BOL: Related items

A guide for complete R beginners :- Installing R packages

Archana Malhotra — Tue, 24 Feb 2015 20:23:34 -0600

Part of the reason R has become so popular is the vast array of packages available at the cran and bioconductor repositories. In the last few years, the number of packages has grown exponentially!

This is a short post giving steps on how to actually install R packages. Let’s suppose you want to install the ggplot2 package. Well nothing could be easier. We just fire up an R shell and type:
> install.packages("ggplot2")

In theory the package should just install, however:

if you are using Linux and don’t have root access, this command won’t work.
you will be asked to select your local mirror, i.e. which server should you use to download the package.

Installing packages without root access

First, you need to designate a directory where you will store the downloaded packages. On my machine, I use the directory /data/Rpackages/ After creating a package directory, to install a package we use the command:
> install.packages("ggplot2", lib="/data/Rpackages/") > library(ggplot2, lib.loc="/data/Rpackages/")

It’s a bit of a pain having to type /data/Rpackages/ all the time. To avoid this burden, we create a file .Renviron in our home area, and add the line R_LIBS=/data/Rpackages/ to it. This means that whenever you start R, the directory /data/Rpackages/ is added to the list of places to look for R packages and so:

> install.packages("ggplot2") > library(ggplot2)

just works!

Setting the repository

Every time you install a R package, you are asked which repository R should use. To set the repository and avoid having to specify this at every package install, simply:

create a file .Rprofile in your home area.
Add the following piece of code to it:

cat(".Rprofile: Setting UK repositoryn") r = getOption("repos") # hard code the UK repo for CRAN r["CRAN"] = "http://cran.uk.r-project.org" options(repos = r) rm(r)

I found this tip in a stackoverflow answer .

OpenCPU

Rahul Nayak — Sun, 05 Jul 2015 18:34:46 -0500

OpenCPU is a system for embedded scientific computing and reproducible research. The OpenCPU server provides a reliable and interoperable HTTP API for data analysis based on R.

The OpenCPU JavaScript client library provides the most seamless integration of R and JavaScript available today.

OpenCPU uses standard R packaging to develop, ship and deploy web applications. Several open source example apps are available from Github.

Installing your own OpenCPU server is super easy and only takes a few minutes.

More at https://www.opencpu.org/

clusterProfiler

Jit — Thu, 16 Jun 2016 18:57:03 -0500

statistical analysis and visulization of functional profiles for genes and gene clusters

Bioconductor version: Release (3.3)

This package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters.

Author: Guangchuang Yu with contributions from Li-Gen Wang and Giovanni Dall'Olio.

Maintainer: Guangchuang Yu

Citation (from within R, enter citation("clusterProfiler")):

Yu G, Wang L, Han Y and He Q (2012). “clusterProfiler: an R package for comparing biological themes among gene clusters.” OMICS: A Journal of Integrative Biology, 16(5), pp. 284-287.
Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("clusterProfiler")

https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html

BioStats class tutorial

Shruti Paniwala — Thu, 16 Mar 2017 01:50:50 -0500

Nice biostat turorial by Ingo Ruczinski

Address of the bookmark: http://www.biostat.jhsph.edu/~iruczins/teaching/

R tuorial

Rahul Nayak — Mon, 31 Jul 2017 08:41:40 -0500

R learning resources

https://flowingdata.com/

Address of the bookmark: https://flowingdata.com/

dupRadar package

Jit — Sun, 04 Feb 2018 14:28:57 -0600

The dupRadar package gives an insight into the duplication problem by graphically relating the gene expression level and the duplication rate present on it. Thus, failed experiments can be easily identified at a glance

Address of the bookmark: https://bioconductor.org/packages/3.7/bioc/vignettes/dupRadar/inst/doc/dupRadar.html

chromoMap-An R package for Interactive visualization and mapping of human chromosomes

Rahul Nayak — Mon, 25 Jun 2018 17:22:24 -0500

chromoMap is an R package that provides interactive, configurable and elegant graphics visualization of the human chromosomes allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. Because of the enormous size of the chromosomes, it is impractical to visualize each element on the same plot. But chromoMap plots provide a magnified view for each of chromosome location to render additional information and visualization specific for that location. You can map thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order (You will see in the demos below). Not ony that, the plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R Shiny applications.

https://cran.r-project.org/web/packages/chromoMap/index.html

MetaPlotR: a Perl/R pipeline for plotting metagenes of nucleotide modifications and other transcriptomic sites

Neel — Mon, 05 Nov 2018 08:12:45 -0600

An increasing number of studies are mapping protein binding and nucleotide modifications sites throughout the transcriptome. Often, these sites cluster in certain regions of the transcript, giving clues to their function. Hence, it is informative to summarize where in the transcript these sites occur. A metagene is a simple and effective tool for visualizing the distribution of sites along a simplified transcript model. In this work, we introduce MetaPlotR, a Perl/R pipeline for creating metagene plots.

Address of the bookmark: https://github.com/olarerin/metaPlotR

GeneOverlap: An R package to test and visualize gene overlaps

Jit — Thu, 27 Dec 2018 19:45:52 -0600

Overlapping gene lists can reveal biological meanings and may lead to novel hypotheses. For example, histone modification is an important cellular mechanism that can pack and re-pack chromatin. By making the chromatin structure more dense or loose, the gene expression can be turned on or off. Tri-methylation on lysine 4 of histone H3 (H3K4me3) is associated with gene activation and its genome-wide enrichment can be mapped by using ChIP-seq experiments. Because of its activating role, if we overlap the genes that are bound by H3K4me3 with the genes that are highly expressed, we should expect a positive association. Similary, we can perform such kind of overlapping between the gene lists of different histone modifications with that of various expression groups and establish each histone modification’s role in gene regulation.

Address of the bookmark: https://bioconductor.org/packages/release/bioc/vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf

chromoMap-An R package for Interactive Visualization and Annotation of Chromosomes

Jit — Sat, 07 Sep 2019 10:45:31 -0500

chromoMap provides interactive, configurable and elegant graphics visualization of chromosomes or chromosomal regions allowing users to map chromosome elements (like genes,SNPs etc.) on the chromosome plot.Each chromosome is composed of loci(representing a specific range determined based on chromosome length) that, on hover, shows details about the annotations in that locus range. The plots can be saved as HTML documents that can be shared easily. In addition, you can include them in R Markdown or in R Shiny applications.

Some of the prominent features of the package are:

visualizing polyploidy simultaneously on the same plot.
annotating groups of elements as distinct colors.
creating chromosome heatmaps.
adjusting chromosome range or visualizing chromosome regions such as genes
adding labels to the plot
adding hyperlinks to each element

Address of the bookmark: https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html