for Charité MD / MD/PhD / PhD students, postdocs and interested scientific staff
Dates: 22.09.2025 - 26.09.2025 from 9:30 am to 4 pm, on-site (partially CCM, partially CVK)
Description:
This one-week on-site course is intended for pre- and postdoctoral researchers as well as scientific staff members interested in getting started with single-cell RNA-Seq data analysis. The course specifically welcomes participants without any prior programming experience and includes introductory elements to python or R as well as dedicated time to set up the required environments on participants’ laptops. During the course, we will cover the following topics:
Participants will be provided with practice data sets and time will be allocated to hands-on analysis of these data sets. There is also an option to work on own data towards the end of the course (if available; own data is not a participation requirement).
Requirements:
Participants need to be available on all course days and bring a laptop on which you have user permissions to install software (specifically, this might not be the case for some centrally administrated Charité devices). The course will be held in English.
Registration:
To register for the course, please send the following information to lisa.buchauer@charite.de by 31st of August 2025:
The course is limited to 16 participants and spots will be distributed on a first-come-first-serve basis.
Admitted students will receive room information. Please see also the listing in the list of doctoral courses for information on awarded ECTS (Charité doctoral students only).
| Journal Club: Data Analysis and Computational Methods in Immunology |
|---|
| for Charité M.Sc. / MD / MD/PhD / PhD students, postdocs and interested staff |
| ongoing throughout the year, biweekly on Thursdays at 2pm, Südring 2 (CVK) and on MS Teams |
| Participants present current publications covering computational methods and data analysis approaches in immunology and infectious diseases (e.g. omics data, flow cytometry data, clinical data). Papers with biomedical rather than computational focus can also be presented if they contain interesting data analysis concepts. Each participant presents a paper of their choice in a presentation of ~45 minutes which should also include relevant background and participants’ own evaluation of the work. |
| Requirements: None, anyone interested in the topic is welcome to join |
| Registration: Please send an email to lisa.buchauer@charite.de and you will receive exact dates as well as an MS Teams link. Please see also the listing in the list of doctoral courses for information on awarded ECTS (Charité doctoral students only). |
for Charité MD / MD/PhD / PhD students and postdocs
Last occurence: 28.04.2025, 05.05.2025, 19.05.2025, 26.05.2025, 02.06.2025, Monday from 15:00-17:00 (this is the correct time, the Charité Intranet time is incorrect) on MS Teams
Description:
The seminar is intended for pre- and postdoctoral researchers analysing single-cell omics data (scRNA-Seq, scVDJ-Seq, CITE-Seq etc.) as part of their research projects who are facing challenges or uncertainties during the process. It specifically welcomes those without a formal background in bioinformatics who are analysing single cell data with the help of online tutorials, possibly for the first time, and want to touch base on their analysis choices. The course consists of 5 units which are divded into (i) a lecture part providing background on specific parts of single-cell data analysis and (ii) a roundtable part during which participants present their projects with a focus on current challenges and questions. Participants will receive input from the instructor as well as suggestions from their peers. The lecture part covers the following topics:
1) Introduction to single cell transcriptomics data
2) Quality control
3) Dimensionality reduction, clsutering & visualization of high-dimensional data
4) Batch correction methods for dataset combination (integration)
5) Cell type annotation and differential abundance and gene expression analysis.
Requirements:
Participants need to have a single cell omics data set that has been processed to the count matrix stage (i.e. cell by gene matrix, not raw data such as FASTQ files) at the beginning of the seminar and need to be actively working on the analysis of that data.