BOL: Related items

‘dockr’: the R container

Jit — Mon, 23 Dec 2019 09:56:49 -0600

dockr 0.8.6 is now available on CRAN. dockr is a minimal toolkit to build a lightweight Docker container image for your R package, in which the package itself is available. The Docker image seeks to mirror your R session as close as possible with respect to R specific dependencies. Both dependencies on CRAN R packages as well as local non-CRAN R packages will be included in the Docker container image.

If you want to know, how Docker works, and why you should consider using Docker, please take a look at the Docker website.

Address of the bookmark: https://www.docker.com/why-docker

PANEV: an R package for a pathway-based network visualization

Jit — Sun, 09 Feb 2020 12:41:52 -0600

PANEV (PAthway NEtwork Visualizer) is an R package set for gene/pathway-based network visualization. Based on information available on KEGG, it visualizes genes within a network of multiple levels (from 1 to n) of interconnected upstream and downstream pathways. The network graph visualization helps to interpret functional profiles of a cluster of genes.

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3371-7

Address of the bookmark: https://github.com/vpalombo/PANEV

RAINBOWR: Reliable Association INference By Optimizing Weights with R (R package for SNP-set GWAS and multi-kernel mixed model)

Surabhi Chaudhary — Fri, 28 Feb 2020 23:27:37 -0600

RAINBOWR(Reliable Association INference By Optimizing Weights with R) is a package to perform several types of GWAS as follows.

Single-SNP GWAS with RGWAS.normal function
SNP-set (or gene set) GWAS with RGWAS.multisnp function (which tests multiple SNPs at the same time)
Check epistatic (SNP-set x SNP-set interaction) effects with RGWAS.epistasis (very slow and less reliable)

https://github.com/KosukeHamazaki/RAINBOWR

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007663

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

Address of the bookmark: https://github.com/KosukeHamazaki/RAINBOWR

Platypus – R package for object detection and image segmentation.

BioStar — Mon, 09 Nov 2020 02:56:25 -0600

platypus is an R package for object detection and semantic segmentation. Currently using

platypus you can perform:

multi-class semantic segmentation using U-Net architecture
multi-class object detection using YOLOv3 architecture

You can install the latest version of platypus with remotes package:

remotes::install_github("maju116/platypus")

Note that in order to install platypus you need to install keras and tensorflow packages and Tensorflow version >= 2.0.0 ( Tensorflow 1.x will not be supported!)

Address of the bookmark: https://github.com/maju116/platypus

R Shiny in Life Sciences – Top 7 Dashboard Examples

Neel — Fri, 01 Apr 2022 19:05:03 -0500

R Shiny is one of the easiest ways for developers to make production-ready dashboards when speed and functionality are crucial. Shiny is approachable with a lot of documentation available, and because of this, a lot of developers/researchers with non-coding backgrounds are able to produce some impressive results. The whole ecosystem is easy to get your head around and pretty much limitless with regard to what you can do.

Address of the bookmark: https://www.r-bloggers.com/2022/03/r-shiny-in-life-sciences-top-7-dashboard-examples/

R Package for PCA Analysis

LEGE — Sun, 24 Mar 2024 20:06:24 -0500

An R package for performing principal component analysis (PCA) of genomics data. The package performs PCA, generates the publication-ready plots, and identifies population-specific outlier individuals. The package can be accessed on GitHub: https://github.com/Devashish13/PopulationStructure

Address of the bookmark: https://rpubs.com/Devashish13/PCAGenomics

useR!

Jitendra Narayan — Wed, 10 Jul 2013 14:41:49 -0500

The R project actively supports two conference series, organized regularly by members from the R community: useR! - providing a forum to the R user community - and DSC - a platform for developers of statistical software.

Recently useR! conference have been organized University of Castilla-La Mancha, Albacete, Spain.

http://www.edii.uclm.es/~useR-2013//

Rdatamining.com : R and Data Mining

Poonam Mahapatra — Thu, 15 Aug 2013 18:37:23 -0500

This website presents examples, documents and resources on data mining with R.
Documents on using R for data mining are available to download for non-commercial personal use, including R Reference card for Data Mining, R and Data Mining: Examples and Case Studies and Time Series Analysis and Mining with R.

Address of the bookmark: http://www.rdatamining.com/

Upgrade R 3.0.3

Rahul Nayak — Mon, 10 Mar 2014 11:23:51 -0500

R is a free software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls and surveys of data miners are showing R's popularity has increased substantially in recent years. Recently the new version of R codename “Warm Puppy" have been released.

You can download the latest version from here http://cran.rstudio.com/ . Or, if you are using Windows, you can upgrade to the latest version using the installr package http://cran.r-project.org/web/packages/installr/ . Simply run the following code:

# installing/loading the package:
if(!require(installr)) {
install.packages("installr"); require(installr)} #load / install+load installr

updateR()

I try to keep the installr package updated and useful. If you have any suggestions or remarks on the package, you’re invited to leave a comment below.

If you use the global library system http://www.r-statistics.com/2010/04/changing-your-r-upgrading-strategy-and-the-r-code-to-do-it-on-windows/ , you can run the following in the new version of R:

source("http://www.r-statistics.com/wp-content/uploads/2010/04/upgrading-R-on-windows.r.txt")
New.R.RunMe()

Reference:

http://www.r-statistics.com/2014/03/r-3-0-3-is-released/

Surrogate Variable Analysis (SVA)

Jit — Thu, 30 Oct 2014 08:01:58 -0500

The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways:

(1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS),

(2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and

(3) removing batch effects with known control probes (Leek 2014 biorXiv).

Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics).

More at http://www.bioconductor.org/packages/release/bioc/html/sva.html