BOL: Related items

A quick guide to Phred scaling

biogeek — Sat, 22 Feb 2020 02:57:19 -0600

A quick guide to Phred scaling

• Phred value = −10 * log10(ε)

• Examples:
• 90% confidence (10% error rate) = Q10
• 99% confidence (1% error rate) = Q20
• 99.9% confidence (.1% error rate) = Q30

• SAM encoding adds 33 to the value (because
ASCII 33 is the first visible character)

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

Pacman

Rahul Nayak — Mon, 16 Feb 2015 12:15:17 -0600

The pacman package is an R package management tool that combines the functionality of base library related functions into intuitively named functions. This package is ideally added to .Rprofile to increase workflow by reducing time recalling obscurely named functions, reducing code and integrating functionality of base functions to simultaneously perform multiple actions.

Function names in the pacman package follow the format of p_xxx where ‘xxx’ is the task the function performs. For instance the p_load function allows the user to load one or more packages as a more generic substitute for the library or require functions and if the package isn’t available locally it will install it for you.

Installation

To download the development version of pacman:

Download the zip ball or tar ball, decompress and run R CMD INSTALL on it, or use the devtools package to install the development version:

## Make sure your current packages are up to date
update.packages()
## devtools is required
devtools::install_github("trinker/pacman")

Note: Windows users need Rtools and devtools to install this way.

More at https://github.com/trinker/pacman

CSBB-v1.0

Neel — Wed, 29 Jun 2016 07:33:05 -0500

CSBB is a command line based bioinformatics suite to analyze biological data acquired through varied avenues of biological experiments. CSBB is implemented in Perl, while it also leverages the use of R and python in background for specific modules. Major focus of CSBB is to allow users from biology and bioinformatics community, to get benefited by performing down-stream analysis tasks while eliminating the need to write programming code. CSBB is currently available on Linux, UNIX, MAC OS and Windows platforms.

Currently CSBB provides 13 modules focused on analytical tasks like performing upper-quantile normalization on expression data or convert genome wide gene expression to z-scores when comparing expression data from different platforms.

More at https://github.com/skygenomics/CSBB-v1.0

Address of the bookmark: https://github.com/skygenomics/CSBB-v1.0

Shinyheatmap

Jit — Fri, 21 Oct 2016 05:12:11 -0500

Background: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps. Results: We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Conclusions: shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap.

More at http://biorxiv.org/content/early/2016/09/21/076463

Address of the bookmark: http://shinyheatmap.com/

fineSTRUCTURE v2 & GLOBETROTTER

Shruti Paniwala — Mon, 13 Feb 2017 08:40:23 -0600

Software available at this site

FineSTRUCTURE version 2, a pipeline for running ChromoPainter and FineSTRUCTURE for population inference. A GUI is available for interpretation. Download from the Downloads page.
FineSTRUCTURE R scripts, a facility for exploring the results when the GUI is unavailable.
GLOBETROTTER, the admixture dating method based on ChromoPainter. Download from the Downloads page.
AdmixturePainting, A set of R tools to inmterpret the results of ADMIXTURE and STRUCTURE-like mixture models.
RADpainter, finestructure and ChromoPainter for RAD tag data used for non-model organisms.
Scripts to perform many types of conversion. Included in the main software download from the Downloads page.

What this page is This page provides information about and downloads for methodology for Chromosome Painting. It is not a facility to analyse your genome. Sorry if you were misled by the punchy name!
About Chromosome Painting Painting is an efficient way of identifying important haplotype information from dense genotype data. It describes ancestry in an efficient way suitable for a range of further analyses, including population identification and admixture dating.

Address of the bookmark: http://paintmychromosomes.com/

Circular plots in R

Jit — Mon, 19 Jun 2017 06:20:27 -0500

Circular plots are useful to represent complicated informations. They are used in 2 specific cases: when you have long axis and numerous categories, and when you want to show relationships between elements. The circos gallery displays several examples of circular plots, what gives a nice overview of the possibilities. Circos is the most famous

tool to create circular plots. Thanks to Zuguang Gu, the Circlize library makes the circos functions available in R! It implements low-level graphic functions for adding common graphics in a circular layout. This page aims to learn you how to use the library, so I strongly advise to read the graphics in the proposed order!

http://www.r-graph-gallery.com/portfolio/circular-plot/

Address of the bookmark: http://www.r-graph-gallery.com/portfolio/circular-plot/