<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/33869?offset=130 https://bioinformaticsonline.com/news/view/19272/translate2r Fri, 21 Nov 2014 01:16:06 -0600 https://bioinformaticsonline.com/news/view/19272/translate2r <![CDATA[translate2R]]> After their presentation at the international “user!” conference, data analysis specialist eoda starts the public alpha testing of translate2R. With the start of alpha testing the innovative migration solution by the company hailing from Kassel discards the working title “translateR” and takes on the final product brand name “translate2R”. translate2R is a service for the automated translation of SPSS® syntax to R code, therefore supporting data analysts with a quick and low-risk migration to R.

The manual translation of many, frequently rather complex SPSS scripts often presents itself as a tedious and error-prone task, and represents a rather large obstacle for many analysts and companies to migrate to a modern, open source data management and analysis tool like R. With translate2R this hurdle will be diminished substantially.

Find at https://service.eoda.de/translater/?lang=en

]]> Rahul Nayak https://bioinformaticsonline.com/pages/view/21365/a-guide-for-complete-r-beginners Fri, 20 Feb 2015 23:36:46 -0600 https://bioinformaticsonline.com/pages/view/21365/a-guide-for-complete-r-beginners <![CDATA[A guide for complete R beginners !]]> This tutorial is intended to introduce users quickly to the basics of R, focusing on a few common tasks that  biologists need to perform  some basic analysis:  load a table, plot some graphs, and perform some basic statistics. More extensive tutorials can be found on the project website and via bioconductor (not covered here).

R-language: http://www.r-project.org

BioConductor: http://www.bioconductor.org

Advantages of R

  • Free!
  • Powerful, many libraries have been created to perform application specific tasks. e.g. analysis of microarray experiments and Next-Gen sequencing (bioconductor: including Bioseq group).
  • Presentation quality graphics
    • Save as a png, pdf or svg
  • History
    • What you do can be saved for the next time you use R.
    • Ability to turn it into an automated script to perform again and again on different data

Disadvantages

Preparation

  • (Optional) Download and save the tutorial data set from
    • http://bioinformatics.knowledgeblog.org/wp-content/uploads/bioinf/kerr/data.tsv
    • Start R (type R on a Linux or Mac terminal, or find the starting link from PC)

Getting More Help

  • Project Home page
    • http://www.r-project.org/
    • Check out the ‘introduction to R’, which is a much more in depth guide .
    • Also R has a built-in help system (see later)

Working directory

This is the directory used to store your data and results. It is useful if it is also the directory where your input data is stored.

  • Mac/Linux: this is the directory where you typed in R
  • PC: Change using the change working directory option
]]> Archana Malhotra https://bioinformaticsonline.com/blog/view/22454/one-page-r-survival-guide Thu, 28 May 2015 21:10:12 -0500 https://bioinformaticsonline.com/blog/view/22454/one-page-r-survival-guide <![CDATA[One page R survival guide !!]]> There any many of the documents have been developed and tested by scientist around the world. I found this one really useful. The data used is available for download as data.zip.

Reference@http://www.datasciencecentral.com/profiles/blogs/one-page-r-a-survival-guide-to-data-science-with-r

  • Templates for the Data Scientist
    1. A Template for Preparing Data: *OnePageR - *R
    2. A Template for Building Models: *OnePageR - *R
  • Getting Started as a Data Scientist
    1. Getting Started with R and Rattle: *Lecture - *Laboratory
    2. Introducing and Interacting with R: *Lecture - *Laboratory
    3. BasicR - OnePage(R) - Writing R scripts
  • Dealing With Data
    1. Read Data into R: *OnePageR - *R
    2. Explore and Summarise Data: *OnePageR - *R
    3. Transform Data: *OnePageR - *R
    4. Dealing with Dates and Time: (PDF, R) Dates and Time
    5. Visualising Data with GGPlot2: *OnePageR - *R
    6. Visualising Data with Maps *OnePageR - *R
    7. Spatial (R) Spatial Analysis
    8. Handling Big Data *OnePageR - *R
  • Descriptive Analytics
    1. Cluster Analysis: *Lecture - *OnePageR - *R
    2. Association Analysis: *Lecture
  • Predictive Analytics
    1. Decision Trees: *Lecture - *OnePageR - *R - *Rattle
    2. Ensembles of Decision Trees: *Lecture - *OnePageR - *R
    3. SVM (R)
    4. KernLab (R)
    5. NeuralNetworks (R)
    6. NNet (R)
  • Model Delivery
    1. Evaluating Models: *OnePageR - *R
    2. Evaluation (R)
    3. Scoring (R)
    4. PMML (R) Exporting Models for Deployment
  • Advanced Topics
    1. Text Mining: *OnePageR - *R
  • Advanced R Topics
    1. Plots (PDF, R) Miscellaneous Plots
    2. Functions (PDF, R) Writing Functions in R
    3. Parallel (PDF, R) Parallel Execution
    4. Packaging (R) Pulling it Together into a Package
    5. Doing R with Style: *OnePageR - *R
    6. Literate Data Mining with KnitR: *Lecture - *OnePageR - *
]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/26573/efficient-genome-searching-with-biostrings-and-the-bsgenome-data-package Mon, 07 Mar 2016 05:18:06 -0600 https://bioinformaticsonline.com/bookmarks/view/26573/efficient-genome-searching-with-biostrings-and-the-bsgenome-data-package <![CDATA[Efficient genome searching with Biostrings and the BSgenome data package]]> Address of the bookmark: https://www.bioconductor.org/packages/3.3/bioc/vignettes/BSgenome/inst/doc/GenomeSearching.pdf

]]> Aasha https://bioinformaticsonline.com/bookmarks/view/27427/rcircos-an-r-package-for-circos-2d-track-plots Fri, 20 May 2016 11:01:13 -0500 https://bioinformaticsonline.com/bookmarks/view/27427/rcircos-an-r-package-for-circos-2d-track-plots <![CDATA[RCircos: an R package for Circos 2D track plots]]> RCircos package provides a simple and flexible way to make Circos 2D track plots with R and could be easily integrated into other R data processing and graphic manipulation pipelines for presenting large-scale multi-sample genomic research data. It can also serve as a base tool to generate complex Circos images.

More at https://bitbucket.org/henryhzhang/rcircos/src

Address of the bookmark: https://bitbucket.org/henryhzhang/rcircos/src

]]> Jit https://bioinformaticsonline.com/bookmarks/view/28855/vcfr Fri, 19 Aug 2016 07:38:24 -0500 https://bioinformaticsonline.com/bookmarks/view/28855/vcfr <![CDATA[vcfR]]> Most variant calling pipelines result in files containing large quantities of variant information. The variant call format (vcf) is an increasingly popular format for this data. The format of these files and their content is discussed in the vignette ‘vcf data.’ These files are typically intended to be post-processed (i.e., filtered) as an attempt to remove false positives or otherwise problematic sites. The R package vcfR provides tools to facilitate this filtering as well as to visualize the effects of choices made during this process.

Address of the bookmark: https://cran.r-project.org/web/packages/vcfR/vignettes/visualization_1.html

]]> Archana Malhotra https://bioinformaticsonline.com/bookmarks/view/29635/r-graphs Fri, 04 Nov 2016 10:48:00 -0500 https://bioinformaticsonline.com/bookmarks/view/29635/r-graphs <![CDATA[R Graphs !!]]> The blog is a collection of script examples with example data and output plots. R produce excellent quality graphs for data analysis, science and business presentation, publications and other purposes. Self-help codes and examples are provided. Enjoy nice graphs !!

Address of the bookmark: http://rgraphgallery.blogspot.be/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/32152/upsetr-shiny-app Fri, 14 Apr 2017 06:19:54 -0500 https://bioinformaticsonline.com/bookmarks/view/32152/upsetr-shiny-app <![CDATA[UpSetR Shiny App!]]> UpSetR generates static UpSet plots. The UpSet technique visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes.

To begin, input your data using one of the three input styles.

  1. "File" takes a correctly formatted.csv file.
  2. "List" takes up to 6 different lists that contain unique elements, similar to that used in the web applications BioVenn (Hulsen et al., 2008) and jvenn (Bardou et al., 2014)
  3. "Expression" takes the input used by the venneuler R package (Wilkinson, 2015)

Address of the bookmark: https://gehlenborglab.shinyapps.io/upsetr/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34400/ioniser-tools-for-the-quality-assessment-of-data-produced-by-oxford-nanopore%E2%80%99s-minion-sequencer Thu, 23 Nov 2017 10:24:19 -0600 https://bioinformaticsonline.com/bookmarks/view/34400/ioniser-tools-for-the-quality-assessment-of-data-produced-by-oxford-nanopore%E2%80%99s-minion-sequencer <![CDATA[IONiseR: tools for the quality assessment of data produced by Oxford Nanopore’s MinION sequencer]]> This package is intended to provide tools for the quality assessment of data produced by Oxford Nanopore’s MinION sequencer. It includes a functions to generate a number plots for examining the statistics that we think will be useful for this task.

However, nanopore sequencing is an emerging and rapidly developing technology. It is not clear what will be most informative. We hope that IONiseR will provide a framework for visualisation of metrics that we haven’t thought of, and welcome feedback at mike.smith@embl.de.

If you’re not interested in the quality assement of the raw or event level data, and want to jump straight to the getting FASTQ format files from fast5 files you can go straight to the final section of this document.

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

]]> Jit https://bioinformaticsonline.com/news/view/36418/r-350-has-been-released Thu, 26 Apr 2018 11:31:58 -0500 https://bioinformaticsonline.com/news/view/36418/r-350-has-been-released <![CDATA[R 3.5.0 has been Released!]]>
  • The latest version of R is a major release! It comes with a ton of new features, including performance and speed improvements
  • All R packages will now be byte-compiled, hence boosting packages installed from GitHub
  • You may need to re-install all previously installed R packages; old scripts however will continue to work normally

    • More at https://cran.r-project.org/doc/manuals/r-release/NEWS.html

    ]]>     Jit