Workshop: Single Cell Data Analysis With Python
Want to learn how to analyse single-cell data? Good at Python? Listen up!
If you’re a Python coder and completely new to single-cell analysis, then this is for you! Whether you have a single-cell project on the horizon or simply want to expand your bioinformatics toolkit, this workshop will give you a solid foundation.
In this hands-on workshop, we will introduce you to the Python ecosystem for single-cell, explain the analytical considerations for single-cell analysis, and showcase an end-to-end suggested processing workflow. We will explain how and why each step is done so that you leave with a conceptual understanding.
Daniel Snell, Hubert Slawinski from the Crick Genomics Platform will be around too for single cell chats about bench data generation and analysis.
Learning objectives
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Explore and manipulate single-cell data using Scanpy and Anndata.
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Perform QC, data viz and exploratory analysis of single-cell data in Python.
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Annotate cell-types via atlas-based label transfer using Celltypist.
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Understand scvi-tools ecosystem.
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Identify and evaluate key decision points and considerations (eg, cell cycle regression, doublet detection, sample integration).
Who should attend
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You should have 3+ years of Python experience (comfortable with Python (functions, pandas/numpy, debugging independently).
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Prior sc-RNAseq experience NOT required.
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Working Laptop with Python installed.
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A virtual env set up (uv, conda) and ready, if you would like to code along.
What to expect
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Warning: Due to the time limit on this workshop, if you attend without being proficient in Python, you will struggle to keep up.
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There will not be time for debugging basic Python syntax during the workshop.
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The main focus is on demystifying a single-cell analysis workflow using Python as the chosen language.