Lecture notes. Written from the material of StatOmics Sga21, licensed CC BY-NC-SA 4.0. Notes, not a transcript — reorganised and rewritten, carrying the same licence.

StatOmics Sga21

41 chapters, written from the course's material.

Contents

1. The MSqRob Statistical Model
2. From Peptides to Differential Expression
3. CPTAC Spike-In Case Study
4. A Proteomics Spike-In Case Study
5. Robust Summarization in Proteomics
6. The msqrob2gui Analysis Workflow
7. Interaction Effects and Stage-wise Testing
8. Illumina Next-Generation Sequencing Overview
9. Course Description and Program
10. Course Description
11. Genomics: A Molecular Biology Primer
12. RNA-seq versus Microarray Reproducibility
13. Detecting Lane Effects in RNA-seq
14. Experimental design
15. Mass Spectrometry Basics for Proteomics
16. Multiple Regression with Interactions
17. Import Data and Preprocessing
18. Differential Abundance Testing and Design
19. Proteomics Hypothesis Testing and Design
20. Preprocessing Quantitative Proteomics Data
21. Label-Free Proteomics Data Preprocessing
22. Peptide-Level Models for Summarization
23. Experimental Design and Blocking
24. Case-Study Datasets for msqrob2
25. Mass Spectrometry-Based Proteomics
26. Recap: The General Linear Model
27. Gene-level quantification
28. Poisson GLMs for Count Data
29. Bulk RNA-seq DE Homework
30. Sequencing Technology and Preprocessing
31. Negative Binomial Model
32. RNA-seq Differential Expression Pipeline
33. Scaling Normalization and Offsets
34. Filtering, Aliasing, and limma-voom
35. Single-Cell RNA-Seq: A First Look
36. Single-Cell QC and Dimensionality Reduction
37. Variance-Stabilizing Transformation for Poisson Counts
38. Proteomics Software Setup
39. Stage-wise Omnibus and Post-hoc Testing
40. Testing for Differential Protein Abundance
41. Mass Spectrometry & Bioinformatics for Proteomics

Sources

The book carries CC BY-NC-SA 4.0.

Offering	Original	Commit	Licence
StatOmics Sga21	github.com/statOmics/SGA21	0ad787d	CC BY-NC-SA 4.0