Using Bioconductor

I also tried the above methods,but it can’t solve my problem. I aloso tried other packages, i have the same problem i use R version 3.5.2 and when i try other version like version 2.10.1 i haven’t find the “gplots” package so i tried other packages like geneR and this time it’s work but i don’t find gplots package in the version 2. Apr 22, 2016  I figured it out. R studio was trying to place files into the library, but were being put in another location. Moved files around, and now all is good.

The current release of Bioconductor is version3.10; it works with R version3.6.0. Users of older R andBioconductor must update their installation to take advantageof new features and to access packages that have been added toBioconductor since the last release.

The development version of Bioconductor is version3.11; it works with R version4.0.0. More recent ‘devel’versions of R (if available) will be supported during the nextBioconductor release cycle.

Install the latest release of R, then get the latest version ofBioconductor by starting R and entering the commands

It may be possible to change the Bioconductor version of an existinginstallation; see the ‘Changing version’ section of the BiocManagervignette.

Details, including instructions toinstall additional packages and toupdate,find, andtroubleshoot are providedbelow. A devel version ofBioconductor is available. There are goodreasons for using BiocManager::install() formanaging Bioconductor resources.

Install R

1. Download the most recent version of R. The R FAQs and the RInstallation and Administration Manual contain detailed instructionsfor installing R on various platforms (Linux, OS X, and Windows beingthe main ones).

1. Start the R program; on Windows and OS X, this will usually meandouble-clicking on the R application, on UNIX-like systems, type“R” at a shell prompt.

2. As a first step with R, start the R help browser by typinghelp.start() in the R command window. For help on anyfunction, e.g. the “mean” function, type ? mean.

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Install Bioconductor Packages

To install core packages, type the following in an R command window:

Install specific packages, e.g., “GenomicFeatures” and “AnnotationDbi”, with

The install() function (in the BiocManager package) has arguments that changeits default behavior; type ?install for further help.

For a more detailed explanation on using BiocManager and its advanced usage,such as version switching, please refer to theBiocManager vignette.

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Find Bioconductor Packages

Visit the software package listto discover available packages.

To search through available packages programmatically, use the following:

For example, using a “^org” search pattern will show all of the availableorganism annotation packages.

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Update Installed Bioconductor Packages

Bioconductor packages, especially those in the development branch, areupdated fairly regularly. To identify packages requiring update withinyour version of Bioconductor, start a new session of R and enter

Use the argument ask=FALSE to update old packages without beingprompted. Read the help page for ?install for additional details.

Upgrading installed Bioconductor packages

Some versions of R support more than one version of Bioconductor. Touse the latest version of Bioconductor for your version of R, enter

Remember that more recent versions of Bioconductor may be available if yourversion of R is out-of-date.

For more details on Bioconductor approaches to versioning, seethe advanced sectionin the vignette and version numbering in the developer reference section.

Recompiling installed Bioconductor packages

Rarely, underlying changes in the operating system require ALLinstalled packages to be recompiled for source (C or Fortran)compatibility. One way to address this might be to start a new Rsession and enter

As this will reinstall all currently installed packages, it likelyinvolves a significant amount of network bandwidth and compilationtime. All packages are implicitly updated, and the cumulative effectmight introduce wrinkles that disrupt your work flow. It also requiresthat you have the necessary compilers installed.

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Troubleshoot Package Installations

Use the commands

to flag packages that are either out-of-date or too new for yourversion of Bioconductor. The output suggests ways to solve identifiedproblems, and the help page ?valid lists arguments influencingthe behavior of the function.

Troubleshoot BiocManager

One likely reason for BiocManager not working on your system couldbe that your version of R is too old for BiocManager. In orderavoid this issue, please ensure that you have the latest version of Rinstalled in your system. BiocManager supports R versions from 3.5.0and above.

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Why use BiocManager::install()?

BiocManager::install() is the recommended way to install Bioconductorpackages. There are several reasons for preferring this to the‘standard’ way in which R pacakges are installed viainstall.packages().

Bioconductor has a repository and release schedule that differs from R(Bioconductor has a ‘devel’ branch to which new packages and updatesare introduced, and a stable ‘release’ branch emitted once every 6months to which bug fixes but not new features are introduced).

A consequence of the mismatch between R and Bioconductor releaseschedules is that the Bioconductor version identified byinstall.packages() is sometimes not the most recent ‘release’available. For instance, an R minor version may be introduced somemonths before the next Bioc release. After the Bioc release the usersof the R minor version will be pointed to an out-of-date version ofBioconductor.

A consequence of the distinct ‘devel’ branch is thatinstall.packages() sometimes points only to the ‘release’repository, whereas Bioconductor developers and users wantingleading-edge features wish to access the Bioconductor ‘devel’repository. For instance, the Bioconductor 3.0 release is availablefor R.3.1.x, so Bioconductor developers and leading-edge users need tobe able to install the devel version of Bioconductor packages into thesame version (though perhaps different instance or at least librarylocation) of R that supports version 2.14 of Bioconductor.

An indirect consequence of Bioconductor’s structured release is thatpackages generally have more extensive dependencies with one another,both explicitly via the usual package mechanisms and implicitlybecause the repository, release structure, and Bioconductor communityinteractions favor re-use of data representations and analysisconcepts across packages. There is thus a higher premium on knowingthat packages are from the same release, and that all packages arecurrent within the release.

The BiocManager package serves as the primary way to ensure thatthe appropriate Bioconductor installation is used with respectto the version of R in use regardless of the R and Bioconductorrelease cycles.

The install() function is provided by BiocManager. This is awrapper around install.packages, but with the repository chosenaccording to the version of Bioconductor in use, rather than to theversion relevant at the time of the release of R.

install() also nudges users to remain current within a release, bydefault checking for out-of-date packages and asking if the user wouldlike to update

The BiocManager package provides facilities for switching to the‘devel’ version of Bioconductor

(at some points in the R / Bioconductor release cycle use of ‘devel’requires use of a different version of R itself, in which case theattempt to install devel fails with an appropriate message).

The BiocManager package also provides valid() to test that theinstalled packages are not a hodgepodge from different Bioconductorreleases (the ‘too new’ packages have been installed from sourcerather than a repository; regular users would seldom have these).

For users who spend a lot of time in Bioconductor, the featuresoutlined above become increasingly important and install() is muchpreferred to install.packages().

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Pre-configured Bioconductor

Bioconductor is also available as a set ofAmazon Machine Images (AMIs) andDocker images.

Legacy and Older R Versions

It is always recommended to update to the most current version of R andBioconductor. If this is not possible and R < 3.5.0 , please use the followingfor installing Bioconductor packages

To install core packages, type the following in an R command window:

Install specific packages, e.g., “GenomicFeatures” and “AnnotationDbi”, with

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These are specific notes for installing PEcAn on Ubuntu (14.04) and will be referenced from the main installing PEcAn page. You will at least need to install the build environment and Postgres sections. If you want to access the database/PEcAn using a web browser you will need to install Apache. To access the database using the BETY interface, you will need to have Ruby installed.

This document also contains information on how to install the Rstudio server edition as well as any other packages that can be helpful.

41.1 Install build environment

41.2 Install Postgres

Documentation: http://trac.osgeo.org/postgis/wiki/UsersWikiPostGIS21UbuntuPGSQL93Apt

To install the BETYdb database . ## Apache Configuration PEcAn

41.3 Apache Configuration BETY

41.4 Rstudio-server

NOTE This will allow anybody to login to the machine through the rstudio interface and run any arbitrary code. The login used however is the same as the system login/password.

Based on version of ubuntu 32/64 use either of the following

How to install ninja 1.7.2 for mac. 32bit only

64bit only

41.5 Additional packages

HDF5 Tools, netcdf, GDB and emacs

41.6 Download and Install Models

This page contains instructions on how to download and install ecosystem models that have been or are being coupled to PEcAn. These instructions have been tested on the PEcAn unbuntu VM. Commands may vary on other operating systems. Also, some model downloads require permissions before downloading, making them unavailable to the general public. Please contact the PEcAn team if you would like access to a model that is not already installed on the default PEcAn VM.

41.7 Install instructions

41.7.1 ED2

41.7.1.1 ED2.2 r46 (used in PEcAn manuscript)

Perform a test run using pre configured ED settings for ED2.2 r46

41.7.1.2 ED 2.2 r82

Perform a test run using pre configured ED settings for ED2.2 r82

41.7.2 SIPNET

41.7.3 BioCro

41.7.3.1 Installation

BioCro Developers: request from [@dlebauer on GitHub](https://github.com/dlebauer)

41.7.4 Linkages

41.7.5 DALEC

41.7.6 LINKAGES

41.7.7 CLM 4.5

The version of CLM installed on PEcAn is the ORNL branch provided by Dan Ricciuto. This version includes Dan’s point-level CLM processing scripts

Download the code (~300M compressed), input data (1.7GB compressed and expands to 14 GB), and a few misc inputs.

Required libraries

Compile and build default inputs

41.7.7.1 CLM Test Run

You will see a new directory in scripts: US-UMB_I1850CLM45CN Enter this directory and run (you shouldn’t have to do this normally, but there is a bug with the python script and doing this ensures all files get to the right place):

Next you are ready to go to the run directory:

Open to edit file: datm.streams.txt.CLM1PT.CLM_USRDAT and check file paths such that all paths start with /home/carya/models/ccsm_inputdata

From this directory, launch the executable that resides in the bld directory:

not sure this was the right location, but wherever the executable is

You should begin to see output files that look like this: US-UMB_I1850CLM45CN.clm2.h0.yyyy-mm.nc (yyyy is year, mm is month) These are netcdf files containing monthly averages of lots of variables.

The lnd_in file in the run directory can be modified to change the output file frequency and variables.

41.7.8 JULES

INSTALL STEPS: 1) Download JULES and FCM JULES: Model requires registration to download. Not to be put on PEcAn VM Registration: https://jules.jchmr.org/software-and-documentation Documentation: http://jules-lsm.github.io/vn4.2/index.html

FCM:

2. edit makefile ```bash open etc/fcm-make/make.cfg

set JULES_NETCDF = actual instead of dummy set path (e.g. /usr/) and lib_path /lib64 to netCDF libraries ```

3. compile JULES

Executable is under build/bin/jules.exe

Example rundir: examples/point_loobos

41.7.9 MAESPA

Navigate to a directory you would like store MAESPA and run the following:

maespa.out is your executable. Example input files can be found in the inpufiles directory. Executing measpa.out from within one of the example directories will produce output.

MAESPA developers have also developed a wrapper package called Maeswrap. The usual R package installation method install.packages may present issues with downloading an unpacking a dependency package called rgl. Here are a couple of solutions:

41.7.9.1 Solution 1

then from within R

41.7.9.2 Solution 2

then from within R

41.7.10 GDAY

Navigate to a directory you would like to store GDAY and run the following:

gday is your executable.

41.7.11 LPJ-GUESS

Instructions to download source code