DOI: 10.18129/B9.bioc.edgeR
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Empirical Analysis of Digital Gene Expression Data in R
Microsoft R Open is the enhanced distribution of R from Microsoft Corporation. It is a complete open source platform for statistical analysis and data science. The current version, Microsoft R Open 3.5.3, is based on (and 100% compatible with) R-3.5.3, the most widely used statistics software in the world, and is therefore fully compatibility with all packages, scripts and applications that.
Clicking on the Software' link. There, you can download: 1.A tutorial that walks you through all types of analyses with related; 2.The R manual for related, which is in the classic' format of R documentation. If you are on a Mac, you can move the package le to R’s working directory (where R will look for les). The statistical approach for R Software Download Step 1: To download R Software, first Go to the official R Programming website by clicking this link Download. The screenshot below shows the Home page of the official website. Next, Click on the download R or CRAN mirror to start the r download process. Apr 30, 2020. Table of Contents Find Missing Values Column List Programmatically How to find outliers using R Programming Lubridate Package in R Programming How to convert String to Date in R Programming using as.Date function Install CatBoost R Package on Mac, Linux and Windows Create Regression Model Using CatBoost Package in R Programming.
Bioconductor version: Release (3.11)
Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce read counts, including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE.
Author: Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth
Maintainer: Yunshun Chen <yuchen at wehi.edu.au>, Gordon Smyth <smyth at wehi.edu.au>, Aaron Lun <infinite.monkeys.with.keyboards at gmail.com>, Mark Robinson <mark.robinson at imls.uzh.ch>
Citation (from within R, enter
citation('edgeR')
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Installation
To install this package, start R (version '4.0') and enter:
For older versions of R, please refer to the appropriate Bioconductor release. Free vpn for mac download.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
edgeR Vignette | |
edgeRUsersGuide.pdf | |
Reference Manual | |
Text | NEWS |
Details
biocViews | AlternativeSplicing, BatchEffect, Bayesian, BiomedicalInformatics, CellBiology, ChIPSeq, Clustering, Coverage, DNAMethylation, DifferentialExpression, DifferentialMethylation, DifferentialSplicing, Epigenetics, FunctionalGenomics, GeneExpression, GeneSetEnrichment, Genetics, ImmunoOncology, MultipleComparison, Normalization, Pathways, QualityControl, RNASeq, Regression, SAGE, Sequencing, Software, SystemsBiology, TimeCourse, Transcription, Transcriptomics |
Version | 3.30.3 |
In Bioconductor since | BioC 2.3 (R-2.8) (12 years) |
License | GPL (>=2) |
Depends | R (>= 3.6.0), limma(>= 3.41.5) |
Imports | methods, graphics, stats, utils, locfit, Rcpp |
LinkingTo | Rcpp |
Suggests | jsonlite, readr, rhdf5, splines, Biobase, AnnotationDbi, SummarizedExperiment, org.Hs.eg.db |
SystemRequirements | C++11 |
Enhances | |
URL | |
Depends On Me | ASpli, DBChIP, EDDA, EGSEA123, IntEREst, manta, methylMnM, ReactomeGSA.data, RNAseq123, rnaseqDTU, RnaSeqGeneEdgeRQL, RNASeqR, RnaSeqSampleSizeData, RUVSeq, samExploreR, TCC, tRanslatome |
Imports Me | affycoretools, anota2seq, ArrayExpressHTS, ATACseqQC, AWFisher, baySeq, BioQC, circRNAprofiler, clusterExperiment, CNVRanger, compcodeR, consensusDE, coseq, countsimQC, csaw, DaMiRseq, debrowser, DEComplexDisease, DEFormats, DEGreport, DEsubs, DiffBind, diffcyt, diffHic, diffloop, dittoSeq, DMRcate, doseR, DRIMSeq, DropletUtils, easyRNASeq, EBSEA, EDDA, eegc, EGSEA, eisaR, EnrichmentBrowser, erccdashboard, ERSSA, GDCRNATools, Glimma, GSEABenchmarkeR, HTSFilter, icetea, infercnv, IsoformSwitchAnalyzeR, KnowSeq, Maaslin2, MEDIPS, metaseqR, metaseqR2, MIGSA, MLSeq, msgbsR, msmsTests, multiHiCcompare, muscat, NBSplice, PathoStat, PROPER, psichomics, RCM, recountWorkflow, regsplice, Repitools, ReportingTools, rnaSeqMap, ROSeq, scde, scone, scran, SEtools, SIMD, SingleCellSignalR, singscore, SingscoreAMLMutations, splatter, STATegRa, sva, SVAPLSseq, systemPipeR, TBSignatureProfiler, TCseq, TimeSeriesExperiment, tradeSeq, tweeDEseq, vidger, yarn, zinbwave |
Suggests Me | ABSSeq, bigPint, biobroom, BitSeq, CAGEWorkflow, chipseqDB, ClassifyR, clonotypeR, cqn, csawUsersGuide, cydar, dcanr, dearseq, DEScan2, easyreporting, EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges, glmGamPoi, goseq, groHMM, GSAR, GSVA, ideal, iSEEu, JctSeqData, leeBamViews, missMethyl, multiMiR, regionReport, ribosomeProfilingQC, SSPA, stageR, subSeq, SummarizedBenchmark, TCGAbiolinks, tidybulk, ToPASeq, topconfects, tximeta, tximport, variancePartition, weitrix, Wrench, zFPKM |
Links To Me | |
Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
Source Package | edgeR_3.30.3.tar.gz |
Windows Binary | edgeR_3.30.3.zip (32- & 64-bit) |
macOS 10.13 (High Sierra) | edgeR_3.30.3.tgz |
Source Repository | git clone https://git.bioconductor.org/packages/edgeR |
Source Repository (Developer Access) | git clone [email protected]:packages/edgeR |
Package Short Url | https://bioconductor.org/packages/edgeR/ |
Package Downloads Report | Download Stats |
Old Source Packages for BioC 3.11 | Source Archive |
- 3 Install R in mac OS
- 5 R basic commands
Installing R. The R project
The first step for becoming a Data Scientist or Data Analyst is having R installed on your computer. R can be freely downloaded in its official page. Note that installation requires up to 150 Mb of disk space available. The official web page of the R project is the following:
www.r-project.org/In the previous link you can find interesting information about the R project, the R Foundation, the releases, documentation and other interesting links.
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Install R on Windows
In order to install R on Windows, you can go through the following link and click on “Download R x.x.x for Windows” to start downloading the latest R version available.
https://cran.r-project.org/bin/windows/base/Once downloaded, open it, select the preferred language and click Next to all dialogs.
Remember to select the path where you want to install R:
Note that you can uncheck the bit files that not correspond to your PC if desired.
You can also specify to customize some startup options:
When the installation finishes, you will be able to start using R base on Windows.
Install R in mac OS
The process of installing R in a mac is analogous to installing it in Windows. You can download the binary package (for Mac OS X 10.6 and above) from the following URL and then install it.
https://cran.r-project.org/bin/macosx/If for some reason you are using Mac OS 8.6 to 9.2 (and Mac OS X 10.1) versions here you can find the old binary packages. For older Mac OS X systems and PowerPC Macs, you must use this repository to download R.
Install R with Homebrew
An alternative to install R in macOS is to use the Homebrew package manager. For that purpose you just need to type the following in the corresponding brew terminal:
Install RStudio
RStudio is the most popular IDE for R, providing a clean and useful graphic interface for developing in R language. The main advantages of this IDE are the pane layout for getting help and displaying plots (even interactive), creating RMarkdown and Sweave documents, among others. You can download RStudio from the following link:
https://rstudio.com/products/rstudio/download/#downloadOnce downloaded, open the setup wizard and follow the simple steps.
R basic commands
After downloading R and RStudio open one and you will see the corresponding IDE (Integrated Development Environment). First, try to write direct commands in the commands window, where you can see the symbol
>
for start writing. Write the following and press Enter in the command line. Those were your first R commands. Amazing!
You can also write in the script window and execute the code when you want. If this window doesn’t appear at first glance go to
File - New File - R Script
or press Ctr + Shift + n
or Cmd + Shift + n
in Mac if you are using RStudio.Once opened, copy and paste the code to the script, select it and press
Ctr + r
or Cmd + r
in R base or Ctr + Enter or Cmd + Enter
in RStudio (you can customize this). In the following block there are several examples you can try.It should be noted that you can use
#
for comments that won’t be executed.You can also write several instructions within the same line, using a semicolon (
;
) as separator, although this practice is not recommended.If you want to store the values in variables you can use the
=
or <-
sign. In R, it is more recommended using the arrow <-
. If you want to print the stored variables in the console call the name or call the assignation between brackets.R Cran Download For Windows
A more detailed example
Let’s see a more advanced example. We are going to simulate a normal random variable of size 200 with mean 105 and standard deviation of 2. Then, we make a summary and its corresponding histogram and boxplot.
Check where is R installed
Sometimes it is useful to know the R installation path. The
R.home
function will output the path where you have R installed in your computer. Open R and type the following in the console if you want to check which is the installation path.Check R version
You can also check you have downloaded and installed the correct version typing:
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In this case the output shows that we have installed the R version 4.0.0. You can have additional information about the version just typing
R.Version()
.How to change the language in R GUI?
By default, R will show the graphic user interface in the language of your operating system. To override this behavior and change the language permanently you can follow this steps:
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- Go to the R installation path. Recall to use the
R.home()
function if you don’t remember where it is. - Open the installation folder.
- Open the
etc
folder. - Open the
RConsole
archive with a notepad. - Change the line
language =
forlanguage = en
if you want to set the language to English, or specify the language code you prefer.