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Authors: Harrison, Paul (orcid: 0000...  or The Carpentries 

WORKSHOP: Single cell RNAseq analysis in R

This record includes training materials associated with the Australian BioCommons workshop 'Single cell RNAseq analysis in R'. This workshop took place over two, 3.5 hour sessions on 26 and 27 October 2023.Event descriptionAnalysis and interpretation of single cell RNAseq (scRNAseq) data requires...

Keywords: bioinformatics, transcriptomics, single cell RNA-seq, Seurat, R statistical software

WORKSHOP: Single cell RNAseq analysis in R https://dresa.org.au/materials/workshop-single-cell-rnaseq-analysis-in-r-6a1126cf-7105-43ec-bf55-7c492f758301 This record includes training materials associated with the Australian BioCommons workshop 'Single cell RNAseq analysis in R'. This workshop took place over two, 3.5 hour sessions on 26 and 27 October 2023.Event descriptionAnalysis and interpretation of single cell RNAseq (scRNAseq) data requires dedicated workflows. In this hands-on workshop we will show you how to perform single cell analysis using Seurat - an R package for QC, analysis, and exploration of single-cell RNAseq data. We will discuss the 'why' behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers. We will also explore various ways of visualising single cell expression data.This workshop is presented by the Australian BioCommons, Queensland Cyber Infrastructure Foundation (QCIF) and the Monash Genomics and Bioinformatics Platform with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative.Lead trainers: Sarah Williams, Adele Barugahare, Paul Harrison, Laura Perlaza JimenezFacilitators: Nick Matigan, Valentine Murigneux, Magdalena (Magda) AntczakInfrastructure provision: Uwe WinterCoordinator: Melissa BurkeTraining materialsMaterials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event.Files and materials included in this record:Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc.Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file.scRNAseq_Schedule (PDF): A breakdown of the topics and timings for the workshopMaterials shared elsewhere:This workshop follows the tutorial 'scRNAseq Analysis in R with Seurat'https://swbioinf.github.io/scRNAseqInR_Doco/index.htmlSlides used to introduce key topics are available via GitHubhttps://github.com/swbioinf/scRNAseqInR_Doco/tree/main/slidesThis material is based on the introductory Guided Clustering Tutorial tutorial from Seurat.It is also drawing from a similar workshop held by Monash Bioinformatics Platform Single-Cell-Workshop, with material here.   Melissa Burke (melissa@biocommons.org.au) bioinformatics, transcriptomics, single cell RNA-seq, Seurat, R statistical software
WORKSHOP: Single cell RNAseq analysis in R

This record includes training materials associated with the Australian BioCommons workshop ‘Single cell RNAseq analysis in R’. This workshop took place over two, 3.5 hour sessions on 22 and 3 August 2022.

Event description

Analysis and interpretation of single cell RNAseq (scRNAseq) data...

Keywords: Bioinformatics, Analysis, Transcriptomics, R software, Single cell RNAseq, scRNAseq

WORKSHOP: Single cell RNAseq analysis in R https://dresa.org.au/materials/workshop-single-cell-rnaseq-analysis-in-r-4f60b82d-2f1e-4021-9569-6955878dd945 This record includes training materials associated with the Australian BioCommons workshop ‘Single cell RNAseq analysis in R’. This workshop took place over two, 3.5 hour sessions on 22 and 3 August 2022. Event description Analysis and interpretation of single cell RNAseq (scRNAseq) data requires dedicated workflows. In this hands-on workshop we will show you how to perform single cell analysis using Seurat - an R package for QC, analysis, and exploration of single-cell RNAseq data.  We will discuss the ‘why’ behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers. We will also explore various ways of visualising single cell expression data. This workshop is presented by the Australian BioCommons and Queensland Cyber Infrastructure Foundation (QCIF) with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative.   Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. scRNAseq_Slides (PDF): Slides used to introduce topics scRNAseq_Schedule (PDF): A breakdown of the topics and timings for the workshop scRNAseq_Resources (PDF): A list of resources recommended by trainers and participants scRNAseq_QandA(PDF): Archive of questions and their answers from the workshop Slack Channel.   Materials shared elsewhere: This workshop follows the tutorial ‘scRNAseq Analysis in R with Seurat’ https://swbioinf.github.io/scRNAseqInR_Doco/index.html This material is based on the introductory Guided Clustering Tutorial tutorial from Seurat. It is also drawing from a similar workshop held by Monash Bioinformatics Platform Single-Cell-Workshop, with material here. Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Transcriptomics, R software, Single cell RNAseq, scRNAseq
WORKSHOP: Working with genomics sequences and features in R with Bioconductor

This record includes training materials associated with the Australian BioCommons workshop ‘Working with genomics sequences and features in R with Bioconductor’. This workshop took place on 23 September 2021.

Workshop description

Explore the many useful functions that the Bioconductor...

Keywords: R software, Bioconductor, Bioinformatics, Analysis, Genomics, Sequence analysis

WORKSHOP: Working with genomics sequences and features in R with Bioconductor https://dresa.org.au/materials/workshop-working-with-genomics-sequences-and-features-in-r-with-bioconductor-8399bf0d-1e9e-48f3-a840-3f70f23254bb This record includes training materials associated with the Australian BioCommons workshop ‘Working with genomics sequences and features in R with Bioconductor’. This workshop took place on 23 September 2021. Workshop description Explore the many useful functions that the Bioconductor environment offers for working with genomic data and other biological sequences.  DNA and proteins are often represented as files containing strings of nucleic acids or amino acids. They are associated with text files that provide additional contextual information such as genome annotations. This workshop provides hands-on experience with tools, software and packages available in R via Bioconductor for manipulating, exploring and extracting information from biological sequences and annotation files. We will look at tools for working with some commonly used file formats including FASTA, GFF3, GTF, methods for identifying regions of interest, and easy methods for obtaining data packages such as genome assemblies.  This workshop is presented by the Australian BioCommons and Monash Bioinformatics Platform with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Schedule (PDF): schedule for the workshop providing a breakdown of topics and timings   Materials shared elsewhere: This workshop follows the tutorial ‘Working with DNA sequences and features in R with Bioconductor - version 2’ developed for Monash Bioinformatics Platform and Monash Data Fluency by Paul Harrison. https://monashdatafluency.github.io/r-bioc-2/ Melissa Burke (melissa@biocommons.org.au) R software, Bioconductor, Bioinformatics, Analysis, Genomics, Sequence analysis
Databases and SQL

A relational database is an extremely efficient, fast and widespread means of storing structured data, and Structured Query Language (SQL) is the standard means for reading from and writing to databases. Databases use multiple tables, linked by well-defined relationships, to store large amounts...

Keywords: SQL

Databases and SQL https://dresa.org.au/materials/databases-and-sql A relational database is an extremely efficient, fast and widespread means of storing structured data, and Structured Query Language (SQL) is the standard means for reading from and writing to databases. Databases use multiple tables, linked by well-defined relationships, to store large amounts of data without needless repetition while maintaining the integrity of your data. Moving from spreadsheets and text documents to a structured relational database can be a steep learning curve, but one that will reward you many times over in speed, efficiency and power. Developed using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. Understand and compose a query using SQL Use the SQL syntax to select, sort and filter data Calculate new values from existing data Aggregate data into sums, averages, and other operations Combine data from multiple tables Design and build your own relational databases The course has no prerequisites. training@intersect.org.au SQL
Unix Shell and Command Line Basics

The Unix environment is incredibly powerful but quite daunting to the newcomer. Command line confidence unlocks powerful computing resources beyond the desktop, including virtual machines and High Performance Computing. It enables repetitive tasks to be automated. And it comes with a swag of...

Keywords: Unix

Unix Shell and Command Line Basics https://dresa.org.au/materials/unix-shell-and-command-line-basics The Unix environment is incredibly powerful but quite daunting to the newcomer. Command line confidence unlocks powerful computing resources beyond the desktop, including virtual machines and High Performance Computing. It enables repetitive tasks to be automated. And it comes with a swag of handy tools that can be combined in powerful ways. Getting started is the hardest part, but our helpful instructors are there to demystify Unix as you get to work running programs and writing scripts on the command line. Every attendee is given a dedicated training environment for the duration of the workshop, with all software and data fully loaded and ready to run. We teach this course within a GNU/Linux environment. This is best characterised as a Unix-like environment. We teach how to run commands within the Bash Shell. The skills you’ll learn at this course are generally transferable to other Unix environments. Navigate and work with files and directories (folders) Use a selection of essential tools Combine data and tools to build a processing workflow Automate repetitive analysis using the command line The course has no prerequisites. training@intersect.org.au Unix
Data Manipulation in R

R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

In this workshop, you will learn how to manipulate, explore and get insights...

Keywords: R

Data Manipulation in R https://dresa.org.au/materials/data-manipulation-in-r R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. In this workshop, you will learn how to manipulate, explore and get insights from your data (Data Manipulation using the dplyr package), as well as how to convert your data from one format to another (Data Transformation using the tidyr package). We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation. DataFrame Manipulation using the dplyr package DataFrame Transformation using the tidyr package Either \Learn to Program: R\ or \Learn to Program: R\ and \R for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: R\ and \R for Research\ courses to ensure that you are familiar with the knowledge needed for this course. training@intersect.org.au R
Learn to Program: R

R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.

But getting started with R can be...

Keywords: R

Learn to Program: R https://dresa.org.au/materials/learn-to-program-r R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this introductory course comes in. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. Introduction to the RStudio interface for programming Basic syntax and data types in R How to load external data into R Creating functions (FUNCTIONS) Repeating actions and analysing multiple data sets (LOOPS) Making choices (IF STATEMENTS – CONDITIONALS) Ways to visualise data in R No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found \here\. training@intersect.org.au R
Python for Research

Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data.

This workshop is an introduction to data structures (DataFrames...

Keywords: Python

Python for Research https://dresa.org.au/materials/python-for-research Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data. This workshop is an introduction to data structures (DataFrames using the pandas library) and visualisation (using the matplotlib library) in Python. The targeted audience for this workshop is researchers who are already familiar with the basic concepts in programming such as loops, functions, and conditionals. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. Introduction to Libraries and Built-in Functions in Python Introduction to DataFrames using the pandas library Reading and writing data in DataFrames Selecting values in DataFrames Quick introduction to Plotting using the matplotlib library \Learn to Program: Python\ or any of the \Learn to Program: R\, \Learn to Program: MATLAB\ or \Learn to Program: Julia\, needed to attend this course. If you already have some experience with programming, please check the topics covered in the \Learn to Program: Python\ course to ensure that you are familiar with the knowledge needed for this course. training@intersect.org.au Python
Data Manipulation in Python

Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data.

In this workshop, you will explore DataFrames in depth (using...

Keywords: Python

Data Manipulation in Python https://dresa.org.au/materials/data-manipulation-in-python Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data. In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. Working with pandas DataFrames Indexing, slicing and subsetting in pandas DataFrames Missing data values Combine multiple pandas DataFrames Either \Learn to Program: Python\ or \Learn to Program: Python\ and \Python for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: Python\ and \Python for Research\ courses to ensure that you are familiar with the knowledge needed for this course. training@intersect.org.au Python
Version Control with Git

Have you mistakenly overwritten programs or data and want to learn techniques to avoid repeating the loss? Version control systems are one of the most powerful tools available for avoiding data loss and enabling reproducible research. While the learning curve can be steep, our trainers are there...

Keywords: Git

Version Control with Git https://dresa.org.au/materials/version-control-with-git Have you mistakenly overwritten programs or data and want to learn techniques to avoid repeating the loss? Version control systems are one of the most powerful tools available for avoiding data loss and enabling reproducible research. While the learning curve can be steep, our trainers are there to answer all your questions while you gain hands on experience in using Git, one of the most popular version control systems available. Join us for this workshop where we cover the fundamentals of version control using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. keep versions of data, scripts, and other files examine commit logs to find which files were changed when restore earlier versions of files compare changes between versions of a file push your versioned files to a remote location, for backup and to facilitate collaboration The course has no prerequisites. training@intersect.org.au Git
R for Social Scientists

R is quickly gaining popularity as a programming language of choice for researchers. It has an excellent ecosystem including the powerful RStudio development environment.

But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this...

Keywords: R

R for Social Scientists https://dresa.org.au/materials/r-for-social-scientists R is quickly gaining popularity as a programming language of choice for researchers. It has an excellent ecosystem including the powerful RStudio development environment. But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this introductory course comes in. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Data Carpentry. Basic syntax and data types in R RStudio interface How to import CSV files into R The structure of data frames A brief introduction to data wrangling and data transformation How to calculate summary statistics A brief introduction to visualise data No prior experience with programming needed to attend this course. training@intersect.org.au R
R for Research

R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.

This workshop is an introduction to data...

Keywords: R

R for Research https://dresa.org.au/materials/r-for-research R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. This workshop is an introduction to data structures (DataFrames) and visualisation (using the ggplot2 package) in R. The targeted audience for this workshop is researchers who are already familiar with the basic concepts in programming such as loops, functions, and conditionals. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. Project Management with RStudio Introduction to Data Structures in R Introduction to DataFrames in R Selecting values in DataFrames Quick introduction to Plotting using the ggplot2 package \Learn to Program: R\ or any of the \Learn to Program: Python\, \Learn to Program: MATLAB\, \Learn to Program: Julia\, needed to attend this course. If you already have some experience with programming, please check the topics covered in the \Learn to Program: R\ course to ensure that you are familiar with the knowledge needed for this course. training@intersect.org.au R
Learn to Program: MATLAB

MATLAB is an incredibly powerful programming environment with a rich set of analysis toolkits. But what if you’re just getting started – with MATLAB and, more generally, with programming?

Nothing beats a hands-on, face-to-face training session to get you past the inevitable syntax errors! ...

Keywords: Matlab

Learn to Program: MATLAB https://dresa.org.au/materials/learn-to-program-matlab MATLAB is an incredibly powerful programming environment with a rich set of analysis toolkits. But what if you’re just getting started – with MATLAB and, more generally, with programming? Nothing beats a hands-on, face-to-face training session to get you past the inevitable syntax errors! So join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. Introduction to the MATLAB interface for programming Basic syntax and data types in MATLAB How to load external data into MATLAB Creating functions (FUNCTIONS) Repeating actions and analysing multiple data sets (LOOPS) Making choices (IF STATEMENTS – CONDITIONALS) Ways to visualise data in MATLAB In order to participate, attendees must have a licensed copy of MATLAB installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found \here\. training@intersect.org.au Matlab