File R-MetabolicSurv.spec of Package R-MetabolicSurv
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# Spec file for package MetabolicSurv
# This file is auto-generated using information in the package source,
# esp. Description and Summary. Improvements in that area should be
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%global packname MetabolicSurv
%global rlibdir %{_libdir}/R/library
Name: R-%{packname}
Version: 1.1.2
Release: 0
Summary: A Biomarker Validation Approach for Classification and Predicting Survival Using Metabolomics Signature
Group: Development/Libraries/Other
License: GPL-3
URL: http://cran.r-project.org/web/packages/%{packname}
Source: MetabolicSurv_1.1.2.tar.gz
Requires: R-base
Requires: R-superpc
Requires: R-glmnet
Requires: R-matrixStats
Requires: R-survminer
Requires: R-rms
Requires: R-tidyr
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Requires: R-Rdpack
Requires: R-ggplot2
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Requires: R-withr
Requires: R-foreach
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Requires: R-Rcpp
Requires: R-RcppEigen
Requires: R-rbibutils
Requires: R-Hmisc
Requires: R-quantreg
Requires: R-SparseM
Requires: R-polspline
Requires: R-multcomp
Requires: R-htmlTable
Requires: R-htmltools
Requires: R-digest
Requires: R-colorspace
Requires: R-knitr
Requires: R-ggpubr
Requires: R-gridExtra
Requires: R-maxstat
Requires: R-broom
Requires: R-survMisc
Requires: R-purrr
Requires: R-ggtext
Requires: R-stringr
Requires: R-cpp11
Requires: R-backports
Requires: R-iterators
Requires: R-ggrepel
Requires: R-ggsci
Requires: R-cowplot
Requires: R-ggsignif
Requires: R-polynom
Requires: R-rstatix
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Requires: R-data.table
Requires: R-viridis
Requires: R-base64enc
Requires: R-rmarkdown
Requires: R-Formula
Requires: R-checkmate
Requires: R-htmlwidgets
Requires: R-rstudioapi
Requires: R-fastmap
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Requires: R-highr
Requires: R-xfun
Requires: R-yaml
Requires: R-exactRankTests
Requires: R-mvtnorm
Requires: R-TH.data
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Requires: R-farver
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Requires: R-stringi
Requires: R-KMsurv
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Requires: R-xtable
Requires: R-pkgconfig
Requires: R-curl
Requires: R-markdown
Requires: R-png
Requires: R-jpeg
Requires: R-xml2
Requires: R-jsonlite
Requires: R-bslib
Requires: R-fontawesome
Requires: R-jquerylib
Requires: R-tinytex
Requires: R-corrplot
Requires: R-car
Requires: R-cachem
Requires: R-memoise
Requires: R-mime
Requires: R-sass
Requires: R-carData
Requires: R-abind
Requires: R-pbkrtest
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Requires: R-litedown
Requires: R-commonmark
Requires: R-minqa
Requires: R-nloptr
Requires: R-reformulas
Requires: R-numDeriv
Requires: R-doBy
Requires: R-fs
Requires: R-rappdirs
Requires: R-Deriv
Requires: R-modelr
Requires: R-microbenchmark
# %%if 0%%{?sle_version} > 120400 || 0%%{?is_opensuse}
# # Three others commonly needed
# BuildRequires: tex(ae.sty)
# BuildRequires: tex(fancyvrb.sty)
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BuildRequires: R-evaluate
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BuildRequires: R-doBy
BuildRequires: R-fs
BuildRequires: R-rappdirs
BuildRequires: R-Deriv
BuildRequires: R-modelr
BuildRequires: R-microbenchmark
Suggests: R-knitr
Suggests: R-rmarkdown
%description
An approach to identifies metabolic biomarker signature for metabolic
data by discovering predictive metabolite for predicting survival and
classifying patients into risk groups. Classifiers are constructed as a
linear combination of predictive/important metabolites, prognostic
factors and treatment effects if necessary. Several methods were
implemented to reduce the metabolomics matrix such as the principle
component analysis of Wold Svante et al. (1987)
<doi:10.1016/0169-7439(87)80084-9> , the LASSO method by Robert
Tibshirani (1998)
<doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>,
the elastic net approach by Hui Zou and Trevor Hastie (2005)
<doi:10.1111/j.1467-9868.2005.00503.x>. Sensitivity analysis on the
quantile used for the classification can also be accessed to check the
deviation of the classification group based on the quantile specified.
Large scale cross validation can be performed in order to investigate
the mostly selected predictive metabolites and for internal validation.
During the evaluation process, validation is accessed using the hazard
ratios (HR) distribution of the test set and inference is mainly based
on resampling and permutations technique.
%prep
%setup -q -c -n %{packname}
# the next line is needed, because we build without --clean in between two packages
rm -rf ~/.R
%build
%install
mkdir -p %{buildroot}%{rlibdir}
%{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname}
test -d %{packname}/src && (cd %{packname}/src; rm -f *.o *.so)
rm -f %{buildroot}%{rlibdir}/R.css
%fdupes -s %{buildroot}%{rlibdir}
#%%check
#%%{_bindir}/R CMD check %%{packname}
%files
%dir %{rlibdir}/%{packname}
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/REFERENCES.bib
%{rlibdir}/%{packname}/data
%{rlibdir}/%{packname}/doc
%{rlibdir}/%{packname}/extdata
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
%changelog
