In this MOOC, we’ll walk through the fundamental steps of single-cell RNA sequencing (scRNA-seq) analysis, using a set of tools specifically designed to ensure both computational and functional reproducibility.
We’ll start with a short introduction to scRNA-seq technologies, focusing on the strengths and limitations of the most widely used platforms. From there, we’ll move step by step through the data analysis workflow.
Along the way, I’ll share real-world examples so you can see firsthand how analyses are carried out in practice. A basic knowledge of R is helpful, but don’t worry, the functions we will use follow a consistent and intuitive structure.
This course is aimed at students, PhD candidates, and early-career researchers in the life sciences who want to make the leap from “I am scared to analyze data” to “I can do this!”.
I hope you will enjoy the journey and perhaps find these tools useful in your own research projects.
The course provides the foundations of data analysis generated using single-cell technologies.
The course is based on 7 modules and exercises:
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Day 1 – Set up computational environment
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Day 2 – Becoming acquainted the computational environment.
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Day 3 – Module 1 Introduction to scRNA-seq, computational environment preparation and exercise
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Day 4 - Module 2 QC for scRNA-seq data, genes annotation, cell filtering and exersise
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Day 5 – Module 3 reducing the complexity of a scRNA-seq dataset and exercise
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Day 6 – Module 4 clustering cells and exercise
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Day 7 – Module 5 clusters specific gene marker detection sand exercise
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Day 8 – Module 6 cell types annotation and exercise
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Day 9 – Module 7 multiple datasets integration and exercise
At the end of the course, students will be able to
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conduct research on matters relating to the genome focused on gene expression
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independently analyze a single-cell RNA-seq experiment,
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assess data quality,
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understanding methods and statistics used to extract content from a dataset
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gather, process and present quantitative data. Use the appropriate programs and methods for validating, organising and interpreting data
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define the parameters required to generate well-resolved and homogeneous cell-type clusters.
Completing the requested exercises.
Listening the lessons, solving the requested exercises
Luca Alessandrì - abc computational reproducibility for life scientist
Luca Alessandrì - Efficient Utilization of ChatGPT for Code Generation in Medical and life Science Education
Raffaele Adolfo Calogero
Per domande o problematiche tecniche relative al corso e alla piattaforma contattare:
edvancedeh@unito.it
Contatto del docente del MOOC: