This course provides a rigorous foundation in the statistical analysis of Bulk RNA-Seq data, the cornerstone of modern transcriptomics. While single-cell analysis offers resolution, bulk RNA-seq remains the gold standard for high-sensitivity gene discovery, biomarker identification, and population-level studies.
You will learn to navigate the complete transition from raw sequencing reads to biological interpretation, mastering the Bioconductor ecosystem to produce publication-quality results.
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Mastering AnnData: Learn to manipulate the .X, .obs, .var, and .obsm slots that make Python-based single-cell analysis so memory-efficient.
Scalable Preprocessing: Perform quality control, normalization, and log-transformation on massive datasets that would typically crash traditional R environments.
Deep Generative Modeling: Introduction to scvi-tools for probabilistic modeling of technical noise and batch effects.
