Closed GabrielHoffman closed 1 year ago
Hi @GabrielHoffman
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: dreamlet
Type: Package
Title: Cohort-scale differential expression analysis of single cell data using linear (mixed) models
Version: 0.99.0
Date: 2023-03-15
Authors@R: person("Gabriel", "Hoffman", role=c("aut", "cre"), email="gabriel.hoffman@mssm.edu")
Description: Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.
VignetteBuilder: knitr
License: Artistic-2.0
Encoding: UTF-8
URL: https://DiseaseNeurogenomics.github.io/dreamlet
BugReports: https://github.com/DiseaseNeurogenomics/dreamlet/issues
Suggests:
BiocStyle,
knitr,
pander,
rmarkdown,
muscat,
ExperimentHub,
RUnit,
scater,
scuttle
biocViews: RNASeq,
GeneExpression,
DifferentialExpression,
BatchEffect,
QualityControl,
Regression,
GeneSetEnrichment,
GeneRegulation,
Epigenetics,
FunctionalGenomics,
Transcriptomics,
Normalization,
SingleCell,
Preprocessing,
Software
Depends:
R (>= 4.2.0),
ggplot2,
variancePartition (>= 1.28.3)
Imports:
edgeR,
SummarizedExperiment,
SingleCellExperiment,
DelayedMatrixStats,
sparseMatrixStats,
MatrixGenerics,
Matrix,
methods,
purrr,
GSEABase,
data.table,
zenith (>= 0.99.13),
mashr (>= 0.2.52),
ashr,
dplyr,
BiocParallel,
S4Vectors,
IRanges,
limma,
tidyr,
BiocGenerics,
HDF5Array,
DelayedArray,
gtools,
reshape2,
ggrepel,
scattermore,
Rcpp,
lme4,
MASS,
Rdpack,
utils,
stats
RoxygenNote: 7.2.3
RdMacros: Rdpack
SystemRequirements: C++11
LinkingTo:
Rcpp,
beachmat
crumblr is referenced in vignettes but not declared in DESCRIPTION
and i can't find it. private repo?
it has to be in CRAN or bioconductor for dreamlet to go in. please advise.
I have removed reference to this package in the latest push to GitHub (v0.99.1).
Reference to this package in the vignette had already been commented out, so a user would never have seen it. But now I removed it completely.
crumblr is a secondary package that I'm developing, but dreamlet does not depend on it.
Package .tar.gz is 11MB on my linux system. Sound right?
dreamlet/docs/deps/bootstrap-5.1.3/fonts/memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkWVAexg.woff
dreamlet/docs/deps/bootstrap-5.1.3/fonts/memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkaVQ.woff
dreamlet/docs/deps/bootstrap-5.1.3/fonts/memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk8ZkWVAexg.woff
do you want the docs folder or should it be in .Rbuildignore?
https://arxiv.org/pdf/2012.02936.pdf discusses inference post-clustering. Does the package address this concern?
1) The new version adds docs
to . Rbuildignore
, so tar.gz
is now 4.4M on my Mac.
2) The Gao, et al. work is very interesting, but separate from the primary problem addressed here. Gao, et al. consider finding genes that are differentially expressed between two clusters. dreamlet
does include this capability just for convenience, and I am well aware that the p-values are poorly calibrated.
But the primary goal of dreamlet
it to identify genes differentially expressed within a single cluster, but across subjects based on a variable of interest (i.e. case/control status). I submitted my manuscript to biorxiv on Friday, but they didn't post it yet. I will link to it as soon at it's up.
Gabriel
Preprint is now posted on biorxiv: https://doi.org/10.1101/2023.03.17.533005 and included in inst/CITATION
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Hi Vince, These warnings are almost all out of my control, these are the ones I couldn't resolve. Do you have technical suggestions on addressing them, or how to proceed?
these warnings should be fixable using name space discipline your reviewer should have some advice on the details
On Mon, Mar 27, 2023 at 3:48 PM Gabriel Hoffman @.***> wrote:
Hi Vince, These warnings are almost all out of my control, these are the ones I couldn't resolve. Do you have technical suggestions on addressing them, or how to proceed?
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Hi @jianhong
I have resolved almost all the warnings with my package submission, but there is one I can't address. The runtime of the vignettes was exceeding the 15 min limit, so I precompiled the Rmd
files as described here. This reduced the runtime, but now R warns that I don't evaluate enough of the chunks in the markdown file, since it is precompiled:
* Checking vignette directory...
* WARNING: Evaluate more vignette chunks.
86 code chunks / 92 total = 93% percent unevaluated
86 non-exec code chunks (e.g., '``` r')
Either way I handle the vignette, R gives a warning.
Given this, can you overlook the warning and accept the package to Bioconductor? Or do you have any suggestions?
Best, Gabriel
Is it possible to try smaller dataset like pbmc_small in seurat package?
Unfortunately no. The focus of this package is variation in one expression across individuals. I have put a huge amount of work into the documentation in order to give users good intuition and reproducible examples.
Our mac builder went down sometime over night; It could be the timeout was related to it failing. Looking at the previous reports it looks like that when you were running the code it passed without warning on linux; If this is the case I would lean towards executing as much as possible within the time restraints of the linux builder
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Thank you for submition your package to Bioconductor. The package passed check and build. However there are several things need to be fixed. Please try to answer the comments line by line when you are ready for a second review.
Code: Note: please condsider; Important: must be addressed.
importFrom
instead of import all with import
.
vapply
instead of sapply
.
::
is not suggested in source code unless you can make sure all the packages are imported. Some people think it is better to keep ::
. However please note that you need to manully double check the import items when you make any change in the DESCRIPTION file during development. My recommendation is to remove one or two repeats to force the dependency check.for
loops present, try to replace them by *apply
funcitons.
drop=FALSE/TRUE
to avoid/sure the reduction of dimension for matrices and arrays. Ignore this if using datatable.
aggregateNonCountSignal
and aggregateVar
aggregateNonCountSignal
and processAssays
and aggregateVar
assay
and assayNames
and colData
and colData<-
assayNames
and metadata
checkFormula
and dropRedundantTerms
and removeConstantTerms
dropRedundantTerms
and removeConstantTerms
BiocStyle
package for formatting.
motivation for submitting to Bioconductor
as part of the abstract/intro of the main vignette.
Could I expect updates soon? Otherwise it will be closed because no updates within 30 days.
Thank you for following up. I have made the changes you requested, but I'm still testing after updating some package dependencies that I also manage. I hope to submit this latest version within a week
Gabriel
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Could I expect the error fix soon?
Thanks for following up. I fixed the issues with the dependencies
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