;;; GNU Guix --- Functional package management for GNU ;;; Copyright © 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 Ricardo Wurmus ;;; Copyright © 2016, 2017, 2018, 2020, 2021 Roel Janssen ;;; Copyright © 2016 Pjotr Prins ;;; Copyright © 2016 Ben Woodcroft ;;; Copyright © 2017 Efraim Flashner ;;; Copyright © 2017, 2018, 2019, 2020, 2021 Tobias Geerinckx-Rice ;;; Copyright © 2019, 2020, 2021, 2022 Simon Tournier ;;; Copyright © 2020 Peter Lo ;;; Copyright © 2020, 2021, 2022 Mădălin Ionel Patrașcu ;;; Copyright © 2020 Jakub Kądziołka ;;; Copyright © 2021 Hong Li ;;; Copyright © 2021 Tim Howes ;;; Copyright © 2021 Nicolas Vallet ;;; ;;; This file is part of GNU Guix. ;;; ;;; GNU Guix is free software; you can redistribute it and/or modify it ;;; under the terms of the GNU General Public License as published by ;;; the Free Software Foundation; either version 3 of the License, or (at ;;; your option) any later version. ;;; ;;; GNU Guix is distributed in the hope that it will be useful, but ;;; WITHOUT ANY WARRANTY; without even the implied warranty of ;;; MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ;;; GNU General Public License for more details. ;;; ;;; You should have received a copy of the GNU General Public License ;;; along with GNU Guix. If not, see . (define-module (gnu packages bioconductor) #:use-module ((guix licenses) #:prefix license:) #:use-module (guix packages) #:use-module (guix download) #:use-module (guix git-download) #:use-module (guix build-system r) #:use-module (gnu packages) #:use-module (gnu packages base) #:use-module (gnu packages bioinformatics) #:use-module (gnu packages boost) #:use-module (gnu packages cran) #:use-module (gnu packages compression) #:use-module (gnu packages curl) #:use-module (gnu packages docker) #:use-module (gnu packages gcc) #:use-module (gnu packages graph) #:use-module (gnu packages graphviz) #:use-module (gnu packages haskell-xyz) #:use-module (gnu packages image) #:use-module (gnu packages maths) #:use-module (gnu packages netpbm) #:use-module (gnu packages perl) #:use-module (gnu packages pkg-config) #:use-module (gnu packages statistics) #:use-module (gnu packages web) #:use-module (gnu packages xml) #:use-module (srfi srfi-1)) ;;; Annotations (define-public r-org-eck12-eg-db (package (name "r-org-eck12-eg-db") (version "3.12.0") (source (origin (method url-fetch) (uri (bioconductor-uri "org.EcK12.eg.db" version 'annotation)) (sha256 (base32 "0c4p6jr83k0gm6pvn760yr8xf33wggrfcr6fg7a42a96bcf817gs")))) (properties `((upstream-name . 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Between 49 and 77 cells were captured at each of four time points (0, 24, 48, 72 hours) following serum switch using the Fluidigm C1 microfluidic system. RNA from each cell was isolated and used to construct mRNA-Seq libraries, which were then sequenced to a depth of ~4 million reads per library, resulting in a complete gene expression profile for each cell.") (license license:artistic2.0))) (define-public r-all (package (name "r-all") (version "1.38.0") (source (origin (method url-fetch) (uri (bioconductor-uri "ALL" version 'experiment)) (sha256 (base32 "0410045x327wmfkksshd8yishw4yxij08vn8p65cdj7hb3qy3p0z")))) (properties `((upstream-name . "ALL"))) (build-system r-build-system) (propagated-inputs (list r-biobase)) (home-page "https://bioconductor.org/packages/ALL") (synopsis "Acute Lymphoblastic Leukemia data from the Ritz laboratory") (description "The data consist of microarrays from 128 different individuals with @dfn{acute lymphoblastic leukemia} (ALL). 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The @code{ADImpute} package proposes two methods to address this issue: @enumerate @item a gene regulatory network-based approach using gene-gene relationships learnt from external data; @item a baseline approach corresponding to a sample-wide average. @end enumerate @code{ADImpute} implements these novel methods and also combines them with existing imputation methods like @code{DrImpute} and @code{SAVER}. @code{ADImpute} can learn the best performing method per gene and combine the results from different methods into an ensemble.") (license license:gpl3+))) (define-public r-adsplit (package (name "r-adsplit") (version "1.66.0") (source (origin (method url-fetch) (uri (bioconductor-uri "adSplit" version)) (sha256 (base32 "1wl2gd0b7krf485clw67cxayp0g9argklkzn8nw1vrkil0vvr4jm")))) (properties `((upstream-name . 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(license license:gpl2+))) (define-public r-aggregatebiovar (package (name "r-aggregatebiovar") (version "1.6.0") (source (origin (method url-fetch) (uri (bioconductor-uri "aggregateBioVar" version)) (sha256 (base32 "0ngg12bgr95m4wm12scmrb55dgy4909c6qrg169l6dkng99v4nx1")))) (properties `((upstream-name . "aggregateBioVar"))) (build-system r-build-system) (propagated-inputs (list r-matrix r-rlang r-s4vectors r-singlecellexperiment r-summarizedexperiment r-tibble)) (native-inputs (list r-knitr)) (home-page "https://github.com/jasonratcliff/aggregateBioVar") (synopsis "Differential gene expression analysis for multi-subject scRNA-seq") (description "This package @code{aggregateBioVar} contains tools to summarize single cell gene expression profiles at the level of subject for single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates). A @code{SingleCellExperiment} object is taken as input and converted to a list of @code{SummarizedExperiment} objects, where each list element corresponds to an assigned cell type. The @code{SummarizedExperiment} objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools.") (license license:gpl3))) (define-public r-agilp (package (name "r-agilp") (version "3.28.0") (source (origin (method url-fetch) (uri (bioconductor-uri "agilp" version)) (sha256 (base32 "1pm329y2nfcnx98ggxq0prdd5pxfcl5iylvsjjnhw5lyz1awg1yf")))) (properties `((upstream-name . 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(license license:artistic2.0))) (define-public r-agimicrorna (package (name "r-agimicrorna") (version "2.46.0") (source (origin (method url-fetch) (uri (bioconductor-uri "AgiMicroRna" version)) (sha256 (base32 "0jic89gyphbv7jzlfgm9bh1aq48lp86rq6hr34gsg9z0pa1192xa")))) (properties `((upstream-name . "AgiMicroRna"))) (build-system r-build-system) (propagated-inputs (list r-affy r-affycoretools r-biobase r-limma r-preprocesscore)) (home-page "https://git.bioconductor.org/packages/AgiMicroRna") (synopsis "Processing and differential expression analysis of Agilent microRNA chips") (description "@code{AgiMicroRna} provides useful functionality for the processing, quality assessment and differential expression analysis of Agilent microRNA array data. The package uses a limma-like structure to generate the processed data in order to make statistical inferences about differential expression using the linear model features implemented in limma. 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The analysis methods apply to different technologies, including microarrays, RNA-seq, and quantitative PCR.") (license license:gpl2+))) (define-public r-methylkit (package (name "r-methylkit") (version "1.22.0") (source (origin (method url-fetch) (uri (bioconductor-uri "methylKit" version)) (sha256 (base32 "00asjzv05avfg0rrkmfbdqd6xx8d18zi72n3b1kf9wj81z2d2a35")))) (properties `((upstream-name . "methylKit"))) (build-system r-build-system) (propagated-inputs (list r-data-table r-emdbook r-fastseg r-genomeinfodb r-genomicranges r-gtools r-iranges r-kernsmooth r-limma r-mclust r-mgcv r-qvalue r-r-utils r-rcpp r-rhtslib r-rsamtools r-rtracklayer r-s4vectors r-zlibbioc)) (native-inputs (list r-knitr)) ; for vignettes (home-page "https://github.com/al2na/methylKit") (synopsis "DNA methylation analysis from high-throughput bisulfite sequencing results") (description "MethylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from @dfn{Reduced representation bisulfite sequencing} (RRBS) and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq.") (license license:artistic2.0))) (define-public r-motifrg (package (name "r-motifrg") (version "1.31.0") (source (origin (method url-fetch) (uri (bioconductor-uri "motifRG" version)) (sha256 (base32 "1ml6zyzlk8yjbnfhga2qnw8nl43rankvka0kc1yljxr2b66aqbhn")))) (properties `((upstream-name . 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It also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc.") (license license:artistic2.0))) (define-public r-mzid (package (name "r-mzid") (version "1.34.0") (source (origin (method url-fetch) (uri (bioconductor-uri "mzID" version)) (sha256 (base32 "1q1aqyya9nd494s7m3rdaf3kixipdrwbj825g40kdljwrg85y961")))) (properties `((upstream-name . "mzID"))) (build-system r-build-system) (propagated-inputs (list r-doparallel r-foreach r-iterators r-plyr r-protgenerics r-xml)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/mzID") (synopsis "Parser for mzIdentML files") (description "This package provides a parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less pretty output than a vendor specific parser.") 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The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining.") ;; Any version of the LGPL. (license license:lgpl3+))) (define r-rcppnumerical (package (name "r-rcppnumerical") (version "0.4-0") (source (origin (method url-fetch) (uri (cran-uri "RcppNumerical" version)) (sha256 (base32 "1a92fql6mijhnr1kxkcxwivf95pk9lhgmhzkshs51h0ybfv5krik")))) (properties `((upstream-name . "RcppNumerical"))) (build-system r-build-system) (propagated-inputs `(("r-rcpp" ,r-rcpp) ("r-rcppeigen" ,r-rcppeigen))) (native-inputs `(("r-knitr" ,r-knitr))) (home-page "https://github.com/yixuan/RcppNumerical") (synopsis "Rcpp integration for numerical computing libraries") (description "This package provides a collection of open source libraries for numerical computing (numerical integration, optimization, etc.) and their integration with @code{Rcpp}.") 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(license license:expat))) (define-public r-ropls (package (name "r-ropls") (version "1.28.0") (source (origin (method url-fetch) (uri (bioconductor-uri "ropls" version)) (sha256 (base32 "1i9g32mjknrd2lbz2nisc7nr0gk9byx9aik1i7xdwmq4ndxf8rf7")))) (build-system r-build-system) (propagated-inputs (list r-biobase r-multidataset)) (native-inputs (list r-knitr)) ; for vignettes (home-page "https://dx.doi.org/10.1021/acs.jproteome.5b00354") (synopsis "Multivariate analysis and feature selection of omics data") (description "Latent variable modeling with @dfn{Principal Component Analysis} (PCA) and @dfn{Partial Least Squares} (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. @dfn{Orthogonal Partial Least Squares} (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. This package provides imlementations of PCA, PLS, and OPLS for multivariate analysis and feature selection of omics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients).") (license license:cecill))) (define-public r-biosigner (package (name "r-biosigner") (version "1.24.0") (source (origin (method url-fetch) (uri (bioconductor-uri "biosigner" version)) (sha256 (base32 "11w28nlz0f06idbna5k9xs891w516401l1zab5gg7s0z3xxaki4f")))) (build-system r-build-system) (propagated-inputs (list r-biobase r-e1071 r-multidataset r-randomforest r-ropls)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/biosigner/") (synopsis "Signature discovery from omics data") (description "Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets.") (license license:cecill))) (define-public r-annotatr (package (name "r-annotatr") (version "1.22.0") (source (origin (method url-fetch) (uri (bioconductor-uri "annotatr" version)) (sha256 (base32 "1fbax9v3d486c8lwf3yfjbf4w7zf53wmdpxk2clwm8ngm7w0pqm0")))) (build-system r-build-system) (propagated-inputs (list r-annotationdbi r-annotationhub r-dplyr r-genomeinfodb r-genomicfeatures r-genomicranges r-ggplot2 r-iranges r-readr r-regioner r-reshape2 r-rtracklayer r-s4vectors)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/annotatr/") (synopsis "Annotation of genomic regions to genomic annotations") (description "Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations.") (license license:gpl3))) (define-public r-rsubread (package (name "r-rsubread") (version "2.10.2") (source (origin (method url-fetch) (uri (bioconductor-uri "Rsubread" version)) (sha256 (base32 "1pvyx8nxv10s816hgxrnw60hp9rlqjdgjq3ajpzqxgxmmlhnk0v0")))) (properties `((upstream-name . "Rsubread"))) (build-system r-build-system) (inputs (list zlib)) (propagated-inputs (list r-matrix)) (home-page "https://bioconductor.org/packages/Rsubread/") (synopsis "Subread sequence alignment and counting for R") (description "This package provides tools for alignment, quantification and analysis of second and third generation sequencing data. It includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. It can be applied to all major sequencing techologies and to both short and long sequence reads.") (license license:gpl3))) (define-public r-flowutils (package (name "r-flowutils") (version "1.59.0") (source (origin (method url-fetch) (uri (bioconductor-uri "flowUtils" version)) (sha256 (base32 "11x362dqb9mjlsbq6g1qkb8hhnkvm22z5s3wkgmpyy9kyifjkm26")))) (properties `((upstream-name . "flowUtils"))) (build-system r-build-system) (propagated-inputs (list r-biobase r-corpcor r-flowcore r-graph r-runit r-xml)) (home-page "https://github.com/jspidlen/flowUtils") (synopsis "Utilities for flow cytometry") (description "This package provides utilities for flow cytometry data.") (license license:artistic2.0))) (define-public r-consensusclusterplus (package (name "r-consensusclusterplus") (version "1.60.0") (source (origin (method url-fetch) (uri (bioconductor-uri "ConsensusClusterPlus" version)) (sha256 (base32 "1021cix4mr9qsafskw4kk1l3wdzx9pk2gcwjifz6f4zqxss9v07p")))) (properties `((upstream-name . "ConsensusClusterPlus"))) (build-system r-build-system) (propagated-inputs (list r-all r-biobase r-cluster)) (home-page "https://bioconductor.org/packages/ConsensusClusterPlus") (synopsis "Clustering algorithm") (description "This package provides an implementation of an algorithm for determining cluster count and membership by stability evidence in unsupervised analysis.") (license license:gpl2))) ;; This is the latest commit and it solves a bug from the latest release. (define-public r-cycombine (let ((commit "f18504bc83ff5daee2b5eb4b28f09abdaaa66698") (revision "1")) (package (name "r-cycombine") (version (git-version "0.2.6" revision commit)) (source (origin (method git-fetch) (uri (git-reference (url "https://github.com/biosurf/cyCombine") (commit commit))) (file-name (git-file-name name version)) (sha256 (base32 "1fiwnik8iahg01732fik85xhz359x32f1xc59h443pdf7jancskm")))) (properties `((upstream-name . "cyCombine"))) (build-system r-build-system) (propagated-inputs (list r-biobase r-cytolib r-dplyr r-flowcore r-ggplot2 r-knitr r-kohonen r-magrittr r-purrr r-rcolorbrewer r-readr r-readxl r-stringr r-sva r-tibble r-tidyr)) (native-inputs (list r-knitr)) (home-page "https://github.com/biosurf/cyCombine") (synopsis "Integration of single-cell cytometry datasets") (description "This package provides a method for combining single-cell cytometry datasets, which increases the analytical flexibility and the statistical power of the analyses while minimizing technical noise.") (license license:expat)))) (define-public r-cytolib (package (name "r-cytolib") (version "2.8.0") (source (origin (method url-fetch) (uri (bioconductor-uri "cytolib" version)) (sha256 (base32 "18b532sicca5l8sn334prrm7g1z1cakiwydccz4i833168pnsjyg")))) (properties `((upstream-name . "cytolib"))) (build-system r-build-system) (native-inputs (list r-knitr)) (propagated-inputs (list r-bh r-rcpp r-rcpparmadillo r-rcppparallel r-rhdf5lib r-rprotobuflib)) (home-page "https://bioconductor.org/packages/cytolib/") (synopsis "C++ infrastructure for working with gated cytometry") (description "This package provides the core data structure and API to represent and interact with gated cytometry data.") (license license:artistic2.0))) (define-public r-flowcore (package (name "r-flowcore") (version "2.8.0") (source (origin (method url-fetch) (uri (bioconductor-uri "flowCore" version)) (sha256 (base32 "17nci6rc4i0vs0ibw5q8zy30ap7q4550qpq4ifkbblqbyzxlzkhr")))) (properties `((upstream-name . "flowCore"))) (build-system r-build-system) (propagated-inputs (list r-bh r-biobase r-biocgenerics r-cytolib r-matrixstats r-rcpp r-rcpparmadillo r-rprotobuflib r-s4vectors)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/flowCore") (synopsis "Basic structures for flow cytometry data") (description "This package provides S4 data structures and basic functions to deal with flow cytometry data.") (license license:artistic2.0))) (define-public r-flowmeans (package (name "r-flowmeans") (version "1.56.0") (source (origin (method url-fetch) (uri (bioconductor-uri "flowMeans" version)) (sha256 (base32 "1n4li43ydwwf5gvgmdml4ba28cxymybg5wnz6jvp35n959fwxv6y")))) (properties `((upstream-name . "flowMeans"))) (build-system r-build-system) (propagated-inputs (list r-biobase r-feature r-flowcore r-rrcov)) (home-page "https://bioconductor.org/packages/flowMeans") (synopsis "Non-parametric flow cytometry data gating") (description "This package provides tools to identify cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection.") (license license:artistic2.0))) (define-public r-ncdfflow (package (name "r-ncdfflow") (version "2.42.0") (source (origin (method url-fetch) (uri (bioconductor-uri "ncdfFlow" version)) (sha256 (base32 "18ba8rygcd1ys150pk38r4w5lxwm6sl76zkd294yg847dygsqa4m")))) (properties `((upstream-name . "ncdfFlow"))) (build-system r-build-system) (propagated-inputs (list r-bh r-biobase r-biocgenerics r-flowcore r-rcpp r-rcpparmadillo r-rhdf5lib r-zlibbioc)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/ncdfFlow/") (synopsis "HDF5 based storage for flow cytometry data") (description "This package provides HDF5 storage based methods and functions for manipulation of flow cytometry data.") (license license:artistic2.0))) (define-public r-ggcyto (package (name "r-ggcyto") (version "1.24.0") (source (origin (method url-fetch) (uri (bioconductor-uri "ggcyto" version)) (sha256 (base32 "0sycyvdpa77mykzr709a7padh6478zmnzapibbq90qkc7bxnj359")))) (properties `((upstream-name . "ggcyto"))) (build-system r-build-system) (propagated-inputs (list r-data-table r-flowcore r-flowworkspace r-ggplot2 r-gridextra r-hexbin r-ncdfflow r-plyr r-rcolorbrewer r-rlang r-scales)) (native-inputs (list r-knitr)) (home-page "https://github.com/RGLab/ggcyto/issues") (synopsis "Visualize Cytometry data with ggplot") (description "With the dedicated fortify method implemented for @code{flowSet}, @code{ncdfFlowSet} and @code{GatingSet} classes, both raw and gated flow cytometry data can be plotted directly with ggplot. The @code{ggcyto} wrapper and some custom layers also make it easy to add gates and population statistics to the plot.") (license license:artistic2.0))) (define-public r-flowviz (package (name "r-flowviz") (version "1.60.0") (source (origin (method url-fetch) (uri (bioconductor-uri "flowViz" version)) (sha256 (base32 "175ygncrv6q6mb8pahixs89m9wm6hdpzx489gc9s8lgad2vrvz8f")))) (properties `((upstream-name . "flowViz"))) (build-system r-build-system) (propagated-inputs (list r-biobase r-flowcore r-hexbin r-idpmisc r-kernsmooth r-lattice r-latticeextra r-mass r-rcolorbrewer)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/flowViz/") (synopsis "Visualization for flow cytometry") (description "This package provides visualization tools for flow cytometry data.") (license license:artistic2.0))) (define-public r-flowclust (package (name "r-flowclust") (version "3.34.0") (source (origin (method url-fetch) (uri (bioconductor-uri "flowClust" version)) (sha256 (base32 "055vm9s8aha92znhpjqkipzprw8bkrinwjaik4ygmhym7w6vbblk")))) (properties `((upstream-name . "flowClust"))) (build-system r-build-system) (arguments `(#:configure-flags (list "--configure-args=--enable-bundled-gsl=no"))) (propagated-inputs (list r-biobase r-biocgenerics r-flowcore r-graph)) (inputs (list gsl)) (native-inputs (list pkg-config r-knitr)) (home-page "https://bioconductor.org/packages/flowClust") (synopsis "Clustering for flow cytometry") (description "This package provides robust model-based clustering using a t-mixture model with Box-Cox transformation.") (license license:artistic2.0))) ;; TODO: this package bundles an old version of protobuf. It's not easy to ;; make it use our protobuf package instead. (define-public r-rprotobuflib (package (name "r-rprotobuflib") (version "2.8.0") (source (origin (method url-fetch) (uri (bioconductor-uri "RProtoBufLib" version)) (sha256 (base32 "1mvqwrm1y0vij66gdwgpf5l1h660wsi9jzjfs4ihw3zm4cb0q5pp")))) (properties `((upstream-name . "RProtoBufLib"))) (build-system r-build-system) (arguments `(#:phases (modify-phases %standard-phases (add-after 'unpack 'unpack-bundled-sources (lambda _ (with-directory-excursion "src" (invoke "tar" "xf" "protobuf-3.8.0.tar.gz"))))))) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/RProtoBufLib/") (synopsis "C++ headers and static libraries of Protocol buffers") (description "This package provides the headers and static library of Protocol buffers for other R packages to compile and link against.") (license license:bsd-3))) (define-public r-flowworkspace (package (name "r-flowworkspace") (version "4.8.0") (source (origin (method url-fetch) (uri (bioconductor-uri "flowWorkspace" version)) (sha256 (base32 "0riyi9628cx1j5x6hmdd28yq75xh25j8ckcdz8dnb94dpvnhaqss")))) (properties `((upstream-name . "flowWorkspace"))) (build-system r-build-system) (propagated-inputs (list r-aws-s3 r-aws-signature r-bh r-biobase r-biocgenerics r-cytolib r-data-table r-delayedarray r-digest r-dplyr r-flowcore r-ggplot2 r-graph r-lattice r-latticeextra r-matrixstats r-ncdfflow r-rbgl r-rcpp r-rcpparmadillo r-rcppparallel r-rgraphviz r-rhdf5lib r-rprotobuflib r-s4vectors r-scales r-xml)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/flowWorkspace/") (synopsis "Infrastructure for working with cytometry data") (description "This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the @code{flowCore} functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis.") (license license:artistic2.0))) (define-public r-flowstats (package (name "r-flowstats") (version "4.8.0") (source (origin (method url-fetch) (uri (bioconductor-uri "flowStats" version)) (sha256 (base32 "1jbc92ah2mlpnd7v3k0207v4qz3rg9g9yy6r6y0s0cc5nifdyhwj")))) (properties `((upstream-name . "flowStats"))) (build-system r-build-system) (propagated-inputs (list r-biobase r-biocgenerics r-cluster r-corpcor r-fda r-flowcore r-flowviz r-flowworkspace r-kernsmooth r-ks r-lattice r-mass r-mnormt r-ncdfflow r-rcolorbrewer r-rrcov)) (home-page "http://www.github.com/RGLab/flowStats") (synopsis "Statistical methods for the analysis of flow cytometry data") (description "This package provides methods and functionality to analyze flow data that is beyond the basic infrastructure provided by the @code{flowCore} package.") (license license:artistic2.0))) (define-public r-opencyto (package (name "r-opencyto") (version "2.8.0") (source (origin (method url-fetch) (uri (bioconductor-uri "openCyto" version)) (sha256 (base32 "1nz5fra0jf70jwyfbcz5ksnz5xb62vfnfwfasr0zwwvjvmmvrs1y")))) (properties `((upstream-name . "openCyto"))) (build-system r-build-system) (propagated-inputs (list r-biobase r-biocgenerics r-clue r-data-table r-flowclust r-flowcore r-flowstats r-flowviz r-flowworkspace r-graph r-gtools r-ks r-lattice r-mass r-ncdfflow r-plyr r-r-utils r-rbgl r-rcolorbrewer r-rcpp r-rrcov)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/openCyto") (synopsis "Hierarchical gating pipeline for flow cytometry data") (description "This package is designed to facilitate the automated gating methods in a sequential way to mimic the manual gating strategy.") (license license:artistic2.0))) (define-public r-cytoml (package (name "r-cytoml") (version "2.8.0") (source (origin (method url-fetch) (uri (bioconductor-uri "CytoML" version)) (sha256 (base32 "0vp7advfh1d8596hjpzayasjhga4mx0l104sgz2asscbrjm4v7rr")))) (properties `((upstream-name . "CytoML"))) (build-system r-build-system) (inputs (list libxml2 zlib)) (propagated-inputs (list r-base64enc r-bh r-biobase r-corpcor r-cytolib r-data-table r-dplyr r-flowcore r-flowworkspace r-ggcyto r-graph r-jsonlite r-lattice r-opencyto r-plyr r-rbgl r-rcpp r-rcpparmadillo r-rcppparallel r-rgraphviz r-rhdf5lib r-rprotobuflib r-runit r-tibble r-xml r-xml2 r-yaml)) (native-inputs (list r-knitr)) (home-page "https://github.com/RGLab/CytoML") (synopsis "GatingML interface for cross platform cytometry data sharing") (description "This package provides an interface to implementations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms.") (license license:artistic2.0))) (define-public r-flowsom (package (name "r-flowsom") (version "2.4.0") (source (origin (method url-fetch) (uri (bioconductor-uri "FlowSOM" version)) (sha256 (base32 "0balsds5mm981cqamdjv3ndq1y9arharisd6f2lrpkzgvwawa645")))) (properties `((upstream-name . "FlowSOM"))) (build-system r-build-system) (propagated-inputs (list r-biocgenerics r-colorramps r-consensusclusterplus r-cytoml r-dplyr r-flowcore r-flowworkspace r-ggforce r-ggnewscale r-ggplot2 r-ggpointdensity r-ggpubr r-ggrepel r-igraph r-magrittr r-pheatmap r-rcolorbrewer r-rlang r-rtsne r-scattermore r-tidyr r-xml)) (home-page "https://bioconductor.org/packages/FlowSOM/") (synopsis "Visualize and interpret cytometry data") (description "FlowSOM offers visualization options for cytometry data, by using self-organizing map clustering and minimal spanning trees.") (license license:gpl2+))) (define-public r-mixomics (package (name "r-mixomics") (version "6.20.0") (source (origin (method url-fetch) (uri (bioconductor-uri "mixOmics" version)) (sha256 (base32 "0fwc2w7frj0bjijzfckkxf7ipx1z13gw7907q4zr5qfl9mh127w7")))) (properties `((upstream-name . "mixOmics"))) (build-system r-build-system) (propagated-inputs (list r-biocparallel r-corpcor r-dplyr r-ellipse r-ggrepel r-ggplot2 r-gridextra r-igraph r-lattice r-mass r-matrixstats r-rarpack r-rcolorbrewer r-reshape2 r-tidyr)) (native-inputs (list r-knitr)) (home-page "http://www.mixOmics.org") (synopsis "Multivariate methods for exploration of biological datasets") (description "mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data.") (license license:gpl2+))) (define-public r-depecher (package ;Source/Weave error (name "r-depecher") (version "1.12.0") (source (origin (method url-fetch) (uri (bioconductor-uri "DepecheR" version)) (sha256 (base32 "0rixczdds5gpac50wap6s68kmpdj4208l38gcihkrysz5frbvqjp")))) (properties `((upstream-name . "DepecheR"))) (build-system r-build-system) (propagated-inputs (list r-beanplot r-dosnow r-dplyr r-fnn r-foreach r-ggplot2 r-gmodels r-gplots r-mass r-matrixstats r-mixomics r-moments r-rcpp r-rcppeigen r-reshape2 r-robustbase r-viridis)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/DepecheR/") (synopsis "Identify traits of clusters in high-dimensional entities") (description "The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters.") (license license:expat))) (define-public r-rcistarget (package (name "r-rcistarget") (version "1.16.0") (source (origin (method url-fetch) (uri (bioconductor-uri "RcisTarget" version)) (sha256 (base32 "17fnjkg6rjqj33v7slg81skqag10y6dc14g5iv69gqshjal4w4im")))) (properties `((upstream-name . "RcisTarget"))) (build-system r-build-system) (propagated-inputs (list r-arrow r-aucell r-biocgenerics r-data-table r-dplyr r-genomeinfodb r-genomicranges r-gseabase r-r-utils r-s4vectors r-summarizedexperiment r-tibble)) (native-inputs (list r-knitr)) (home-page "https://aertslab.org/#scenic") (synopsis "Identify transcription factor binding motifs enriched on a gene list") (description "RcisTarget identifies @dfn{transcription factor binding motifs} (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the @dfn{transcription start site} (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high @dfn{Normalized Enrichment Score} (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge).") (license license:gpl3))) (define-public r-chicago (package (name "r-chicago") (version "1.24.0") (source (origin (method url-fetch) (uri (bioconductor-uri "Chicago" version)) (sha256 (base32 "13vzxmvxpc3r9gii37zvhhr5nbnaggrva97g6m2n02qn9daf6vmm")))) (properties `((upstream-name . "Chicago"))) (build-system r-build-system) (propagated-inputs (list r-data-table r-delaporte r-hmisc r-mass r-matrixstats)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/Chicago") (synopsis "Capture Hi-C analysis of genomic organization") (description "This package provides a pipeline for analysing Capture Hi-C data.") (license license:artistic2.0))) (define-public r-cicero (package (name "r-cicero") (version "1.14.0") (source (origin (method url-fetch) (uri (bioconductor-uri "cicero" version)) (sha256 (base32 "1ip12ijazlmcfbym078slxykpkz7d1zwvs8l8aqdnqpxjfk1ipx5")))) (build-system r-build-system) (propagated-inputs (list r-assertthat r-biobase r-biocgenerics r-data-table r-dplyr r-fnn r-genomicranges r-ggplot2 r-glasso r-gviz r-igraph r-iranges r-matrix r-monocle r-plyr r-reshape2 r-s4vectors r-stringi r-stringr r-tibble r-tidyr r-vgam)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/cicero/") (synopsis "Predict cis-co-accessibility from single-cell data") (description "Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends the monocle package for use in chromatin accessibility data.") (license license:expat))) ;; This is the latest commit on the "monocle3" branch. (define-public r-cicero-monocle3 (let ((commit "fa2fb6515857a8cfc88bc9af044f34de1bcd2b7b") (revision "1")) (package (inherit r-cicero) (name "r-cicero-monocle3") (version (git-version "1.3.2" revision commit)) (source (origin (method git-fetch) (uri (git-reference (url "https://github.com/cole-trapnell-lab/cicero-release") (commit commit))) (file-name (git-file-name name version)) (sha256 (base32 "077yza93wdhi08n40md20jwk55k9lw1f3y0063qkk90cpz60wi0c")))) (propagated-inputs (modify-inputs (package-propagated-inputs r-cicero) (delete "r-monocle") (prepend r-monocle3)))))) (define-public r-circrnaprofiler (package (name "r-circrnaprofiler") (version "1.10.0") (source (origin (method url-fetch) (uri (bioconductor-uri "circRNAprofiler" version)) (sha256 (base32 "1gwm416shhv2p3gh1n6kv1rvx0n0imy25b7z62z4s8b3gs3nfp5j")))) (properties `((upstream-name . "circRNAprofiler"))) (build-system r-build-system) (propagated-inputs (list r-annotationhub r-biostrings r-bsgenome r-bsgenome-hsapiens-ucsc-hg19 r-deseq2 r-dplyr r-edger r-genomeinfodb r-genomicranges r-ggplot2 r-gwascat r-iranges r-magrittr r-r-utils r-readr r-reshape2 r-rlang r-rtracklayer r-s4vectors r-seqinr r-stringi r-stringr r-universalmotif)) (native-inputs (list r-knitr)) (home-page "https://github.com/Aufiero/circRNAprofiler") (synopsis "Computational framework for the downstream analysis of circular RNA's") (description "@code{r-circrnaprofiler} is a computational framework for a comprehensive in silico analysis of @dfn{circular RNA} (circRNAs). This computational framework allows combining and analyzing circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis.") (license license:gpl3))) (define-public r-cistopic (package (name "r-cistopic") (version "2.1.0") (source (origin (method git-fetch) (uri (git-reference (url "https://github.com/aertslab/cisTopic") (commit (string-append "v" version)))) (file-name (git-file-name name version)) (sha256 (base32 "0c4553rnxq7b1w451kcc3iwvak4qa5h2b43xmfw6ii8096zd1gbf")))) (build-system r-build-system) (propagated-inputs (list r-aucell r-data-table r-dplyr r-dosnow r-dt r-feather r-fitdistrplus r-genomicranges r-ggplot2 r-lda r-matrix r-plyr r-rcistarget r-rtracklayer r-s4vectors)) (home-page "https://github.com/aertslab/cisTopic") (synopsis "Modelling of cis-regulatory topics from single cell epigenomics data") (description "The sparse nature of single cell epigenomics data can be overruled using probabilistic modelling methods such as @dfn{Latent Dirichlet Allocation} (LDA). This package allows the probabilistic modelling of cis-regulatory topics (cisTopics) from single cell epigenomics data, and includes functionalities to identify cell states based on the contribution of cisTopics and explore the nature and regulatory proteins driving them.") (license license:gpl3))) (define-public r-cistopic-next (let ((commit "04cecbb9d1112fcc1a6edc28b5a506bcb49f2803") (revision "1")) (package (inherit r-cistopic) (name "r-cistopic-next") ;; The DESCRIPTION file says this is version 0.3.0, which is a bit odd ;; since the previous release is 2.1.0. Oh well. (version (git-version "0.3.0" revision commit)) (source (origin (method git-fetch) (uri (git-reference (url "https://github.com/aertslab/cisTopic") (commit commit))) (file-name (git-file-name name version)) (sha256 (base32 "11cg9szlysnsjiaahda4k5v2vh4rxx27zhz53hafgaq9mdz0kgi2")))) (properties `((upstream-name . "cisTopic"))) (propagated-inputs (list r-aucell r-data-table r-dosnow r-dplyr r-dt r-feather r-fitdistrplus r-genomicranges r-ggplot2 r-lda r-matrix r-plyr r-rcistarget r-rtracklayer r-s4vectors r-text2vec)) (native-inputs (list r-knitr))))) (define-public r-genie3 (package (name "r-genie3") (version "1.18.0") (source (origin (method url-fetch) (uri (bioconductor-uri "GENIE3" version)) (sha256 (base32 "0h3vnpnznb9rda8gfwp4cnd2mqsvs1vzmfx90dchn5pqaphz1k2l")))) (properties `((upstream-name . "GENIE3"))) (build-system r-build-system) (propagated-inputs (list r-dplyr r-reshape2)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/GENIE3") (synopsis "Gene network inference with ensemble of trees") (description "This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data.") (license license:gpl2+))) (define-public r-roc (package (name "r-roc") (version "1.72.0") (source (origin (method url-fetch) (uri (bioconductor-uri "ROC" version)) (sha256 (base32 "0yfq0d0j2bzqdnjs6l2h6p48kmv9wfphlqym3brgndlnadipq1v2")))) (properties `((upstream-name . "ROC"))) (build-system r-build-system) (native-inputs (list r-knitr)) (home-page "https://www.bioconductor.org/packages/ROC/") (synopsis "Utilities for ROC curves") (description "This package provides utilities for @dfn{Receiver Operating Characteristic} (ROC) curves, with a focus on micro arrays.") (license license:artistic2.0))) (define-public r-watermelon (package (name "r-watermelon") (version "2.2.0") (source (origin (method url-fetch) (uri (bioconductor-uri "wateRmelon" version)) (sha256 (base32 "0adqyfabrvfcaj3mwp0rbqlcgpj92yb205cyhibbrs5gdr5ri4pv")))) (properties `((upstream-name . "wateRmelon"))) (build-system r-build-system) (propagated-inputs (list r-biobase r-illuminahumanmethylation450kanno-ilmn12-hg19 r-illuminaio r-limma r-lumi r-matrixstats r-methylumi r-roc)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/wateRmelon/") (synopsis "Illumina 450 methylation array normalization and metrics") (description "The standard index of DNA methylation (beta) is computed from methylated and unmethylated signal intensities. Betas calculated from raw signal intensities perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. This package provides 15 flavours of betas and three performance metrics, with methods for objects produced by the @code{methylumi} and @code{minfi} packages.") (license license:gpl3))) (define-public r-gdsfmt (package (name "r-gdsfmt") (version "1.32.0") (source (origin (method url-fetch) (uri (bioconductor-uri "gdsfmt" version)) (sha256 (base32 "1cdwyivgfc6yw5hj9b3j57wx55gckwhx6fwx2lvqynrjzjyzf3q0")) (modules '((guix build utils))) ;; Remove bundled sources of zlib, lz4, and xz. Don't attempt to build ;; them and link with system libraries instead. (snippet '(begin (for-each delete-file-recursively '("src/LZ4" "src/XZ" "src/ZLIB")) (substitute* "src/Makevars" (("all: \\$\\(SHLIB\\)") "all:") (("\\$\\(SHLIB\\): liblzma.a") "") (("^ (ZLIB|LZ4)/.*") "") (("CoreArray/dVLIntGDS.cpp.*") "CoreArray/dVLIntGDS.cpp") (("CoreArray/dVLIntGDS.o.*") "CoreArray/dVLIntGDS.o") (("PKG_LIBS = ./liblzma.a") "PKG_LIBS = -llz4")) (substitute* "src/CoreArray/dStream.h" (("include \"../(ZLIB|LZ4|XZ/api)/(.*)\"" _ _ header) (string-append "include <" header ">"))))))) (properties `((upstream-name . "gdsfmt"))) (build-system r-build-system) (inputs (list lz4 xz zlib)) (native-inputs (list r-knitr)) (home-page "http://corearray.sourceforge.net/") (synopsis "R Interface to CoreArray Genomic Data Structure (GDS) Files") (description "This package provides a high-level R interface to CoreArray @dfn{Genomic Data Structure} (GDS) data files, which are portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The @code{gdsfmt} package offers efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like @dfn{single-nucleotide polymorphism} (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package @code{parallel}.") (license license:lgpl3))) (define-public r-bigmelon (package (name "r-bigmelon") (version "1.22.0") (source (origin (method url-fetch) (uri (bioconductor-uri "bigmelon" version)) (sha256 (base32 "1msch4qbifkdqv0bbw03xj6d9w28z91mf4ki41rqg6048cq17h2k")))) (properties `((upstream-name . "bigmelon"))) (build-system r-build-system) (propagated-inputs (list r-biobase r-biocgenerics r-gdsfmt r-geoquery r-illuminaio r-methylumi r-minfi r-watermelon)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/bigmelon/") (synopsis "Illumina methylation array analysis for large experiments") (description "This package provides methods for working with Illumina arrays using the @code{gdsfmt} package.") (license license:gpl3))) (define-public r-seqbias (package (name "r-seqbias") (version "1.44.0") (source (origin (method url-fetch) (uri (bioconductor-uri "seqbias" version)) (sha256 (base32 "085nq6pf0bdn17wsbr5jnyy512v7rf67xff9rp5wz47mcifbv6rg")))) (properties `((upstream-name . "seqbias"))) (build-system r-build-system) (propagated-inputs (list r-biostrings r-genomicranges r-rhtslib)) (home-page "https://bioconductor.org/packages/seqbias/") (synopsis "Estimation of per-position bias in high-throughput sequencing data") (description "This package implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence.") (license license:lgpl3))) (define-public r-reqon (package (name "r-reqon") (version "1.42.0") (source (origin (method url-fetch) (uri (bioconductor-uri "ReQON" version)) (sha256 (base32 "1f5pplm8fy3wvl0b6n18gph4dq9i9x5qiyjrj0bk0kwlkbpba74r")))) (properties `((upstream-name . "ReQON"))) (build-system r-build-system) (propagated-inputs (list r-rjava r-rsamtools r-seqbias)) (home-page "https://bioconductor.org/packages/ReQON/") (synopsis "Recalibrating quality of nucleotides") (description "This package provides an implementation of an algorithm for recalibrating the base quality scores for aligned sequencing data in BAM format.") (license license:gpl2))) (define-public r-wavcluster (package (name "r-wavcluster") (version "2.30.0") (source (origin (method url-fetch) (uri (bioconductor-uri "wavClusteR" version)) (sha256 (base32 "04di095i9i19j9ppx8gdsk7n18vd02d4rjdi9d4a3p0xv05ihnb6")))) (properties `((upstream-name . 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In addition, the package integrates RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. While wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq).") (license license:gpl2))) (define-public r-timeseriesexperiment (package (name "r-timeseriesexperiment") (version "1.13.0") (source (origin (method url-fetch) (uri (bioconductor-uri "TimeSeriesExperiment" version)) (sha256 (base32 "0bdpxxr739qdg92qabfx122k9f43vw2hyxp4yxqlbp37vzgcdf2c")))) (properties `((upstream-name . 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(license license:artistic2.0))) (define-public r-wavetiling (package (name "r-wavetiling") (version "1.28.0") (source (origin (method url-fetch) (uri (bioconductor-uri "waveTiling" version)) (sha256 (base32 "0d7l559zlmly8mncmh1zhkqmsml0bwwfpm7ccp8l26y852vwf7hf")))) (properties `((upstream-name . "waveTiling"))) (build-system r-build-system) (propagated-inputs (list r-affy r-biobase r-biostrings r-genomegraphs r-genomicranges r-iranges r-oligo r-oligoclasses r-preprocesscore r-waveslim)) (home-page "https://r-forge.r-project.org/projects/wavetiling/") (synopsis "Wavelet-based models for tiling array transcriptome analysis") (description "This package is designed to conduct transcriptome analysis for tiling arrays based on fast wavelet-based functional models.") (license license:gpl2+))) (define-public r-variancepartition (package (name "r-variancepartition") (version "1.26.0") (source (origin (method url-fetch) (uri (bioconductor-uri "variancePartition" version)) (sha256 (base32 "0wk1xql8b0gxyrqz9hs54xvmp7qdw9b8jnv88p4vgv061iwyk7wv")))) (properties `((upstream-name . "variancePartition"))) (build-system r-build-system) (propagated-inputs (list r-aod r-biobase r-biocparallel r-doparallel r-foreach r-ggplot2 r-gplots r-iterators r-limma r-lme4 r-lmertest r-mass r-matrix r-pbkrtest r-progress r-rdpack r-reshape2 r-rhpcblasctl r-rlang r-scales)) (native-inputs (list r-knitr)) (home-page "https://bioconductor.org/packages/variancePartition/") (synopsis "Analyze variation in gene expression experiments") (description "This is a package providing tools to quantify and interpret multiple sources of biological and technical variation in gene expression experiments. It uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. The package includes dream differential expression analysis for repeated measures.") (license license:gpl2+))) (define-public r-htqpcr (package (name "r-htqpcr") (version "1.50.0") (source (origin (method url-fetch) (uri (bioconductor-uri "HTqPCR" version)) (sha256 (base32 "0am98rzwpi3kybq1l27c5qn3n1pg5aqwmh6jq9q0lzbjjin3haqc")))) (properties `((upstream-name . "HTqPCR"))) (build-system r-build-system) (propagated-inputs (list r-affy r-biobase r-gplots r-limma r-rcolorbrewer)) (home-page (string-append "https://www.ebi.ac.uk/sites/ebi.ac.uk/files/" "groups/bertone/software/HTqPCR.pdf")) (synopsis "Automated analysis of high-throughput qPCR data") (description "Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs).") (license license:artistic2.0))) (define-public r-unifiedwmwqpcr (package (name "r-unifiedwmwqpcr") (version "1.32.0") (source (origin (method url-fetch) (uri (bioconductor-uri "unifiedWMWqPCR" version)) (sha256 (base32 "1skfs94a6bv05c844zf5vfqw1fbgxyppgdnckdbhxg2a2470a4wh")))) (properties `((upstream-name . "unifiedWMWqPCR"))) (build-system r-build-system) (propagated-inputs (list r-biocgenerics r-htqpcr)) (home-page "https://bioconductor.org/packages/unifiedWMWqPCR") (synopsis "Unified Wilcoxon-Mann Whitney Test for differential expression in qPCR data") (description "This package implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data.") (license license:gpl2+))) (define-public r-universalmotif (package (name "r-universalmotif") (version "1.14.1") (source (origin (method url-fetch) (uri (bioconductor-uri "universalmotif" version)) (sha256 (base32 "1sm54z8aq3534qjsa19wychhwcvwnjlkydmiqqvidiiwcxwqpjsr")))) (properties `((upstream-name . 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A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others.") (license license:gpl3))) (define-public r-ace (package (name "r-ace") (version "1.14.0") (source (origin (method url-fetch) (uri (bioconductor-uri "ACE" version)) (sha256 (base32 "1xnw288vz810vjkidar5h218wyc0q2hx0k4zi3r88vaz5rfhc05m")))) (properties `((upstream-name . "ACE"))) (build-system r-build-system) (propagated-inputs (list r-biobase r-genomicranges r-ggplot2 r-qdnaseq)) (native-inputs (list r-knitr)) (home-page "https://github.com/tgac-vumc/ACE") (synopsis "Absolute copy number estimation from low-coverage whole genome sequencing") (description "This package uses segmented copy number data to estimate tumor cell percentage and produce copy number plots displaying absolute copy numbers. For this it uses segmented data from the @code{QDNAseq} package, which in turn uses a number of dependencies to turn mapped reads into segmented data. @code{ACE} will run @code{QDNAseq} or use its output rds-file of segmented data. It will subsequently run through all samples in the object(s), for which it will create individual subdirectories. For each sample, it will calculate how well the segments fit (the relative error) to integer copy numbers for each percentage of @dfn{tumor cells} (cells with divergent segments).") (license license:gpl2))) (define-public r-acgh (package (name "r-acgh") (version "1.74.0") (source (origin (method url-fetch) (uri (bioconductor-uri "aCGH" version)) (sha256 (base32 "00ni0kwy68v33ggfi8g5vffirhmbhaxg4l54hcqhx75m535z1x7d")))) (properties `((upstream-name . 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(license license:gpl2+))) (define-public r-acde (package (name "r-acde") (version "1.26.0") (source (origin (method url-fetch) (uri (bioconductor-uri "acde" version)) (sha256 (base32 "0lgq546y4qklfzbc6fjr3d656hn76p6dn4694qfiafql2nlsjbj2")))) (properties `((upstream-name . "acde"))) (build-system r-build-system) (propagated-inputs (list r-boot)) (home-page "https://bioconductor.org/packages/acde") (synopsis "Identification of differentially expressed genes with artificial components") (description "This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the @dfn{false discovery rate} (FDR).") 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