build for commit id: bc9383d2ea0f6998b2a21cb4d94a0a35b1edcfca

bioc-issue-bot commented 1 year ago

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

Congratulations! The package built without errors or warnings on all platforms.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: Linux (Ubuntu 22.04.2 LTS): IsoBayes_0.99.5.tar.gz macOS 12.6.5 Monterey: IsoBayes_0.99.5.tar.gz

Links above active for 21 days.

Remember: if you submitted your package after July 7th, 2020, when making changes to your repository push to git@git.bioconductor.org:packages/IsoBayes to trigger a new build. A quick tutorial for setting up remotes and pushing to upstream can be found here.

SimoneTiberi commented 1 year ago

Hi again Laurent @lgatto, ok, we sorted the previous issues.

Although @bioc-issue-bot did not remove "ERROR" label, there are no errors in the current checks, so I think the package should be ok now.

I'll leave it to you, and wait for your feedback. Simone

SimoneTiberi commented 1 year ago

Hi @lgatto, and @vjcitn, this issue has been quite for the last couple of weeks (I suppose for summer vacations). Nonetheless, just in case it was forgotten, I'd like to gently put this submission to your attention.

Thanks, Simone

ttriche commented 1 year ago

Oh hey, what’s my role here?--tOn Aug 18, 2023, at 6:06 AM, Simone Tiberi @.***> wrote: Hi @lgatto, @vjcitn and @ttriche, this issue has been quite for the last couple of weeks (I suppose for summer vacations). Nonetheless, just in case it was forgotten, I'd like to gently put this submission to your attention. Thanks, Simone

—Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you were mentioned.Message ID: @.***>

SimoneTiberi commented 1 year ago

Hi Tim @ttriche, sorry I tagged you by mistake!

lshep commented 1 year ago

@lgatto was away on holiday and should be returning this week. He will look at your package and have feedback soon. We apologize for the delay.

SimoneTiberi commented 1 year ago

Thanks for the clarification. No problem, I just wanted to make sure someone was on it.

Simone

SimoneTiberi commented 1 year ago

Hi Laurent @lgatto and @lshep, any updates on the review process?

Thanks, Simone

lgatto commented 1 year ago

Hi @SimoneTiberi - I will post more technical comments tomorrow or on Monday, but I wanted to write this part already so you could give it some thought. It concerns an important concept for the Bioconductor project, namely the re-use of classes and functionality.

Bioconductor is well known for it's core classes, that allow for better interoperability between independently developed packages. The SummarizedExperiment is a prime example thereof, that is both used for RNA-Seq and quantitative proteomics data. In addition, there's the QFeatures class that focuses on the multi-level structure of proteomics data (precursors, peptides, proteins), that you tackle in your package. My request for you would be to make use of this carefully crafted and maintained infrastructure in your package, at least use SummarizedExperiments as inputs. We can also discuss the usage of QFeatures, if you want.

In you vignette, you say that IsoBayes works with outputs from MetaMorpheus or Percolator, but why not make it applicable to any software by using SummarizedExperiments or QFeatures, and the QFeatures::readSummarizedExperiments() or QFeatures::readQFeatures() functions? Currently, you have very a specific and limited (in scope) load_data() function.

In addition to making use of existing classes and enable interoperability with other packages, you would also benefit from other functionality, such features aggregation and visualisation, to cite only two.

lgatto commented 1 year ago

Documentation

Unit tests

R code

styler::style_pkg(transformers = styler::tidyverse_style(indent_by = 4))

Misc

bioc-issue-bot commented 1 year ago

Received a valid push on git.bioconductor.org; starting a build for commit id: a4172450d734cb8aa5953fa585afb5d48e2b0776

bioc-issue-bot commented 1 year ago

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

On one or more platforms, the build results were: "ERROR". This may mean there is a problem with the package that you need to fix. Or it may mean that there is a problem with the build system itself.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: Linux (Ubuntu 22.04.2 LTS): IsoBayes_0.99.10.tar.gz macOS 12.6.5 Monterey: IsoBayes_0.99.10.tar.gz

Links above active for 21 days.

Remember: if you submitted your package after July 7th, 2020, when making changes to your repository push to git@git.bioconductor.org:packages/IsoBayes to trigger a new build. A quick tutorial for setting up remotes and pushing to upstream can be found here.

bioc-issue-bot commented 1 year ago

Received a valid push on git.bioconductor.org; starting a build for commit id: 5cebbb3e74c0551bd3d060bbb9b676679e3b458f

bioc-issue-bot commented 1 year ago

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

Congratulations! The package built without errors or warnings on all platforms.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: Linux (Ubuntu 22.04.2 LTS): IsoBayes_0.99.11.tar.gz macOS 12.6.5 Monterey: IsoBayes_0.99.11.tar.gz

Links above active for 21 days.

Remember: if you submitted your package after July 7th, 2020, when making changes to your repository push to git@git.bioconductor.org:packages/IsoBayes to trigger a new build. A quick tutorial for setting up remotes and pushing to upstream can be found here.

SimoneTiberi commented 1 year ago

Hi Laurent @lgatto , thanks a lot for taking the time to review our work; that was a very through review.

We implemented several of your suggestions, while we argued against others we disagree with.

Here is a point-to-point response.

I hope we addressed all your comments.

Simone and Jordy

Options 3-5 are generic, and can be easily generated by users. I checked QFeatures and saw that a SummarizedExperiment of the psms can easily be extracted as hl[["psms"]]. We also added a note about this in the README and vignettes.

2 blocks are disabled because they only report example code to load the data from alternative inputs (from Percolator and a general tsv file); Similarly 1 block is redundant (and computationally intensive), because we show 2 ways to run inference function (1 disabled).

We kept those scripts, even though they are disabled, because we think that it's still useful to show the code of alternative options (for loading the data, and doing inference).

Wrong syntax, fixed now, thanks!

Sorry, we don't fully understand the comment: exactly what piece of information do you suggest adding to the README? Do you suggest adding a link to the Vignette, like "browseVignettes"?

Yes, highest posterior density. We missed it, properly introduced now, thanks.

Unit tests

This is odd; the package only has 3 functions which are exported to users (load, inference and plot). All 3 are tested; why is the coverage 43 % only?

Thanks, we missed this; fixed now.

I know this is a sensitive matter, but I disagree with using "<-". Personally, instead of "<-", I often write "< -" (i.e., smaller than minus). For me, "<-" is more error prone.

I agree, but at the same time I also think that splitting a function too many times is not ideal (for the same reasons: error prone and maintenance). Only 3 functions are exported to the users, and overall we have ~40 functions (36 in R and 3 in C++). Personally, I prefer longer functions than excessive splitting intro micro-scripts.

We split some of the longer lines.

We didn't know this function; thanks!

I agree with re-using classes, but our data_loaded object is only used by IsoBayes package (inference function). Users do not interact directly with this object.

Although in principle we could use BiocParallel, we do have some reasons for using foreach. In particular: 1) we use iterators (so only a part of the full dataset is passed to each thread); 2) we specify the order in which parallel tasks are executed, so that "heavier" tasks are done first -> this avoids waiting for 1 single task to be completed while all other threads are done; 3) we use doRNG to generate independent parallel seeds; 4) doParallel can run on all OS. The advantage of using BiocParallel is that users could define some things, such as the parallel class. But, in order to have the 4 points above, I think that only the DoparParam could be used; this removes the benefit of additional flexibility, and increases the possibility of introducing mistakes, if other classes are used.

Honestly, I don't this it's a very interesting script: we mainly used it to decrease the size of our original dataset, so that the data would fit into the package.

Yes, we generally use mzid files. However, when using Percolator (from the OpenMS toolkit), peptide files are stored in a idXML format. So, only when users use Percolator, we input that format (see our answer above about the possible formats for the input data in load_data).

lgatto commented 1 year ago
> library(covr)
> pc <- covr::package_coverage("IsoBayes/")
> pc
IsoBayes Coverage: 42.14%
R/aggregate_components_pep.R: 0.00%
R/aggregate_components.R: 0.00%
R/build_intensity.R: 0.00%
R/check_variables.R: 0.00%
R/get_components.R: 0.00%
R/get_list_pept_prot.R: 0.00%
R/get_peptides_from_idXML.R: 0.00%
R/get_proteins_from_idXML.R: 0.00%
R/list_components_for_MCMC_pep.R: 0.00%
R/list_components_for_MCMC.R: 0.00%
R/parallel_MCMC_pep.R: 0.00%
R/parallel_MCMC.R: 0.00%
R/reorder_groups_by_nProteins.R: 0.00%
R/run_MCMC.R: 0.00%
R/utils_idXML.R: 0.00%
src/MCMC_Unique.cpp: 0.00%
src/MCMC.cpp: 0.00%
R/collapse_pept_w_equal_EC.R: 28.57%
R/load_tpm.R: 35.71%
R/input_check_plot.R: 36.84%
R/input_check_inference.R: 46.15%
R/load.R: 57.00%
R/input_check.R: 61.70%
R/inference.R: 71.21%
R/map_isoform_to_gene.R: 73.33%
R/get_overall_abundance.R: 75.00%
R/run_MCMC_pep.R: 84.21%
R/set_MCMC_args.R: 85.19%
R/plot_relative_abundances.R: 89.47%
R/normalize_by_gene.R: 90.91%
R/aggregate_sim_pep.R: 100.00%
R/build_peptide_df.R: 100.00%
R/convert_EC_to_num.R: 100.00%
R/get_prot_from_EC.R: 100.00%
R/get_res_MCMC.R: 100.00%
R/stat_from_MCMC_PI.R: 100.00%
R/stat_from_MCMC_Y.R: 100.00%
R/stat_from_TPM.R: 100.00%
R/unique_peptides.R: 100.00%
R/unique_protein_abundance.R: 100.00%
src/MCMC_PEP.cpp: 100.00%

And by the way, that's one reason why short functions are useful: they are easier to test in isolation.

I checked QFeatures and saw that a SummarizedExperiment of the psms can easily be extracted as hl[["psms"]]. We also added a note about this in the README and vignettes.

The name hl[["psms"]] is only an example, and a complex data set could contain several PSM-level assays. I don't see any reference to this in either the vignette, not the README file - are you sure you pushed all your changes?

SimoneTiberi commented 1 year ago

Hi Laurent @lgatto, thanks again for your feedback. I must say that, although I don't agree with all comments, this is the most detailed feedback I have ever received on a package, and it's certainly very useful. So, thanks a lot for taking this review so seriously.

We'll do a few extra edits and update the package.

In the meantime, I'd like to ask for one clarification about the data structure in QFeatures. For our analyses, we need peptide level information; in particular, for each peptide:

Now, I did not add a direct reference to QFeatures in the vignettes (sorry, I was unclear: the reference is to SE), because I am not 100% I understand where the "EC" is. In SE$ProteinDescriptions below, the protein information seems to be at the gene level. If we can get that at the isoform-level, then we can easily directly use the data in a QFeatures object directly. So far, I did not understand how to do that (my bad, I am not familiar with QFeatures).

library("QFeatures")
data(hlpsms)
hl <- readQFeatures(hlpsms, ecol = 1:10, name = "psms")
SE = rowData(hl[["psms"]])
SE$ProteinDescriptions

So, if we manage, we will try to change the load_data function to also input a QFeatures object directly, but will not modify the output of load_data. It is mainly a list of data.frames with different rows and columns (because some refer to isoforms, while others refer to peptides), so I don't see how we could (easily) fit that into a single SE object. Also, (sorry for iterating this), this is really just used by inference function, so users should not interact with this.

Thanks again Laurent, Simone and Jordy

bioc-issue-bot commented 1 year ago

Received a valid push on git.bioconductor.org; starting a build for commit id: 46dd9914a02ccf9ca35e161b320abb43f4a5117f

bioc-issue-bot commented 1 year ago

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

Congratulations! The package built without errors or warnings on all platforms.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: Linux (Ubuntu 22.04.2 LTS): IsoBayes_0.99.12.tar.gz macOS 12.6.5 Monterey: IsoBayes_0.99.12.tar.gz

Links above active for 21 days.

Remember: if you submitted your package after July 7th, 2020, when making changes to your repository push to git@git.bioconductor.org:packages/IsoBayes to trigger a new build. A quick tutorial for setting up remotes and pushing to upstream can be found here.

SimoneTiberi commented 1 year ago

Hi Laurent @lgatto, we have increased the coverage of our tests, and extended the vignette with a final Section about OpenMS/Metamorpheus pipelines (basically copied from the README).

We would be grateful if you could accept our package on Bioc :D

Once we understand, in the readQFeatures object, where the isoform-level mapping (EC) of peptides is stored, we can easily include that option as a further input to load_data (although I know that's not exactly what you meant; see my message above -> https://github.com/Bioconductor/Contributions/issues/3084#issuecomment-1755806136).

Thanks, Simone and Jordy

SimoneTiberi commented 1 year ago

Hi Laurent @lgatto, is there anything else we'd do on the package?

It'd be really nice if we'd have IsoBayes in the upcoming Bioc release.

Thanks, Simone

lgatto commented 1 year ago

Dear @SimoneTiberi

A QFeatures object is simply a collection of SummarizedExperiment objects, one for each level such as PSM, peptides, protein, ... or isoform, if you wish so. The example that you point to defines proteins (protein groups, to be precise) from peptides via the protein descriptions. But I don't think or claim you would need QFeatures - an SE would suffice. However, that SE could be an QFeatures element, and build from lower-level features via the QFeatures infrastructure.

Let me emphasise again my point here. You should not overload load_data() with yet another data structure (QFeatures here). This is not the right approach. You want to focus on one well-defined data structure, one that is widely used/accepted/tested in Bioconductor (SE here), and use that one class as the main input for your work, and leave to conversions such as csv/data.frame to SE to others, or to specialised functions. You have achieved the exact opposite by writing a longer, more convoluted load_data().

Now, it doesn't look like you will address this suggestion, which is fine by me - you package is a great contribution anyway. Just let me know if you don't feel that a better/cleaner integration is worth the effort at this stage, and I'm happy to accept now and, if you wish so, help you (or directly contribute) a PR along the lines I'm suggesting.

SimoneTiberi commented 1 year ago

Hi Laurent @lgatto , ok, got it; we misunderstood some of your comments.

I agree with your suggestion: we will have load_data working with SE objects only. To facilitate users, we will have 3 functions to generate the SE object from: i) MetaMorpheus output; ii) Percolator output; iii) a general csv. We will clarify in the vignettes that other ways of generating the SE objects are possible (e.g., via QFeatures).

If you can help us understand how to obtain isoform-level ECs from QFeatures, we will provide a direct link/clear explanation.

We will work on these changes in the coming days and update the package in a couple of weeks. In the meantime, yes, it would be nice to have the package accepted so that we can enter the upcoming release.

Thanks again for all your suggestions, Simone and Jordy

lgatto commented 1 year ago

@lshep - what do you think of accepting the package (which is a great addition to Bioconductor), while some additional changes are to be expected?

@SimoneTiberi - I don't want to do this without explicit agreement from Lori, who oversees the package reviews.

lshep commented 1 year ago

@SimoneTiberi / @lgatto with the understanding that this is a rare exception to the normal process in case future submissions are made. Normally we expect recommended changes to be made before acceptance as in our experience promises of later implementation rarely come to be. @lgatto if you think it is still in a good usable state as is and hopefully @SimoneTiberi will follow up post acceptance. But yes.that's ok.

lgatto commented 1 year ago

I understand, and wouldn't suggest this if the package wouldn't be a useful addition anyway.

@SimoneTiberi - do we both agree to take this on (on a new issue on https://github.com/SimoneTiberi/IsoBayes for instance) beyond the Bioc review?

lshep commented 1 year ago

If it is going to make the 3.18 release it needs to be accepted in the next few hours so it can be listed before the bump and branch tomorrow.

bioc-issue-bot commented 1 year ago

Your package has been accepted. It will be added to the Bioconductor nightly builds.

Thank you for contributing to Bioconductor!

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lgatto commented 1 year ago

I've opened this issue to continue with what was discussed here.

SimoneTiberi commented 1 year ago

Thanks for the review process, and for accepting the package Laurent @lgatto and @lshep.

Good idea about the issue! We'd deal with it within 1-2 weeks.

lshep commented 1 year ago

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