Closed songfd2018 closed 3 years ago
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The DESCRIPTION file for this package is:
Package: BUSseq
Type: Package
Title: Batch Effect Correction with Unknow Subtypes for scRNA-seq data
Version: 0.99.0
Date: 2021-05-04
Description: BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value can be used for downstream analysis as if all cells were sequenced in a single batch. BUSseq can integrate read count matrices obtained from different scRNA-seq platforms and allow cell types to be measured in some but not all of the batches as long as the experimental design fulfills the conditions listed in our manuscript.
Authors@R: c(person(given = "Fangda", family = "Song", role = c("aut","cre"), email = "sfd1994895@gmail.com", comment = c(ORCID = "0000-0001-6007-3517")), person("Ga Ming", "Chan", role = c("aut"), email = "middletotheleft@gmail.com"), person("Yingying", "Wei", role = c("aut"), email = "ywei@cuhk.edu.hk", comment = c(ORCID = "0000-0003-3826-336X")))
License: Artistic-2.0
Imports: gplots, grDevices, methods, stats, utils
Suggests: BiocStyle, knitr, BiocGenerics
LazyData: true
Keywords: Single-cell RNA-seq experiments; Batch effects; Model-based clustering; Integrative analysis
VignetteBuilder: knitr
biocViews: ExperimentalDesign, GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect, SingleCell, Sequencing
URL: https://github.com/songfd2018/BUSseq
BugReports: https://github.com/songfd2018/BUSseq/issues
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I apologize for the delay in review of this package. I am glad it is passing check nicely now. There are in my view a number of improvements that should be considered. First, there is no use of shared Bioconductor infrastructure in the package. Thus the inputs are to be a list of matrices, whereas it would be typical to use SummarizedExperiment or SingleCellExperiment to manage the assay data (counts) along with relevant cell-level or experiment-level metadata. I would strongly encourage adoption of this approach. When we are using unannotated matrices as in the vignette, a risk arises that rows are shuffled independently of associated identifiers, and data become uninterpretable or misleading.
A second concern is acknowledgment of limitations. The vignette includes the remark that "BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data." In what sense does it do all these things in a reliable way? Is there a measure of accuracy of batch effect correction, and has BUSseq been shown to outperform, say, spike-in-based correction, and if so, under what conditions? These topics may be addressed in your paper, and if so, please pass a few key findings to the user through the vignette. Thanks for submitting your package and I hope this review is helpful!
Thanks a lot for your kind comments. I will try to allow the input of our algorithm as a SummarizedExperiment or SingleCellExperiment object and outline the advantages of BUSseq through the vignette.
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Dear Reviewer,
I truly appreciate your precious and constructive comments. Please find our point-to-point response as follows.
First, there is no use of shared Bioconductor infrastructure in the package. Thus the inputs are to be a list of matrices, whereas it would be typical to use SummarizedExperiment or SingleCellExperiment to manage the assay data (counts) along with relevant cell-level or experiment-level metadata. I would strongly encourage adoption of this approach. When we are using unannotated matrices as in the vignette, a risk arises that rows are shuffled independently of associated identifiers, and data become uninterpretable or misleading.
For the first point, the revised package has allowed both a SingleCellExperiment
object or a list
as the input of the main MCMC algorithm function BUSseq_MCMC
. Meanwhile, the output of BUSseq_MCMC
is also a SingleCellExperiment
object. Based on the input, BUSseq_MCMC
incorporates a new assay of the imputed count data into the output object and adds the posterior inference as the package-specific metadata int_metadata(sce)$BUSseq
, where sce
is the name of the output. Furthermore, all the other functions are based on the SingleCellExperiment
object generated by the BUSseq_MCMC
function.
A second concern is acknowledgment of limitations. The vignette includes the remark that "BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data." In what sense does it do all these things in a reliable way?
In the vignette, I add a new section Methodology
to show that the hierarchal structure of our BUSseq model mimics the data generation mechanism of scRNA-seq data. Meanwhile, diverse model parameters or latent variables can characterize the batch effects, cell type labels, overdispersion levels, and dropout event occurrence as well as cell-specific size factors. Moreover, in our paper, we mathematically prove that our model is identifiable up to the label switching under mild conditions. In other words, model parameters can be correctly recovered by the observed read count data, even if the observed data suffer from dropout events and batch effects. We further conduct simulation studies to verify our theoretical results. As a result, BUSseq can provide a one-stop service to do all these things simultaneously.
Is there a measure of accuracy of batch effect correction, and has BUSseq been shown to outperform, say, spike-in-based correction, and if so, under what conditions? These topics may be addressed in your paper, and if so, please pass a few key findings to the user through the vignette.
If we directly cluster all the cells from different scRNA-seq experiments, then cells are often separated by experiment or batch rather than by cell type due to severe batch effects. Therefore, in our paper, we clustered cells based on the corrected data or the learned low-dimension embeddings generated by different batch-effect-correction methods. To benchmark different methods, we compare the consistency between the reference cell-type labels and the estimated labels of each method. As a result, BUSseq outperforms six other state-of-the-art methods in both the mouse hematopoietic study and the human pancreatic study. To present our results, I add a new section Performance of BUSseq in real data analysis
in the vignette.
Thanks a lot again! Please kindly let me know if there is any issue in my package.
Best regards, Fangda
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