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Day 1, 8:00-16:00, Python
Easy introduction, light tasks.
- What is Python?
- Stats, strengths and weaknesses
- Past, present and future of Python
- As a new "pythonista", who will be your friends?
- How to make Python work for this course
- Python distributions, Anaconda
- Jupyter and interactive notekeeping
- Installing libraries
- Python consoles, interpreters and editors
- Python tutorial
- Basics: Math, Variables, Functions, Control flow, Modules
- Data representation: String, Tuple, List, Set, Dictionary, Objects and Classes
- Standard library modules: script arguments, file operations, timing, processes, forks, multiprocessing
- Text manipulation:
- File IO, streaming, serialization
- Parsing and regular expressions
- XML, HTML editing
- Python and the web
- Introduction to Django
- SQL interogation
- Remote API calls (Entrez, BioBank)
- Python and other languages
- Python and C: Mutual Information
- Python and R: microarray processing
Day 2, 8:00-16:00, Data science
Intensive in math, slightly harder tasks to accomplish in class.
- Visualization:
- Standard plots with matplotlib: line, scatter, chart
- Web publishing with plotly: heatmap example
- Network display with graphviz
- GUI programming with wxpython
- Statistics:
- Dataframing with pandas
- scipy: anova, linear regression, curve fitting
- Statistical enrichment analysis
- Scientific computing:
- Numpy: advanced array operations
- Scipy introduction: from linear algebra to image analisys
- Simpy: symbolic math
- Machine learning:
- scikit-learn: clustering
- Handling multivariate data: PCA and PLS regression
- Networks:
- networkx: centrality computation
- Network IO
- Presentation of Omics
- Omics tasks of day 3 are presented and discussed.
Day 3, 8:00-16:00, 'Omics
I setup the problems and describe the tasks, and give you some helper code to start with, and you will solve them in class, in the order of your choosing. I will tend to guide rather than tell. I will only give you the solved problems after the course. What you choose to do is up to you, but I reccomend that you stick to one task until you finish it.
- Sequencing:
- Making a command pipeline
- Run a RNA-Seq task on an Amazon cloud.
- Manipulate sequences in BioPython
- Regulomics
- Gather promoter regions
- Collect TFBS, manipulate motif logos
- Reconstruct a regulatory network
- Gene Expression
- Download a GEO dataset and prepare it
- Cluster the genes based on their expression
- Compute a co-expression network
- Compute differential gene expression for a set of samples.
- Proteomics
- Compute a protein similarity graph, cluster enrichment study
- Perform structural alignment and plots with PyMol
- Metabolomics
- Metabolic pathway assembly and display
- Flux balance analysis
- Population genetics and philogeny
- Run a small scale coalescent simulation
- Compute a philogeny tree and display it