Search Results for: Bioconductor

08/23/2024 - In this webinar, Dr. Carey will provide an introduction to Bioconductor for genomic data science.   Bioconductor.org enters its third decade as an NHGRI/NCI-funded resource for many aspects of genomic data Read More...

The easiest way to search Bioconductor for a topic specific package is to use the BiocViews search . BiocViews includes a controlled vocabulary to categorize Bioconductor packages. Because packages are tagged using this vocabulary, they can Read More...

Bioconductor packages are divided into four types: software annotation data experiment data workflows. Software packages themselves can be subdivided into packages that provide infrastructure (i.e., classes) to store and access data, and packages that Read More...

12/16/2020 - Recording Slides are here. Hands-on portion is here. Bioconductor is a large, NIH-funded project that provides tools and data resources for the analysis and comprehension of high-throughput biological data. Bioconductor uses the R statistical Read More...

03/19/2026 - In this talk, Dr. Carey will describe how Bioconductor approaches new challenges in supporting open method development and reproducible analyses in genomic data science. He will discuss aspects of the project that bear on Read More...

02/15/2024 - This lesson will be divided into two parts. Part 1 will introduce Bioconductor, an R package repository for the analysis of biological data. Part 2 will introduce RMarkdown and Quarto for report generation with R.

This lesson will be divided into two parts. Part 1 will introduce Bioconductor, an R package repository for the analysis of biological data. Part 2 will introduce RMarkdown and Quarto for report generation with R.

12/18/2023 - In this lesson, we will learn about specialized data containers / classes that are shared across Bioconductor packages. These classes allow us to store and easily manage multiple -omics types. We will discuss some of Read More...

In this lesson, we will learn about specialized data containers / classes that are shared across Bioconductor packages. These classes allow us to store and easily manage multiple -omics types. We will discuss some of the Read More...

-omics data can be fairly complex, and data structures are a useful way of organizing and working with complex data. Many data structures are used or extended across multiple Bioconductor packages, thereby allowing users to Read More...

Bioconductor is a repository for R packages related to biological data analysis, primarily bioinformatics and computational biology, and as such it is a great place to search for -omics packages and pipelines.

Here, we will review major concepts taught in the first three sectinos. We will explore R Markdown functionality, to help learners generate shareable, professional, and reproducible data analysis reports. We will also introduce Bioconductor, including Read More...

Objectives To explore Bioconductor, a repository for R packages related to biological data analysis. To generate high quality data reports using R Markdown to make data analysis more reproducible. Reminder: Uploading files from RStudio Server Read More...

Resources for B https://github.com/seandavi/SDIntroToR https://github.com/Bioconductor/BiocManager

Launch RStudio Desktop from the Applications folder as you did before. Click the + item (upper far-left in graphic below) and open a new R Script. In the upper-left pane, type the following command and click Read More...

12/20/2016 - ABOUT THE COURSE R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series Read More...

11/22/2016 - ABOUT THE COURSE R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series Read More...

11/09/2015 - A Short Course in R for Biologists "A Short Course in R for Biologists" is a two-day course given in four three-hour sessions entitled: Introduction to R, Introduction to Bioconductor, Introduction to Read More...

Objectives To explore Bioconductor, a repository for R packages related to biological data analysis. To learn about options for report generation with R: RMarkdown and Quarto. Introducing Bioconductor Bioconductor is both an open source project Read More...

10/22/2015 - /* element spacing */ p, pre { margin: 0em 0em 1em; } /* center images and tables */ img, table { margin: 0em auto 1em; } p { text-align: justify; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Read More...

01/29/2015 - /* element spacing */ p, pre { margin: 0em 0em 1em; } /* center images and tables */ img, table { margin: 0em auto 1em; } p { text-align: justify; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Read More...

Objectives To explore Bioconductor, a repository for R packages related to biological data analysis. To better understand S4 objects as they relate to the Bioconductor core infrastructure. To learn more about a popular Bioconductor S4 Read More...

A repository for R packages related to biological data analysis , primarily bioinformatics and computational biology. a great place to search for -omics packages and pipelines. Released every 6 months and work with a specific version of Read More...

A repository for R packages related to biological data analysis , primarily bioinformatics and computational biology. a great place to search for -omics packages and pipelines. Released every 6 months and work with a specific version of Read More...

Follow the directions to install DESeq: DESeq To install these packages, start R (version "4.0") by typing "R" at the command line and enter: You will know you are in R when Read More...

To explore Bioconductor, a repository for R packages related to biological data analysis. To better understand S4 objects as they relate to the Bioconductor core infrastructure. To learn more about a popular Bioconductor S4 class, Read More...

library(tidyverse) # dplyr and ggplot2; CRAN library(Seurat) # Seurat toolkit; CRAN library(hdf5r) # for data import; CRAN library(patchwork) # for plotting; CRAN library(presto) # for differential expression; Github library(glmGamPoi) # for sctransform; Bioconductor library( Read More...

Material from this lesson was either taken directly or adapted from the Intro to R and RStudio for Genomics lesson provided by datacarpentry.org and The Bioconductor Project: Introduction to Bioconductor from the Carpentries Incubator.

There are a number of Bioconductor events/conferences throughout the year including the annual BioC conference in North America and similar regional conferences throughout the world (e.g., BioC Asia, BioC Europe). Upcoming events (e. Read More...

R packages are loadable extensions that contain code, data, documentation, and tests in a standardized shareable format that can easily be installed by R users. CRAN = the primary repository for R packages. To install a Read More...

S4 is a rigorous system that forces you to think carefully about program design. It’s particularly well-suited for building large systems that evolve over time and will receive contributions from many programmers. --- Advanced Read More...

As a reminder, R packages are loadable extensions that contain code, data, documentation, and tests in a standardized shareable format that can easily be installed by R users. The primary repository for R packages is Read More...

The following sources inspired this content: https://www.sc-best-practices.org https://hbctraining.github.io/scRNA-seq_online/ https://bioconductor.org/books/3.15/OSCA.basic/

Following Cell Ranger and/or other pre-processing tools, you will have a gene-by-cell counts table for each sample. The three most popular frameworks for analyzing these count matrices include: R ( Seurat ). Seurat, brought to you Read More...

The following sources inspired this content: https://www.sc-best-practices.org https://hbctraining.github.io/scRNA-seq_online/ https://bioconductor.org/books/3.15/OSCA.basic/ This is only a small subset of tools available to single cell RNASeq. Read More...

BTEP scRNA-Seq FAQs Training modules available on Github Orchestrating Single Cell Analysis with Bioconductor Single Cell Best Practices 2023 BTEP Single Cell Annotation Seminar Series Event recordings are located in the BTEP Video Archive . Analysis Guides Read More...

To explore Bioconductor, a repository for R packages related to biological data analysis. To learn about options for report generation with R: RMarkdown and Quarto.

Material from this lesson was adapted from Chapter 3 of R for Data Science and from "Data Visualization", Introduction to data analysis with R and Bioconductor , which is part of the Carpentries Incubator.

Material from this lesson was either taken directly or adapted from the Intro to R and RStudio for Genomics lesson provided by datacarpentry.org . Material was also inspired by content from Introduction to data analysis Read More...

Material from this lesson was either taken directly or adapted from the Intro to R and RStudio for Genomics lesson provided by datacarpentry.org . Material was also inspired by content from Introduction to data analysis Read More...

For package support and questions on related topics, there is an active Bioconductor support site that operates similarly to other forums (e.g., Biostars ). There is also a Slack workspace for general community interaction with Read More...

There are data sets available in R to practice with or showcase different packages. For today's lesson and the remainder of this course, we will use data from the Bioconductor package airway to showcase Read More...

R is a particularly great resource for statistical analyses, plotting, and report generating. wide use = functions and packages covering a broad range of topics. CRAN has 20,000 + packages Bioconductor (v 3.18) includes 2,266 software packages, 429 experiment data packages, 920 Read More...

Material from this lesson was either taken directly or adapted from the Intro to R and RStudio for Genomics lesson provided by datacarpentry.org and from a 2021 workshop entitled Introduction to Tidy Transciptomics by Maria Read More...

Material from this lesson was either taken directly or adapted from the Intro to R and RStudio for Genomics lesson provided by datacarpentry.org and from a 2021 workshop entitled Introduction to Tidy Transciptomics by Maria Read More...

Material from this lesson was either taken directly or adapted from the Intro to R and RStudio for Genomics lesson provided by datacarpentry.org and from a 2021 workshop entitled Introduction to Tidy Transciptomics by Maria Read More...

R is a particularly great resource for statistical analyses, plotting, and report generating. The fact that it is widely used means that users do not need to reinvent the wheel. There is a package available Read More...

Size: nrow() - number of rows ncol() - number of columns Content: head() - returns first 6 rows by default tail() - returns last 6 rows by default Names: colnames() - returns column names rownames() - returns Read More...

An important goal of the Bioconductor project is interoperability, or the ability of packages to work together using shared data classes and methods. This is achieved through the use of common data structures. These common Read More...

There are a number of other file types you may be interested in. For genomic specific formats, you will likely need to install specific packages; check out Bioconductor for packages relevant to bioinformatics. For information Read More...

The following is not a comprehensive list: tidySingleCellExperiment - for SingleCellExperiment objects tidyseurat - for Seurat objects Note Seurat is not a Bioconductor package. It is a CRAN package. tidyseurat is also a CRAN package. Read More...

There are many packages available to work with microbiome data in R. While there is an R API in the works for QIIME 2, for now, users can use the R package, qiime2R , to easily Read More...

Before we can do anything with our data, we need to first import it into R. There are several ways to do this. First, the RStudio IDE has a drop down menu for data import. Read More...

11/15/2023 - This session introduces two versions of the R/ Bioconductor package OmicCircos to generate high-quality circular plots for visualizing multi-dimensional omics data: coding in the R environment for programmers; point-and-click OmicCircos R Shiny app on Read More...

Congratulations, your experiments have been completed and you have a large amount of data. Now, it’s time to analyze these data. But where do you begin? How can you gain the most meaning from Read More...

R is both a computational language and environment for statistical computing and graphics. It is open-source and widely used by scientists, not just bioinformaticians. Base packages of R are built into your initial installation, but Read More...

To take full advantage of R, you need to install R packages. R packages are loadable extensions that contain code, data, documentation, and tests in a standardized shareable format that can easily be installed by Read More...

Additional Resources BTEP scRNA-Seq FAQs Training modules available on Github Orchestrating Single Cell Analysis with Bioconductor Single Cell Best Practices 2023 BTEP Single Cell Annotation Seminar Series Event recordings are located in the BTEP Video Archive . Read More...

Now that we have loaded the package, we can import our data. Generally, in R programming, functions that involve data import begin with "read / Read". Seurat includes a number of read functions for Read More...

Lesson 1: Introduction to R and RStudio\ Lesson 2: The Basics of R Programming (syntax and base R)\ Lesson 3: R Data Structures: Introducing Data Frames\ Lesson 4: Data Frames and Data Wrangling (part 1)\ Lesson 5: Data Frames and Data Read More...

To take full advantage of R, you need to install R packages. R packages are loadable extensions that contain code, data, documentation, and tests in a standardized shareable format that can easily be installed by Read More...

Welcome! Welcome to the R Introductory Series! Who: Novices and beginners\ What: A course series introducing R and RStudio. This course will introduce the foundational skills necessary to begin to analyze and visualize data in Read More...

Course Overview Welcome to the R Introductory Series! A series of introductory lessons in R for scientists. This course will include a series of lessons for individuals new to R or with limited R experience . Read More...

These steps can be used to create a publish worthy figure. For example, let's create a volcano plot of our differential expression results. A volcano plot is a type of scatterplot that shows statistical Read More...

R is both a computational language and open-source environment for statistical computing and graphics. While R programming was ranked 20 th in popularity when compared with other programming languages in December 2023, it remains a favorite among Read More...

Content from this lesson was inspired by or taken from the following sources: https://bioconductor.org/packages/devel/bioc/vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html https://carpentries-incubator.github.io/bioc-project/05-s4.html#s4-classes-and-methods https:// Read More...

Course Overview Welcome to the Data Wrangling with R course series The purpose of this course is to introduce you to essential R packages and functions that will make your life easier when it comes Read More...

Why did we focus so heavily on the tidyverse if it can't be used to manipulate Bioconductor objects? Well, for one, regardless of whether you are a user of Bioconductor packages, you will often Read More...

Notes: PANTHER DAVID Qiagen IPA - more detailed R Bioconductor clusterProfiler GSVA goseq GOexpress (for GO annotation) pathview EGSEA seqGSEA CRAN GANPA GOxploreR geneset GSEA, GSVA, ORA https://www.rna-seqblog.com/blitzgsea-efficient-computation-of-gene-set-enrichment-analysis-in-python/ https://github.com/ Read More...

The Human Brain Reference (HBR) RNA sequencing data are derived from RNA extracted from 23 human brains brains are from both males and females, age ranging from 60 to 80 years The Universal Human Reference data used RNA Read More...

The Human Brain Reference (HBR) RNA sequencing data are derived from RNA extracted from 23 human brains brains are from both males and females, age ranging from 60 to 80 years The Universal Human Reference data used RNA Read More...

  Update: Please contact your IC representative to get access to the Anaconda Business license as it has transitioned from enterprise to seat-based licensing. For questions, email anaconda@nih.gov. Anaconda ( https://www.anaconda. Read More...

Listed below are the video recordings of past BTEP events (classes, seminars, workshops). Videos are hosted on various servers and may play slightly differently. Some videos may be downloaded for local viewing. Recorded Videos of Read More...

Some tools have been described in the previous session (see here ). Today, we will be focusing on the SingleR tool, which also requires the celldex package . In short, SingleR operates by comparing your current dataset Read More...

This lesson provides an introduction to R in the context of single cell RNA-Seq analysis with Seurat. Learning Objectives Learn about options for analyzing your scRNA-Seq data. Learn about resources for learning R programming. Learn Read More...

1. Introduction and Learning Objectives This tutorial has been designed to demonstrate common secondary analysis steps in a scRNA-Seq workflow. We will start with a merged Seurat Object with multiple data layers representing multiple samples that Read More...

Learning Objectives This tutorial was designed to demonstrate common secondary analysis steps in a scRNA-Seq workflow. We will start with a merged Seurat Object with multiple data layers representing multiple samples. Throughout this tutorial we Read More...

Learning Objectives To understand: 1. the difference between R and RStudioIDE. 2. how to work within the RStudio environment including: creating an Rproject and Rscript navigating between directories using functions obtaining help how R can enhance data Read More...

Learning Objectives Learn about data structures including factors, lists, data frames, and matrices. Load, explore, and access data in a tabular format (data frames) Learn to write out (export) data from the R environment Data Read More...

Objectives To understand some of the most basic features of the R language including: Creating R objects and understanding object types Using mathematical operations Using comparison operators Creating, subsetting, and modifying vectors By the end Read More...

Objectives Review the grammar of graphics template. Learn about the statistical transformations inherent to geoms. Learn more about fine tuning figures with labels, legends, scales, and themes. Learn how to save plots with ggsave() . Review Read More...

In this lesson, attendees will learn how to transform, summarize, and reshape data using functions from the tidyverse. Learning Objectives Continue to wrangle data using tidyverse functionality. To this end, you should understand: how to Read More...

Data visualization with ggplot2 Objectives To learn how to create publishable figures using the ggplot2 package in R. By the end of this lesson, learners should be able to create simple, pretty, and effective figures. Read More...

In this lesson, attendees will learn how to transform, summarize, and reshape data using functions from the tidyverse. Learning Objectives Continue to wrangle data using tidyverse functionality. To this end, you should understand: how to Read More...

This lesson will introduce data wrangling with R. Attendees will learn to filter data using base R and tidyverse (dplyr) functionality. Learning Objectives Understand the concept of tidy data. Become familiar with the tidyverse packages. Read More...

Data import and reshape Objectives 1. Learn to import multiple data types 2. Data reshape with tidyr : pivot_longer() , pivot_wider() , separate() , and unite() Installing and loading packages So far we have only worked with objects that Read More...

Lesson 7: Course Wrap-Up Learning Objectives Introduce the QIIME2 microbiome workflow for Biowulf Review key concepts Showcase additional plugins QIIME 2 on Biowulf As mentioned previously, QIIME 2 is installed on Biowulf. To see available versions use module Read More...