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We’ll drop resources that we find
that might be useful to others.
Coding and Single cell crash course
From a single cell and spatial analysis course by Cold Spring Harbour
Laboratory, these resources are great for both total beginners to coding
and analysis to those starting to analyse their own data.
This is the Basics
of Linux/Shell and R where you how to navigate through your Terminal
and the start of your R journey.
Processing
scRNA and Visium HD can be daunting but this breaks down all the
steps you need to clean, cluster, and visualise your data, as well as
the theory behind it.
The purpose of the Sydney Precision Data Science Centre is to engage
in high quality and transformational multidisciplinary research and to
provide a research hub to all interdisciplinary researchers in
data-intensive science.
We generate innovative, fit-for-purpose applied analytical
methodologies that are critical to knowledge discovery through the deep
integration of various quantitative disciplines and research
paradigms.
Computational Genomics with R is an exceptionally comprehensive
resource for biomedical bioinformatics. It covers a broad range of
topics—statistics, machine learning, sequencing data processing, and
more—while providing both code examples and clear explanations. Although
it may not be the most accessible starting point for wet-lab
researchers, it serves as an invaluable reference for deeper
explorations of bioinformatic analyses.
10X Analysis Resources
10X Genomics are a unique company, in that they provide a
lot of supporting materials. In the drop down below, we have listed
a series of resources that 10X have provided, including how-to videos
and tutorials.
Stretchly is an open-source app designed to
encourage healthy work habits by prompting regular short (30-second) and
long (20-minute) breaks. I’ve found it invaluable for maintaining focus
and preventing burnout. It’s highly customizable, allowing you to tailor
prompts to your needs.
roadmap.sh provides structured learning pathways for
various tech-related skills, from Data Science to DevOps. The Data
Science and AI roadmap outlines essential topics such as mathematics,
statistics, and coding, along with curated free and paid learning
resources. These roadmaps are community-driven and frequently updated,
making them a great guide for self-paced learning.
LinkedIn Learning offers a vast library of online
courses covering data analysis, programming (including R and Python),
statistics, research skills, and professional development. There is a
short R
for Data Science course that is quite nice.
You should have access to LinkedIn Learning through your University
email. Otherwise, WIMR staff can apply for a license through WIMR.
Sydney Informatics Hub
Sydney Informatics Hub offers training
workshops regularly on research data management, statistical
methods, and high-performance computing. These are more general methods
that not only apply to biomedical research, but may provide inspiration
or good fundamental knowledge on the analytical methods we use.
Interactive R learning UI
learnr is a package that you
can install which provides a nice little UI if you want to practice
basicR skills and prefer some interactivity.
ggplot2 book
ggplot2:Elegant
Graphics for Data Analysis is a nice, comprehensive handbook for
graphing using ggplot2. It covers the basic layout, different graph
types, customizing elements such as colours, group overlays,
annotations, etc.
