Gplots Bioconductor
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*Plots Bioconductor
*Gplots Bioconductor DOI: 10.18129/B9.bioc.Linnorm
This package is for version 3.8 of Bioconductor;for the stable, up-to-date release version, seeLinnorm.Linear model and normality based transformation method (Linnorm)
Bioconductor version: 3.8
Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data. It transforms such datasets for parametric tests. In addition to the transformtion function (Linnorm), the following pipelines are implemented: 1. Library size/Batch effect normalization (Linnorm.Norm), 2. Cell subpopluation analysis and visualization using t-SNE or PCA K-means clustering or Hierarchical clustering (Linnorm.tSNE, Linnorm.PCA, Linnorm.HClust), 3. Differential expression analysis or differential peak detection using limma (Linnorm.limma), 4. Highly variable gene discovery and visualization (Linnorm.HVar), 5. Gene correlation network analysis and visualization (Linnorm.Cor), 6. Stable gene selection for scRNA-seq data; for users without or do not want to rely on spike-in genes (Linnorm.SGenes). 7. Data imputation. (under development) (Linnorm.DataImput). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, the RnaXSim function is included for simulating RNA-seq data for the evaluation of DEG analysis methods.
Gplots: Various R Programming Tools for Plotting Data. Various R programming tools for plotting data, including: - calculating and plotting locally smoothed summary. Bioconductor version: Release (3.12) This workflow package provides, through its vignette, a complete case study analysis of an RNA-Seq experiment using the Rsubread and edgeR packages. The workflow starts from read alignment and continues on to data exploration, to differential expression and, finally, to pathway analysis.
Author: Shun Hang Yip <shunyip at bu.edu>, Panwen Wang <pwwang at pwwang.com>, Jean-Pierre Kocher <Kocher.JeanPierre at mayo.edu>, Pak Chung Sham <pcsham at hku.hk>, Junwen Wang <junwen at uw.edu>
Maintainer: Ken Shun Hang Yip <shunyip at bu.edu> Plots Bioconductor
Citation (from within R, enter citation(’Linnorm’)): 888 casino live help.Gplots BioconductorInstallation
To install this package, start R (version ’3.5’) and enter: Slots empire review.
For older versions of R, please refer to the appropriate Bioconductor release. Documentation
To view documentation for the version of this package installed in your system, start R and enter:PDFR ScriptLinnorm User ManualPDFReference ManualTextNEWSTextLICENSEDetailsbiocViewsBatchEffect, ChIPSeq, Clustering, DifferentialExpression, GeneExpression, Genetics, ImmunoOncology, Network, Normalization, PeakDetection, RNASeq, Sequencing, SingleCell, Software, TranscriptionVersion2.6.1In Bioconductor sinceBioC 3.3 (R-3.3) (3 years)LicenseMIT + file LICENSEDependsR (>= 3.4)ImportsRcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap, Rtsne, gmodelsLinkingToRcpp, RcppArmadilloSuggestsBiocStyle, knitr, rmarkdown, gplots, RColorBrewer, moments, testthatSystemRequirementsEnhancesURLhttp://www.jjwanglab.org/Linnorm/Depends On MeImports MeSuggests MeLinks To MeBuild ReportPackage Archives
Follow Installation instructions to use this package in your R session.Source Package Linnorm_2.6.1.tar.gzWindows Binary Linnorm_2.6.1.zip (32- & 64-bit) Mac OS X 10.11 (El Capitan) Linnorm_2.6.1.tgzSource Repositorygit clone https://git.bioconductor.org/packages/LinnormSource Repository (Developer Access)git clone git@git.bioconductor.org:packages/LinnormPackage Short Urlhttp://bioconductor.org/packages/Linnorm/Package Downloads ReportDownload Stats
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*Plots Bioconductor
*Gplots Bioconductor DOI: 10.18129/B9.bioc.Linnorm
This package is for version 3.8 of Bioconductor;for the stable, up-to-date release version, seeLinnorm.Linear model and normality based transformation method (Linnorm)
Bioconductor version: 3.8
Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data. It transforms such datasets for parametric tests. In addition to the transformtion function (Linnorm), the following pipelines are implemented: 1. Library size/Batch effect normalization (Linnorm.Norm), 2. Cell subpopluation analysis and visualization using t-SNE or PCA K-means clustering or Hierarchical clustering (Linnorm.tSNE, Linnorm.PCA, Linnorm.HClust), 3. Differential expression analysis or differential peak detection using limma (Linnorm.limma), 4. Highly variable gene discovery and visualization (Linnorm.HVar), 5. Gene correlation network analysis and visualization (Linnorm.Cor), 6. Stable gene selection for scRNA-seq data; for users without or do not want to rely on spike-in genes (Linnorm.SGenes). 7. Data imputation. (under development) (Linnorm.DataImput). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, the RnaXSim function is included for simulating RNA-seq data for the evaluation of DEG analysis methods.
Gplots: Various R Programming Tools for Plotting Data. Various R programming tools for plotting data, including: - calculating and plotting locally smoothed summary. Bioconductor version: Release (3.12) This workflow package provides, through its vignette, a complete case study analysis of an RNA-Seq experiment using the Rsubread and edgeR packages. The workflow starts from read alignment and continues on to data exploration, to differential expression and, finally, to pathway analysis.
Author: Shun Hang Yip <shunyip at bu.edu>, Panwen Wang <pwwang at pwwang.com>, Jean-Pierre Kocher <Kocher.JeanPierre at mayo.edu>, Pak Chung Sham <pcsham at hku.hk>, Junwen Wang <junwen at uw.edu>
Maintainer: Ken Shun Hang Yip <shunyip at bu.edu> Plots Bioconductor
Citation (from within R, enter citation(’Linnorm’)): 888 casino live help.Gplots BioconductorInstallation
To install this package, start R (version ’3.5’) and enter: Slots empire review.
For older versions of R, please refer to the appropriate Bioconductor release. Documentation
To view documentation for the version of this package installed in your system, start R and enter:PDFR ScriptLinnorm User ManualPDFReference ManualTextNEWSTextLICENSEDetailsbiocViewsBatchEffect, ChIPSeq, Clustering, DifferentialExpression, GeneExpression, Genetics, ImmunoOncology, Network, Normalization, PeakDetection, RNASeq, Sequencing, SingleCell, Software, TranscriptionVersion2.6.1In Bioconductor sinceBioC 3.3 (R-3.3) (3 years)LicenseMIT + file LICENSEDependsR (>= 3.4)ImportsRcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap, Rtsne, gmodelsLinkingToRcpp, RcppArmadilloSuggestsBiocStyle, knitr, rmarkdown, gplots, RColorBrewer, moments, testthatSystemRequirementsEnhancesURLhttp://www.jjwanglab.org/Linnorm/Depends On MeImports MeSuggests MeLinks To MeBuild ReportPackage Archives
Follow Installation instructions to use this package in your R session.Source Package Linnorm_2.6.1.tar.gzWindows Binary Linnorm_2.6.1.zip (32- & 64-bit) Mac OS X 10.11 (El Capitan) Linnorm_2.6.1.tgzSource Repositorygit clone https://git.bioconductor.org/packages/LinnormSource Repository (Developer Access)git clone git@git.bioconductor.org:packages/LinnormPackage Short Urlhttp://bioconductor.org/packages/Linnorm/Package Downloads ReportDownload Stats
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