Systems Biology is a systemic approach to understand the biological phenomena that occurs inside a cell at the molecular level. This course is an introduction to the theoretical tools that are used to understand the emerging behavior of complex biological networks.
The schedule is Tuesdays at 11:00 and Fridays at 15:00 via Zoom.
We have finished the first part of the course, about gene expression analysis using linear models. Now we will study biological networks. A good overview of the subject is available on the following paper. Please read it before the next class.
Albert, Réka. “Network Inference, Analysis, and Modeling in Systems Biology.” The Plant Cell 19, no. 11 (2007): 3327–38. https://doi.org/10.1105/tpc.107.054700.
Slides used in classes
Online material
Essential
- Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME (2018). “RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR.” F1000Research, 5, 1408. https://f1000research.com/articles/5-1408/v3.
- A
guide to creating design matrices for gene expression experiments
doi:10.18129/B9.bioc.RNAseq123
- R package that supports the F1000Research workflow article on RNA-seq analysis using limma, Glimma and edgeR by Law et al. (2016).
- The Art of Linear Algebra – Graphic Notes on “Linear Algebra for Everyone.” (PDF), (GitHub source), (Blog Entry).
- Hypothesis Test: Difference Between Paired Means. (web page)
Suggested, but not Essential
- The p value and the base rate fallacy. (Statistics Done Wrong website).
- The lady tasting tea: Using experimental methods to introduce inference statistics. (PDF).
- A lady tasting tea and other applications of Categorical Data Analysis. (PDF)>
- You Can Load a Die, But You Cant Bias a Coin. (ResearchGate page).
- A Class Project in Survey Sampling. (ResearchGate page).
- Debunking the p-value with Statistics. (Backyard Brains website).
- Paired Sample T-Test. (Statistics Solutions website).
- Comparing Two Population Means: Paired Data. (Pennsylvania State University).
- RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR (Bioconductor tutorial).
Bibliography
- No Bullshit guide to Linear Algebra by Ivan Savov (Website), (PDF extract).
- OpenIntro Statistics (Fourth Edition) by David Diez, Mine Çetinkaya-Rundelm, and Christopher D. Barr (Free PDF available) (there are other good free books in the same website).
Contact
The forum of the course is at https://groups.google.com/d/forum/iu-systems-biology. You can also participate writing an email to iu-systems-biology@googlegroups.com. Feel free to use it to ask any question or give any answer>.
Topics to be discussed
- Gene expression analysis
- qPCR, micro arrays, RNA-seq
- Public databases
- Statistical analysis of differential expression
- Normalization
- 2-delta-delta
- RMA
- TMM
- Network inference
- Gaussian graphs
- Causality