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:
After successful completion of this course, participants are able to evaluate, plan and conduct investigations in the life sciences using common methods.
Feedback form for final project presentations:
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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 |
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Apr 24 |
Jahn |
Statistical Data Analysis 2 |
Apr 26 |
Jahn |
Statistical Data Analysis 3 Worksheet 2 |
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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 |
|
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May 17 |
Jahn |
Multiple Regression Worksheet 5 Caret, seaborn, pandas |
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3 |
Visualization of genome scale data |
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May 22 |
5 |
Genomic Annotation with Bioconductor |
May 24 |
Jahn |
Classification Worksheet 6 Reticulate, |
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7 |
Genome-scale hypothesis testing with Bioconductor |
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May 29 |
|
Holiday |
May 31 |
8 |
Case-Study: RNA-Seq |
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4 |
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Jun 5 |
9 |
Multi-omics data integration |
Jun 7 |
all |
Project Pitches |
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1 |
Case-study: scRNA-Seq |
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Jun 12 |
4
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Case-study: Chip-Seq |
Jun 14 |
Jahn |
Batch effects Scikitlearn, plotnine, rmysql |
| numpy | |||||
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Jun 19 |
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Model Selection and Regularization rmysql |
Jun 21 |
Jahn |
Tree-Based Methods Tensorflow, Keras |
| Project meetings: 4,7,8,6 | |||||
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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 |
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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 | |||||
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Jul 10 |
|
Project meetings: 6, 3, 7, |
Jul 12 |
Jahn
|
Project meeting: 10, 8, 9 (if time permits) |
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Jul 17 |
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Final presentations: 7, 9, 4, 5, 8 |
Jul 19 |
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Final presentations: 3, 10, 6, 2, 1 |
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
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ggvis |
Kurnaz |
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ggally |
Sielatchom Foyang |
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reticulate |
Nepal |
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shiny |
Woeller, Fischer |
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caret |
Ma, Ngu |
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seaborn |
Bendikova |
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RMySQL |
Rajan, Kaushik |
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TensorFlow |
Dieser |
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Pandas |
Rieger |
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SciPy |
Brenningmeyer, Hein |
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Keras |
Khachatryan |
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PyTorch |
Sotelo, Junghans |
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Scikit-Learn |
Ashraf, Pham |
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Numpy |
Wang, Djuhadi |
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plotNine |
Herzler |
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plotly |
Mammadli, Hamidovic |
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Ninja |
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tidyverse |
Harlos |
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Cython |
Eckhoff |
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pathlib |
Otto |
| Course No | Course Type | Hours |
|---|---|---|
| 19405612 | Projektseminar | 4 |
| 19405606 | Seminaristischer Unterricht | 4 |
| Time Span | 17.04.2023 - 25.07.2023 |
|---|---|
| Instructors |
Maryam Ghareghani
Katharina Jahn
|
| 0262c_MA120 | 2019 (ÄO 2021), MA Bioninformatik (Mono), 120 LP |
| 0590a_MA120 | 2019, MSc Data Science, 120 LP |
| 0590b_MA120 | 2021, MSc Data Science, 120 LP |
| Day | Time | Location | Details |
|---|---|---|---|
| Monday | 10-14 | T9/SR 005 Übungsraum | 2023-04-17 - 2023-07-17 |
| Wednesday | 10-14 | T9/SR 006 Seminarraum | 2023-04-19 - 2023-07-19 |