barbieQ

[FeiLiyang]

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[bioc-issue-bot]

Hi @FeiLiyang

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: barbieQ
Type: Package
Title: Analyze Barcode Data from Clonal Tracking Experiments
Version: 0.99.0
Description: The barbieQ package provides a series of robust statistical tools 
  for analysing barcode count data generated from cell clonal tracking (i.e., lineage tracing) experiments.
  In these experiments, an initial cell and its offspring collectively form a clone (i.e., lineage).
  A unique barcode sequence, incorporated into the DNA of the inital cell, is inherited within the clone. 
  This one-to-one mapping of barcodes to clones enables clonal tracking of their behaviors.
  By counting barcodes, researchers can quantify the population abundance
  of individual clones under specific experimental perturbations. 
  barbieQ supports barcode count data preprocessing, statistical testing, and visualization.
Authors@R:
  c(person(given = "Liyang", 
 family = "Fei",
 role = c("aut", "cre"),
 email = "[email protected]",
 comment = c(ORCID = "0000-0002-3293-2094"))
    )
License: GPL-3
LazyData: FALSE
Encoding: UTF-8
VignetteBuilder: knitr
Suggests: 
    knitr,
    rmarkdown,
    testthat (>= 3.0.0)
Imports: 
    magrittr,
    tidyr,
    dplyr,
    grid,
    circlize,
    ComplexHeatmap,
    ggplot2,
    logistf,
    limma,
    stats,
    igraph,
    utils,
    data.table
biocViews: Sequencing, Regression, Preprocessing, Visualization
BugReports: https://github.com/Oshlack/barbieQ
URL: https://github.com/Oshlack/barbieQ/issues
RoxygenNote: 7.3.2
Config/testthat/edition: 3
Depends: 
    R (>= 4.5)
Roxygen: list(markdown = TRUE)

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[js2264]

Hi @FeiLiyang,

The {barbieQ} package is in a good shape and will be a valuable contribution to Bioconductor. The package provides a thorough documentation and a clear vignette with lots of examples. The source code is also very well tested.
That being said, there is a major improvement that I would suggest, which is to rely on SummarizedExperiment data class instead of the current list-based data structure. SummarizedExperiment objects are widely used in Bioconductor (read more here). In your case, they would be very well suited since:

1. They can store any number of assays, which are rectangular data.frames with the same number of rows (features) and columns (samples). They also store row metadata and column metadata. In your case, features (ie rows) would be barcodes, and samples (ie columns) would be genes.

2. They are supported by well-understood semantics and provide out-of-the-box methods to manipulate data, including subsetting rows and columns (synchronously across all assays, and rows and column metadata).

AFAICT, this is more or less the structure of the data that you are working with. In your barbieQ list, you could have the assay, proportion, CPM, occurrence, rank, and $isTop$mat provided as individual assays. metadata would be colData, and $isTop$vec would be rowData. Extra bits could go to metadata or row/colData, as needed.

library(SummarizedExperiment)
x <- createBarbieQ(
  object = myBarbieQ, target = data.frame(Mouse = rep(seq_len(4), each = 3)),
  factorColors = list(
    Mouse = c("1" = "#111199", "2" = "#112200", "3" = "#441111", "4" = "#000000")
  )
)
barbieQ_se <- SummarizedExperiment(
    assays = list(assay = x$assay, proportion = x$proportion, CPM = x$CPM, occurrence = x$occurrence, rank = x$rank, isTop = x$isTop$mat),
    colData = data.frame(Mouse = x$metadata),
    rowData = data.frame(isTopVec = x$isTop$vec)
)

barbieQ_se
## class: SummarizedExperiment 
## dim: 50 12 
## metadata(0):
## assays(6): assay proportion ... rank isTop
## rownames(50): Barcode1 Barcode2 ... Barcode49 Barcode50
## rowData names(1): isTopVec
## colnames(12): V1 V2 ... V11 V12
## colData names(1): Mouse

colData(barbieQ_se)
## DataFrame with 12 rows and 1 column
##         Mouse
##     <integer>
## V1          1
## V2          1
## V3          1
## V4          2
## V5          2
## ...       ...
## V8          3
## V9          3
## V10         4
## V11         4
## V12         4

rowData(barbieQ_se)
## DataFrame with 50 rows and 1 column
##            isTopVec
##           <logical>
## Barcode1       TRUE
## Barcode2       TRUE
## Barcode3       TRUE
## Barcode4       TRUE
## Barcode5       TRUE
## ...             ...
## Barcode46      TRUE
## Barcode47      TRUE
## Barcode48      TRUE
## Barcode49      TRUE
## Barcode50      TRUE

Points to adress prior to acceptance

• Decide whether or not you wish to update the source code to support SummarizedExperiment class. This would require a lot of work, however I believe it would make the package more user-friendly, particularly to Bioconductor users who are familiar with SummarizedExperiment-like objects, and will allow users to use the package in combination with other Bioconductor packages. Let me know whether you wish to proceed with this change. If so, I will be happy to provide you with some guidance on how to implement this. If not, just let me know, so I can accept the package as it is (once the following points are addressed).

• You'll need to document the monkeyHSPC data object (see here for more info). You already have some information provided in inst/scripts/, but you would need to move some to R/monkeyHSPC.R.

• Remember to update the Installation section in the vignette to explain how to install barbieQ from Bioconductor.

• You have wrapped several calls to ggplot in suppressWarnings(). This is not recommended, as it can hide important warnings. Also, when I run the internal plotting code with the provided example outside of the suppressWarnings() call, I don't see any warning. You mention in a comment that "ggplot2 has a bug that gives wrong warning of 'Removed rows containing missing values'". AFAIK, this is not a bug but an actual feature of ggplot(), which warns the end-user when they are trying to plot data with missing values or out-of-range values. I would recommend that you remove the suppressWarnings() calls and instead address the underlying issue that is causing the warnings.

Let me know if anything is not clear, I will be happy to provide more information!