BOL: Related items

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/

Venn Diagrams on R Studio

Jitendra Prajapati — Mon, 25 Apr 2016 16:22:28 -0500

First step: Install & load “VennDiagram” package.

# install.packages('VennDiagram')
library(VennDiagram)

Second step: Load data

Add filepath if “catdoge.csv” is not in working-directory.

d <- read.csv("catdoge.csv")

Address of the bookmark: http://rstudio-pubs-static.s3.amazonaws.com/13301_6641d73cfac741a59c0a851feb99e98b.html

radar charts with ggplot2

Rahul Nayak — Tue, 17 Sep 2019 23:01:28 -0500

ggradar allows you to build radar charts with ggplot2. This package is based on Paul Williamson’s code, with new aesthetics and compatibility with ggplot2 2.0.

It was inspired by d3radaR, an htmlwidget built by timelyportfolio.

Address of the bookmark: https://github.com/ricardo-bion/ggradar

Trelliscope: flexibly visualize large, complex data in great detail from within the R statistical programming environment.

Jit — Tue, 21 Jan 2020 04:22:49 -0600

Trelliscope provides a way to flexibly visualize large, complex data in great detail from within the R statistical programming environment. Trelliscope is a component in the DeltaRho environment.

For those familiar with Trellis Display, faceting in ggplot, or the notion of small multiples, Trelliscope provides a scalable way to break a set of data into pieces, apply a plot method to each piece, and then arrange those plots in a grid and interactively sort, filter, and query panels of the display based on metrics of interest. With Trelliscope, we are able to create multipanel displays on data with a very large number of subsets and view them in an interactive and meaningful way.

Address of the bookmark: http://deltarho.org/docs-trelliscope/#introduction

Post Doc Computational Biology, Bioinformatics - Network Biology & Data Science, NGS (m/f/d)

Sat, 15 Feb 2020 06:13:35 -0600

https://www.jobvector.de/jobs-stellenangebote/biologie-life-sciences/forschung-entwicklung/post-doc-computational-biology-bioinformatics-network-biology-data-science-ngs-129867.html?suid=e522e9793b41817e52ac58d6963b94e2519920df

Requirements
Doctoral degree in Bioinformatics, Computational Biology, (Bio)physics/-mathematics, Biochemistry/Biology or similar with strong quantitative and numeric focus
Ability to numerically process complex and large data sets
Good programming skills (R/Bioconductor and/or Python preferred, Linux is a plus)
Experience in analyzing next-generation sequencing data sets using network biology
Scientific publication record in applied bioinformatics
Familiarity with single cell NGS analyses and other –omics techniques is a plus, but not essential

GeneMates: an R package for Detecting Horizontal Gene Co-transfer between Bacteria Using Gene-gene Associations Controlled for Population Structure

Rahul Nayak — Sat, 07 Mar 2020 05:52:20 -0600

GeneMates is an R package implementing a network approach to identify horizontal gene co-transfer (HGcoT) between bacteria using whole-genome sequencing (WGS) data. It is particularly useful for investigating intra-species HGcoT, where presence-absence status of acquired genes is usually confounded by bacterial population structure due to clonal reproduction.

https://www.biorxiv.org/content/10.1101/2020.02.29.970970v1

Address of the bookmark: https://github.com/wanyuac/GeneMates

CRBHits: From Conditional Reciprocal Best Hits to Codon Alignments and Ka/Ks in R

Shruti Paniwala — Wed, 11 Nov 2020 23:06:03 -0600

CRBHits is a coding sequence (CDS) analysis pipeline in R (R Core Team, 2019). It reimplements the Conditional Reciprocal Best Hit (CRBH) algorithm crb-blast and covers all necessary steps from sequence similarity searches, codon alignments to Ka/Ks calculations and synteny. The new R package targets ecology, population and evolutionary biologists working in the field of comparative genomics.

Address of the bookmark: https://gitlab.gwdg.de/mpievolbio-it/crbhits

Powerful books for learning data analysis with R

LEGE — Tue, 28 May 2024 07:42:56 -0500

R is powerful tool for data analysis, visualization, and machine learning. And it costs $0 to use! Here are six FREE books you can use to learn R today:

https://csgillespie.github.io/efficientR/

https://r-graphics.org/

https://rstudio-education.github.io/hopr/

https://r-pkgs.org/

https://r4ds.had.co.nz/

Address of the bookmark: https://r-graphics.org/

Smyth Lab

Sun, 14 Jul 2013 12:26:18 -0500

Statistical functional genomics in experimental medicine
The genome projects and the accelerated development of high-throughput genomic technologies such as microarrays have revolutionised biology. Making the most of this revolution requires the marriage of researchers from mathematical and biological backgrounds.

Research Area:
Linear models for microarray data
Digital gene expression technologies
Detection of molecular pathways
Bioinformatics resources for medical research

Link @ http://www.wehi.edu.au/faculty_members/professor_gordon_smyth/

Statistics and probability

Jit — Tue, 08 Nov 2016 07:34:25 -0600

Topics

Displaying and describing data

Modeling distributions of data

Describing relationships in quantitative data

Designing studies

Probability

Random variables

Sampling distributions

Confidence intervals (one sample)

Significance tests (one sample)

Significance tests and confidence intervals (two samples)

Inference for categorical data (chi-square tests)

Advanced regression (inference and tran

Address of the bookmark: https://www.khanacademy.org/math/statistics-probability