This course offers an introduction to various types of data and analysis techniques which are typically used in the life sciences (e.g. omics technologies). The goal is to get a deeper understanding of advanced concepts and data analytical methods in the area of life sciences.
The focus will be on the following topics:
- acquisition and pre-processing of data from the area of life sciences,
- explorative analysis techniques,
- concepts and tools for reproducible research,
- theory and practice of methods and models for the analysis of data from the life sciences (statistical inference, regression models, methods of machine learning),
- introduction to methods of big data analysis.
After successful completion of this course, participants are able to evaluate, plan and conduct investigations in the life sciences using common methods.
News
Feedback form for final project presentations:
Schedule
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Mondays |
Wednesdays |
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Date |
Presenter |
Topic |
Date |
Presenter |
Topic |
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Apr 17 |
Jahn |
Introduction |
Apr 19 |
Jahn |
Statistical Data Analysis 1 Worksheet 1 |
|
Apr 24 |
Jahn |
Statistical Data Analysis 2 |
Apr 26 |
Jahn |
Statistical Data Analysis 3 Worksheet 2 |
|
May 1 |
|
Holiday |
May 3 |
Jahn |
Unsupervised Learning Worksheet 3 Tidyverse, plotly, pathlib |
|
May 8 |
10 |
Intro to Bioconductor |
May 10 |
Jahn |
Linear Regression Worksheet 4 Ggally, ggvis, shiny |
|
2 |
The ENCODE Project |
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May 15 |
|
|
May 17 |
Jahn |
Multiple Regression Worksheet 5 Caret, seaborn, pandas |
|
3 |
Visualization of genome scale data |
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May 22 |
5 |
Genomic Annotation with Bioconductor |
May 24 |
Jahn |
Classification Worksheet 6 Reticulate, |
|
7 |
Genome-scale hypothesis testing with Bioconductor |
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|
May 29 |
|
Holiday |
May 31 |
8 |
Case-Study: RNA-Seq |
|
4 |
|
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Jun 5 |
9 |
Multi-omics data integration |
Jun 7 |
all |
Project Pitches |
|
1 |
Case-study: scRNA-Seq |
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Jun 12 |
4
|
Case-study: Chip-Seq |
Jun 14 |
Jahn |
Batch effects Scikitlearn, plotnine, rmysql |
| numpy | |||||
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Jun 19 |
|
Model Selection and Regularization rmysql |
Jun 21 |
Jahn |
Tree-Based Methods Tensorflow, Keras |
| Project meetings: 4,7,8,6 | |||||
|
Jun 26 |
|
Project presentations: 3, 7, 8 |
Jun 28 |
Jahn |
SVM PyTorch |
| Project meetings: 10 |
Project Presentations: 6, 9 (if time permits) Project meetings: 2 |
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|
Jul 3 |
|
Project presentations: 10, 4, 5, 2, 1 |
Jul 5 |
Jahn |
Survival Analysis and Censored Data Cython |
| Project meetings: 4,1, |
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| Project meetings: 3, 9 | |||||
|
Jul 10 |
|
Project meetings: 6, 3, 7, |
Jul 12 |
Jahn
|
Project meeting: 10, 8, 9 (if time permits) |
|
Jul 17 |
|
Final presentations: 7, 9, 4, 5, 8 |
Jul 19 |
|
Final presentations: 3, 10, 6, 2, 1 |
Tech talk presentations
If you do not yet have a topic for the tech talk, or if you have a topic, but no presentation date, contact the lecturer. Presentation dates are given in the schedule above.
Guidelines:
- Each topic presented by 1 to 2 people
- Duration: 8-10 mins per person
- If package is complex, focus on most useful features for beginners
- Presentation can be based on R/Jupyter notebook instead of slides
- Provide a ”Cheat sheet” with most important info/commands to get started
|
ggvis |
Kurnaz |
|
ggally |
Sielatchom Foyang |
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reticulate |
Nepal |
|
shiny |
Woeller, Fischer |
|
caret |
Ma, Ngu |
|
seaborn |
Bendikova |
|
RMySQL |
Rajan, Kaushik |
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TensorFlow |
Dieser |
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Pandas |
Rieger |
|
SciPy |
Brenningmeyer, Hein |
|
Keras |
Khachatryan |
|
PyTorch |
Sotelo, Junghans |
|
Scikit-Learn |
Ashraf, Pham |
|
Numpy |
Wang, Djuhadi |
|
plotNine |
Herzler |
|
plotly |
Mammadli, Hamidovic |
|
Ninja |
|
|
tidyverse |
Harlos |
|
Cython |
Eckhoff |
|
pathlib |
Otto |