BOL: Related items

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

RITA: Rapid identification of high-confidence taxonomic assignments for metagenomic data

Jit — Mon, 27 Nov 2017 08:25:33 -0600

RITA is a standalone software package and Web server for taxonomic assignment of metagenomic sequence reads. By combining homology predictions from BLAST or UBLAST with compositional classifications from a Naive Bayes classifier, RITA is able to achieve very high accuracy on short reads. Unlike other hybrid approaches which combine these predictions for all sequences to be classified, RITA uses a pipeline to first identify cases where both types of classifier are in agreement, which constitute the highest-confidence set. Sequences not classified in this manner are subjected to a series of downstream classification steps.

This work has been accepted for publication:

MacDonald NJ, Parks DH, and Beiko RG. Rapid identification of taxonomic assignments. Accepted to Nucleic Acids Research April 4, 2012.

If you have any questions or bug reports, please let us know at .

Address of the bookmark: http://kiwi.cs.dal.ca/Software/RITA

TACOA: Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach

Poonam Mahapatra — Tue, 15 May 2018 09:52:28 -0500

TACOA is a software that can accurately predict the taxonomic origin of genomic fragments from metagenomic data sets by combining the advantages of the k -NN approach with a smoothing kernel function. TACOA can be easily installed and run on a desktop computer, therefore allowing researchers to locally analyze their metagenomic sequence data or integrate it into their pipelines.

Address of the bookmark: http://www.cebitec.uni-bielefeld.de/index.php/2-uncategorised/99-tacoa

ClipCrop: a tool for detecting structural variations with single-base resolution using soft-clipping information

Rahul Nayak — Thu, 04 Oct 2018 16:39:28 -0500

This is a tool for detecting structural variations using soft-clipping information From SAM files.

https://github.com/shinout/clipcrop

Address of the bookmark: https://github.com/shinout/clipcrop

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