petehaitch/bioc2020_delayedarray_workshop repository overview

⁠Effectively using the DelayedArray framework to support the analysis of large datasets

⁠Key resources

• Docker image: Docker Hub⁠
• Workshop material: pkgdown website⁠
• Video: YouTube⁠

⁠Workshop description

This workshop gives an introductory overview of the DelayedArray framework, which can be used by R / Bioconductor packages to support the analysis of large array-like datasets. A DelayedArray is like an ordinary array in R, but allows for the data to be in-memory, on-disk in a file, or even hosted on a remote server.

Workshop participants will learn where they might encounter a DelayedArray in the wild while using Bioconductor and understand the fundamental concepts underlying the DelayedArray framework. This workshop will feature introductory material, ‘live’ coding, and Q&A, all of which are adapted from the content below.

⁠Instructor

• Peter Hickey⁠ ([email protected]⁠)

⁠Pre-requisites

• Basic knowledge of R syntax.
• Familiarity with common operations on matrices in R, such as colSums() and colMeans().
• Some familiarity with S4 objects may be helpful but is not required.

⁠Workshop Participation

Students will be able to run code examples from the workshop material. There will be a Q&A session in the second half of the workshop.

⁠R / Bioconductor packages used

These packages are the focus of this workshop:

• DelayedArray⁠
• HDF5Array⁠
• DelayedMatrixStats⁠

Please see the workshop DESCRIPTION⁠ for a full list of dependencies.

⁠Time outline

⁠Workshop goals and objectives

⁠Learning goals

• Learn of existing packages and functions that use the DelayedArray framework.
• Develop a high-level understanding of classes and packages that implement the DelayedArray framework.
• Become familiar with the fundamental concepts of delayed operations, block processing, and realization.
• Reason about potential bottlenecks, and how to avoid or reduce these, in algorithms operating on DelayedArray objects.

⁠Learning objectives

• Identify when an object is a DelayedArray or one of its derivatives.
• Be able to recognise when it is useful to use a DelayedArray instead of an ordinary array or other array-like data structure.
• Learn how to load and save a DelayedArray-backed object.
• Learn how the ‘block size’ and ‘chunking’ of the dataset affect performance when operating on DelayedArray objects.
• Take away some miscellaneous tips and tricks I’ve learnt over the years when working with DelayedArray-backed objects.

⁠Docker set up

• Run docker run -e PASSWORD=delayedarray -p 8787:8787 -d --rm petehaitch/bioc2020_delayedarray_workshop. Use -v $(pwd):/home/rstudio argument to map your local directory to the container.
• Log in to RStudio at http://localhost:8787⁠ using username rstudio and password yourpassword. Note that on Windows you need to provide your localhost IP address like http://191.163.92.108:8787/ - find it using docker-machine ip default in Docker’s terminal.
• Run browseVignettes(package = "DelayedArrayWorkshop"). Click on one of the links, “HTML”, “source”, “R code”.
  • In case of The requested page was not found error, add help/ to the URL right after the hostname, e.g., http://localhost:8787/help/library/DelayedArrayWorkshop/doc/Effectively_using_the_DelayedArray_framework_for_users.html⁠. This is a known bug⁠.