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Analyze single-cell transcriptomics data using Python and machine learning techniques to uncover novel disease mechanisms.
Single-cell genomics is revolutionizing computational biology and drug development by providing detailed insights into cellular functions. Increasingly large mountains of transcriptomic data have been generated over the past few years, and its secrets are waiting to be unlocked!
In this Build Project, you'll assume the role of a Computational Biologist, analyzing single-cell transcriptomics data to identify novel disease mechanisms. Under the guidance of an industry expert, you will retrieve, clean, and process omics data, apply machine learning models for data analysis, and produce scientific figures. You will gain hands-on experience with tools and methodologies commonly used in the field, all within a simulated professional environment.
Interactive session to set up the development environment and introduce project expectations.
Learn to find and preprocess single-cell genomics data. Will guide you through locating datasets, cleaning, and preprocessing.
Reduce data complexity and visualize clusters and trends using dimensionality reduction methods.
Identify cell populations and annotate cell types. Use clustering algorithms and marker genes for annotation.
Quantify cell type proportions across conditions. Perform compositional analysis to understand tissue composition.
Identify and correct batch effects in data. You will learng the basics of the underlying machine learning methods, including Variational Autoencoders.
Identify genes affected by conditions using statistical tests, which you will learn.
You will present your findings and reflect on your learnings throughout this project.
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