Learning path topics
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Module 2: FAIR Pointers
This learning path aims to teach you the basics FAIR data and signpost to other useful learning materials and resources. You will learn FAIR from the perspective of the 15 FAIR Principles published in 2016. You will learn about FAIR, its origins and the FAIR Principles using real examples of FAIR...
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Module 2: Generating cluster plots
These tutorials take you from the pre-processed matrix to cluster plots and gene expression values. You can pick whether to follow the Scanpy or Seurat tutorials - they will accomplish the same thing and generate the same results, so follow whichever you prefer!
Time estimation: 6...
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Module 2: Machine Learning using R
Having some foundational understanding of how to code in R, this module will provide initially an overview of the different types of Machine Learning, and then will provide some practical, hands-on examples of creating ML models.
Time estimation: 3 hours
Learning objectives:
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Module 2: Theory of Single-Cell RNA-seq
When analysing sequencing data, you should always start with a quality control step to clean your data and make sure your data is good enough to answer your research question. After this step, you will often proceed with a mapping (alignment) or genome assembly step, depending on whether you have...
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Module 3: Inferring trajectories
This isn’t strictly necessary, but if you want to infer trajectories - pseudotime relationships between cells - you can try out these tutorials with the same dataset. Again, you get two options for inferring trajectories, and you can choose either.
Time estimation: 5 hours
Learning...
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Module 3: Time to analyse data!
It’s time to apply your skills! You’ll now analyse some clean data from the 10X Chromium platform.
Time estimation: 9 hours
Learning objectives:
- Demultiplex single-cell FASTQ data from 10X Genomics
- Learn about transparent matrix formats
- Understand the importance of high and low...
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Module 3: Training techniques to enhance learner participation and engagement
This module covers the following questions:
- What does make a training effective?
- How can instructors enhance learner participation and engagement?
At the end of this module, learners should be able to:
- Describe what makes training effective
- Describe what makes a trainer...
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Module 4: Motivation and Demotivation
This module covers the following questions:
What is motivation and demotivation?
How do motivation and demotivation impact learning processes?
What can instructors do to motivate learners?
At the end of this module, learners should be able to:List factors of motivation and...
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Module 4: Moving into coding environments
Did you know Galaxy can host coding environments? They don’t have the same level of computational power as the easy-to-use Galaxy tools, but you can unlock the full freedom in your data analysis. You can install your favourite single-cell tool suite that is not available on Galaxy, export your...
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Module 5: Assessment and feedback in training and teachings
This module covers the following questions:
- What are the different types of feedback?
- When do get and receive feedback?
- For which purpose do we need feedback?
At the end of this module, learners should be able to:
- Describe the differences between formative and summative...