maaslin3

[WillNickols]

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[bioc-issue-bot]

Hi @WillNickols

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: maaslin3
Title: "Refining and extending generalized multivariate linear models
        for meta-omic association discovery"
Year: 2024
Version: 0.99.0
Authors@R: c(
  person("William", "Nickols", email = "[email protected]", role = c("aut", "cre"), comment=c(ORCID="0000-0001-8214-9746")), 
  person("Jacob", "Nearing", email = "[email protected]", role = "aut")
  )
Depends: R (>= 4.4)
Description: MaAsLin 3 refines and extends generalized multivariate linear models for meta-omicron association discovery. It finds abundance and prevalence associations between microbiome meta-omics features and complex metadata in population-scale epidemiological studies. The software includes multiple analysis methods (including support for multiple covariates, repeated measures, and ordered predictors), filtering, normalization, and transform options to customize analysis for your specific study.
License: MIT + file LICENSE
Imports: dplyr, pbapply, lmerTest, parallel, lme4, optparse, logging,
        multcomp, ggplot2, RColorBrewer, patchwork, scales,
        rlang, tibble, ggnewscale, survival, methods, BiocGenerics
Suggests: knitr, testthat (>= 2.1.0), rmarkdown, markdown, kableExtra
VignetteBuilder: knitr
Collate: fit.R utility_scripts.R viz.R maaslin3.R
URL: http://huttenhower.sph.harvard.edu/maaslin3
biocViews: Metagenomics, Software, Microbiome, Normalization, MultipleComparison
BugReports: https://github.com/biobakery/maaslin3/issues
NeedsCompilation: no
Packaged: 2024-07-16 15:01:54 UTC; williamnickols

[lshep]

Please also provide an inst/scripts directory that describes how the data in inst/extdata was generated. It can be code, pseudo-code, or text but should minimally list any source or licensing information.

Do you have an approximation on how long this takes to build? I killed after 20 min. which would be above our time limit.

I also don't see any integration with existing Bioconductor class structures. See interoperability requirement. You should show interoperability with existing microbiome packages

[WillNickols]

Hi! Thanks for the helpful suggestions. We've updated the following accordingly:

1. We've added an inst/scripts directory that describes how the data in inst/extdata were generated and where the raw data can be found.
2. We've moved out some files we mainly use for microbiome classes, so it's now taking 6 minutes to build on my computer and 8 in GitHub actions. Most of this time comes from building the vignettes since they demonstrate many non-overlapping analysis options included in the package.
3. We've added in an option to run maaslin3, the main function that we expect almost all users will use, with a SummarizedExperiment input and explained how to do so in the documentation and vignette. This should allow MaAsLin 3 to take the same sort of inputs used by ANCOM-BC, a similar tool. Based on user feedback on our forum, we believe most users will use the base R options since they are typically reading in their taxonomic abundance tables and metadata from disk rather than from other BioC packages. Still, this change should be sufficient for anyone using a SummarizedExperiment input.

We've pushed a new version updated from 0.99.0 to 0.99.1. Please let us know if there's anything else we should do!

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[bioc-issue-bot]

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[bioc-issue-bot]

A reviewer has been assigned to your package for an indepth review.
Please respond accordingly to any further comments from the reviewer.

[DarioS]

• Don't include GitHub installation instructions but only recommend BioManager installation. It also does GitHub.

BiocManager::install("biobakery/maaslin3") # Update the value to "maaslin3" after acceptance and branch creation.

Also, mention that if user wants to compile vignette themselves, they should also specify dependencies = TRUE.

• Use sapply or vapply instead of for loops. Please read Vectorize section of Developer's Guide. For example,

all_factors <- c()
for (col in colnames(dat_sub)) {
  if (!is.numeric(dat_sub[, col])) {
    if (is.factor(dat_sub[, col])) {
      factor_levels <- levels(dat_sub[, col])
    } else {
       factor_levels <- levels(factor(dat_sub[, col]))
    }
    # All factor levels except baseline
    all_factors <- c(all_factors, paste0(col, unique(factor_levels[-1])))
  }
}

• Supports SummarizedExperiment as input, but not TreeSummarizedExperiment. TreeSummarizedExperiment allows for easy switching between different feature granularity (e.g. aggregate species to genus). This is a core class for microbiome data.
• Lack of input parameter value checking. It should be the first task done within a function for parameters that the a value within a small set of valid values, such as model. See a match.arg tutorial for an example of how to use match.arg.
• It is not clear whether the user should read manual or tutorial first. Please clarify somehow.
• Bioconductor has its own data frame in S4Vectors named DataFrame. MaAsLin 3 can't handle it.

> library(S4Vectors)
> metadata <- as(metadata, "DataFrame")
> metadata[, 1:5]
DataFrame with 1527 rows and 5 columns
           participant_id      site_name  week_num     reads diagnosis
              <character>    <character> <integer> <integer>  <factor>
CSM5FZ3N_P          C3001   Cedars-Sinai         0   9961743        CD
CSM5FZ3R_P          C3001   Cedars-Sinai         2  16456391        CD
CSM5FZ3T_P          C3002   Cedars-Sinai         0  10511448        CD
CSM5FZ3V_P          C3001   Cedars-Sinai         6  17808965        CD
CSM5FZ3X_P          C3002   Cedars-Sinai         2  13160893        CD
...                   ...            ...       ...       ...       ...
PSMB4MBS            P6037 MGH Pediatrics        33  11589722        CD
PSMB4MC1            P6038 MGH Pediatrics        34  15565445        UC
PSMB4MC3            P6038 MGH Pediatrics        35   7319782        UC
PSMB4MC5            P6038 MGH Pediatrics        37   6913198        UC
PSMB4MC7            P6035 MGH Pediatrics        38   5297087        UC
> fit_out <- maaslin3(input_data = taxa_table, input_metadata = metadata, output = 'hmp2_output', ...
Error in maaslin_read_data(input_data, input_metadata, feature_specific_covariate,  : 
  input_metadata is neither a file nor a data frame!

Please improve the level of interoperability with core Bioconductor infrastructure.

• MaAsLin 3's new functionality sounds a lot like LinDA. It would be nice to see a feature comparison table with other R packages designed for the same purpose. See Comparison to Existing for an example of such a table. What is the great new feature that makes MaAsLin 3 irresistible compared to alternatives? It is not clear to me.