Closed metamaden closed 8 months ago
Hi @metamaden
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: lute
Version: 0.99.0
Title: Framework for cell size scale factor normalized bulk transcriptomics deconvolution experiments
Authors@R: c(person(c("Sean", "K"), "Maden", role = c("cre", "aut"),
email = "maden.sean@gmail.com", comment = c(ORCID = "0000-0002-2212-4894")),
person("Stephanie", "Hicks", role = "aut", email = "shicks19@gmail.com", comment = c(ORCID = "0000-0002-7858-0231")))
Description: Provides a framework for adjustment on cell type size when performing bulk transcripomics deconvolution. The main framework function provides a means of reference normalization using cell size scale factors. It allows for marker selection and deconvolution using non-negative least squares (NNLS) by default. The framework is extensible for other marker selection and deconvolution algorithms, and users may reuse the generics, methods, and classes for these when developing new algorithms.
License: Artistic-2.0
Encoding: UTF-8
URL: https://github.com/metamaden/lute
BugReports: https://github.com/metamaden/lute/issues
LazyData: FALSE
Depends:
R (>= 4.3.0),
stats,
methods,
utils,
SummarizedExperiment,
SingleCellExperiment,
BiocGenerics
Imports:
S4Vectors,
Biobase,
scran,
dplyr
Suggests:
nnls,
knitr,
testthat,
rmarkdown,
BiocStyle,
GenomicRanges,
limma,
ExperimentHub,
AnnotationHub,
DelayedMatrixStats,
BisqueRNA,
DelayedArray
VignetteBuilder:
knitr
biocViews: RNASeq, Sequencing, SingleCell, Coverage, Transcriptomics, Normalization
RoxygenNote: 7.2.3
Collate:
'lute_generics.R'
'deconvolutionParam-class.R'
'referencebasedParam-class.R'
'independentbulkParam-class.R'
'bisqueParam-class.R'
'typemarkersParam-class.R'
'findmarkersParam-class.R'
'globals.R'
'lute_cellScaleFactors.R'
'lute_classes.R'
'lute_conversions.R'
'lute_framework.R'
'lute_metadata.R'
'lute_randomized-data.R'
'lute_rnf.R'
'lute_utilities.R'
'nnlsParam-class.R'
Is this somehow related/can borrow ideas from the omnideconv
package/framework? See https://github.com/omnideconv/omnideconv/ for more details
Hi there,
Thanks for pointing out the omnideconv
resource and for the suggestion that lute
borrow ideas from omnideconv
. I have looked at the omnideconv
repo, and it seems like both lute
and omnideconv
provide support for multiple deconvolution algorithms, but that each uses a distinct approach. Perhaps there are properties from omnideconv
that could be incorporated in lute
, but it's not clear.
I think lute and omnideconv
will have to remain as separate frameworks that the community could choose from, for several reasons. First, we want lute
to be available on Bioconductor, and it only ships with support for CRAN (nnls
, BisqueRNA
) and Bioconductor (scuttle
) algorithms. omnideconv
supports several algorithms not on CRAN or Bioconductor. Second, lute
uses generics, methods, and classes based on the bluster
package. These resources could be utilized by other developers to develop their algorithms, including omnideconv
, but I am not sure if the reverse is true (i.e. that lute
could utilize the omnideconv
approach for supporting multiple algorithms). Third, the lute
framework has 2 steps, (1) a cell type marker identification step and (2) a deconvolution step. Both steps use standard term mappings so that the same inputs can be passed to variable pipelines or combinations of the 2 steps. Fourth, I designed lute
for standard application of cell size scale factors. This is a step performed outside of the supported algorithms, to allow its routine use in deconvolution experiments.
best regards,
Sean
Hi Sean, thanks for the detailed answer!
I did not mean in any way that this is a duplicated effort - I am aware of the pros and cons of the approach omnideconv
deliberately took!
I know the support for CRAN/Bioc only was a tough choice, but also the only way to include some methods that, for one reason or another, did not (want to) make it to any of these two venues.
I like a lot some of the design decisions you took, and look forward for this to be welcome into the Bioc ecosystem.
The best thing is that both projects are out in the open, and that is the best to learn from each other/improve each other, exploiting some lessons learnt and so on 😉
Federico
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A reviewer has been assigned to your package for an indepth review. Please respond accordingly to any further comments from the reviewer.
The package is at a fairly well-developed state. Some issues are:
Code readability should be improved. See Use of Space and Function Names and Variable Names and Class Names.
Put the show
method on the same page as the class definition. For example, see ?GenomicRanges::GenomicRanges
.
Choose informative variable names - not s
, y
, z
. See Review Checklist
Function argument names are descriptive and documented.
The image deconvolutionParam_hierarchy_diagram.jpeg is blurry when embedded in the rendered HTML document.
Vignette needs to have an Installation section. See Installation.
The main application is deconvolution with cell size adjustment. Some justification of why size adjustment is important should be added. Why is it important biologically? Does it enable new conclusions which are not possible without it?
Could the interoperability with existing Bioconductor packages be increased? Is anything in SingleCellData view reusable? Currently, all examples use simulated data and it may be more engaging to readers to see a biological data set.
Hello,
Thanks for this feedback! I have taken the following steps to address these issues:
The package is at a fairly well-developed state. Some issues are:
- Code readability should be improved. See Use of Space and Function Names and Variable Names and Class Names.
For use of space, spaces were removed around "=" in function calls and definitions, and spaces have been added around "==" calls. Comment hashes have also been added. Variable names have also been improved throughout the package.
I have not yet changed any function and class names, but I would be happy to make specific changes. The class names borrowed nomenclature from the bluster
package, but they should say include lute
in the name? Please let me know.
- Put the
show
method on the same page as the class definition. For example, see?GenomicRanges::GenomicRanges
.
The show method has been added to the class definitions for bisqueParam, nnlsParam, typemarkersParam, and findmarkersParam. The remaining classes already have show in their scripts.
- Choose informative variable names - not
s
,y
,z
. See Review Checklist
I have revised variables s, y, and z and several of the shortest variable names to make them more informative (e.g. "s" was changed to "input_s", etc.).
Function argument names are descriptive and documented.
Function argument descriptions and names have been improved
- The image deconvolutionParam_hierarchy_diagram.jpeg is blurry when embedded in the rendered HTML document.
The old image (82KB) has been replaced with a higher resolution version (119KB).
- Vignette needs to have an Installation section. See Installation.
An installation section has been added to the package User's Guide.
- The main application is deconvolution with cell size adjustment. Some justification of why size adjustment is important should be added. Why is it important biologically? Does it enable new conclusions which are not possible without it?
I have added a subheading called "Biological importance of cell size scale factors" in the User's Guide to explain the definition and biological importance of cell size scale factors more clearly. The new pseudobulk example vignette also discusses this a little.
- Could the interoperability with existing Bioconductor packages be increased? Is anything in SingleCellData view reusable? Currently, all examples use simulated data and it may be more engaging to readers to see a biological data set.
A new vignette titled "Pseudobulk cell size rescaling example" shows a small pseudobulk example that uses snRNAseq data from the scRNAseq R/Bioconductor package. This shows results from scaling versus not scaling with cell size scale factors, and discusses a little about how to derive them.
Sincerely,
Sean
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Use camelCase: initial lowercase, then alternate case between words.
Also, replacing s
with input_s
is not more intuitive. Something such as sizeFactor
is. Bluster does e.g. nestedClusters
. Also the parameters not really been refactored but just reassigned internally by input_z <- z; input_s <- s; input_y <- y; input_yse <- y.se;
.
Using consistent variable and function naming style helps users when switching between Bioconductor packages. That's the intention of the contributor's guide but didn't apply to older packages accepted into Bioconductor a few years ago.
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Remember: if you submitted your package after July 7th, 2020,
when making changes to your repository push to
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A quick tutorial for setting up remotes and pushing to upstream can be found here.
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