Closed federicomarini closed 3 years ago
Hi @federicomarini
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: quantiseqr
Title: Quantification of the Tumor Immune contexture from RNA-seq data
Version: 0.99.0
Authors@R:
c(
person(
given = "Federico", family = "Marini", role = c("aut", "cre"),
email = "marinif@uni-mainz.de", comment = c(ORCID = "0000-0003-3252-7758")
),
person(
given = "Francesca", family = "Finotello", role = "aut",
email = "francesca.finotello@i-med.ac.at", comment = c(ORCID = "0000-0003-0712-4658")
)
)
Description: This package provides a streamlined workflow for the quanTIseq method,
developed to perform the quantification of the Tumor Immune contexture from
RNA-seq data. The quantification is performed against the TIL10 signature
(dissecting the contributions of ten immune cell types), carefully crafted
from a collection of human RNA-seq samples. The TIL10 signature has been
extensively validated using simulated, flow cytometry, and
immunohistochemistry data.
License: GPL-3
Depends:
R (>= 4.0.0)
Imports:
Biobase,
limSolve,
MASS,
methods,
preprocessCore,
stats,
SummarizedExperiment,
ggplot2,
tidyr,
rlang,
utils
Suggests:
AnnotationDbi,
BiocStyle,
dplyr,
GEOquery,
knitr,
macrophage,
org.Hs.eg.db,
reshape2,
rmarkdown,
testthat,
tibble
biocViews:
GeneExpression, Software, Transcription, Transcriptomics, Sequencing, Microarray,
Visualization, Annotation, ImmunoOncology, FeatureExtraction, Classification,
StatisticalMethod, ExperimentHubSoftware
VignetteBuilder: knitr
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.1
I am tagging @FFinotello as co-author for this package, and @lshep as this package contains a single entry for ExperimentHub
(a SummarizedExperiment dataset, used in the vignette - currently the related chunks are disabled, but it is one of four examples, so the rest of the demonstrative code is up and running)
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Received a valid push on git.bioconductor.org; starting a build for commit id: 5f08120a39896badac2b8dcf9566aa29f080a092
Since I am new to the "new" version of the package tracker - I would be expecting a build report after bumping the version, but I do not see any (yet?). Should I do a gentle version bump to make sure it is triggered?
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Received a valid push on git.bioconductor.org; starting a build for commit id: 5896e7f3e7ef352dc215339fb100289eeec76c48
Dear Package contributor,
This is the automated single package builder at bioconductor.org.
Your package has been built on Linux, Mac, and Windows.
On one or more platforms, the build results were: "WARNINGS". This may mean there is a problem with the package that you need to fix. Or it may mean that there is a problem with the build system itself.
Please see the build report for more details. This link will be active for 21 days.
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Received a valid push on git.bioconductor.org; starting a build for commit id: a8112d2ab549c3034a1a6f9d9ada622cd50238f5
Dear Package contributor,
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Your package has been built on Linux, Mac, and Windows.
On one or more platforms, the build results were: "ERROR, WARNINGS, skipped". This may mean there is a problem with the package that you need to fix. Or it may mean that there is a problem with the build system itself.
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Hi @vjcitn - I was not able to reproduce the Windows warning, normally I rely on GH Actions but it is now a pretty troubled phase there.
Re: the specific MacOS error (polygon edge not found
, looks pretty ggplot-specific) - that is also not popping up in my current setup, I am trying on Docker to see if it is triggered 😉
EDIT: tried on Docker with latest devel setup, no issue.
I also installed a latest R devel (R version 4.1.0 alpha (2021-04-29 r80247)
) on my laptop, and could not reproduce the polygon edge not found
🤷
Hi @federicomarini -- there have been some issues with Single Package Builder, so we'll have to be patient about responses to commits. For now the package check performance seems good.
The dataset quantiseqr::dataset_racle contains - a gene expression matrix (dataset_racle$expr_mat) generated using bulk RNA-seq - ‘gold standard’ estimates of immune cell contents profiled with FACS (dataset_racle$ref).
is unclear to me. Oh I think you are using markdown, trying to get a bulleted list -- but you need a newline before the items
is GEOquery caching the material that you download? If so it might be nice to mention this. If not, maybe use BiocFileCache?
It seems to me desirable to avoid dumps like
in vignettes. Some rounding and subsetting will get the point across. Are confidence intervals available?
I had bad luck with figure in FAQ
This is a shortcoming with my laptop I guess ... but will others suffer this?
I think explicit captioning on figures is helpful.
Hi @federicomarini -- there have been some issues with Single Package Builder, so we'll have to be patient about responses to commits. For now the package check performance seems good.
Thanks for chiming in Vince - indeed I have seen sometimes Mac+Win, other times Win only, the latest one is on all three...
We'll start working on the points you noted down below.
Good catch for the transparency issue - I never encountered till now, what system are you on for the laptop? I'll see what it looks like without it, if there's not too much of overcrowding in the points.
It is an ubuntu 20 dell xps ... crummy drivers, keyboard etc. so maybe it is not a big deal. if the builders get it right maybe ignore this.
I can try to check this out on the Docker instance - of course, no guarantee it mirrors your builder setup as a whole, but can give some hints. We'll keep this as a flagged item to check 😉
Hi @vjcitn, thank you again for the first round of comments. We have addressed them in our recent commits, and provide specific detail on each point in our reply below.
Is it possible to add some more substantive information in the vignette introduction? Any references to TIL10 signature creation and use?
We expanded the introduction section in the vignette, detailing the content of the TIL10 signature, with proper literature citation, and what to expect from a typical run of quantiseqr
.
This sentence "The dataset quantiseqr::dataset_racle contains - a gene expression matrix (dataset_racle$expr_mat) generated using bulk RNA-seq - ‘gold standard’ estimates of immune cell contents profiled with FACS (dataset_racle$ref)." is unclear to me. Oh I think you are using markdown, trying to get a bulleted list -- but you need a newline before the items
Yes, that was the issue. Thank you for catching this.
is GEOquery caching the material that you download? If so it might be nice to mention this. If not, maybe use BiocFileCache?
The first call to GEOquery does retrieve a small ~700kb file from the supplemental material, and we could not find an option to cache this. When the second dataset is retrieved, it is cached - we reported this explicitly in the vignette text.
It seems to me desirable to avoid dumps like [...] in vignettes. Some rounding and subsetting will get the point across. Are confidence intervals available?
Thank you for the suggestion regarding the subsetting and rounding.
As for the second question, quantiseqr
(and quanTIseq) only provide point estimates, without confidence intervals.
Here's a question about the correlation plots displayed before 3.3 -- the lines drawn are data-driven but a good result would have the points on the line of identity.
Good point. The current plot was replicating the one included in the original publication, which was referring to similar plots where the linear model fit was displayed, instead of the identity line. We added both lines, with different traits and colors, and mentioned this in the figure legend.
The big table dump in 3.3 should be compacted somehow
The full info of the colData slot is now removed, we left only the information on the column names to display how the columns corresponding to the cell fractions are named when providing a SummarizedExperiment
I had bad luck with figure in FAQ [...] This is a shortcoming with my laptop I guess ... but will others suffer this?
Could be indeed an issue related to your specific machine - we could not reproduce it on our testing setups. As an alternative, we can consider switching to density-based hexagonal geometries, but probably it is worth trying to see if the builders can handle this.
I think explicit captioning on figures is helpful.
Indeed it is - we added explicit figure captions on all generated figures. Thanks for the suggestion, it is also better for the document accessibility.
Received a valid push on git.bioconductor.org; starting a build for commit id: caab7528891a9cbbfc7c3e0c8e8f8395158867d5
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Please see the build report for more details. This link will be active for 21 days.
Remember: if you submitted your package after July 7th, 2020,
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"Consider adding these automatically suggested biocViews: FlowCytometry"
Your package has been accepted. It will be added to the Bioconductor nightly builds.
Thank you for contributing to Bioconductor!
"Consider adding these automatically suggested biocViews: FlowCytometry"
Given that we do not really "handle" or process such data, we opted to not add that as biocViews item - does this sound reasonable to you as well?
Thank you for the happy news, by the way!
that is fine, but is it really an erroneous suggestion? at least two of your use cases in vignette refer to the gold standard by flow, and you have some such data in there, right? i think those interested in flow would be well-advised to have a look at your applications
On Fri, May 7, 2021 at 4:51 AM Federico Marini @.***> wrote:
"Consider adding these automatically suggested biocViews: FlowCytometry"
Given that we do not really "handle" or process such data, we opted to not add that as biocViews item - does this sound reasonable to you as well?
Thank you for the happy news, by the way!
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