Single-cell Transcriptomics
Titel: Analysis of single-cell RNA sequencing data
Dozenten: Helene Kretzmer
Maximale Teilnehmerzahl: 9
Zeitraum/Vorbesprechungstermin: 01.03. - 24.04.2023
Am 11. Januar 2023 findet um 9 Uhr ein Infotermin statt.https://us04web.zoom.us/j/6588496485?pwd=ZTZubUUweldLNW9EQjVMT1RCTFVrdz09
Meeting ID: 658 849 6485
Passcode: Yi40Fn
Ort: Max Planck Institute for Molecular Genetics, Ihnestr. 63-73, PC-Pool
Kurze inhaltliche Beschreibung:
Gene expression is the process by which a cell produces proteins from the genetic information of the DNA. While all cells in a body have (almost) identical DNA, the gene expression varies between cells and specifically also along differentiation. During embryonic development, a single cell, the oocyte, starts to divide and already after a few days, cells start to acquire cell identities that can be distinguished on transcriptomic level. One of the research aims is to understand this heterogeneity inside the emerging embryo and how which genes are relevant for proper development. Single-cell transcriptomics is a novel technique that allows the measurement of gene expression for individual cells. These measurements allow us to identify cell types or developmental trajectories which describe how cells of one type become another.
In our course, we will cover the whole bioinformatical pipeline for single-cell transcriptomics, including read processing, quality control, and gene-expression matrix construction and analysis. Additionally, we will use unpublished single-cell gene-perturbation data to evaluate the impact of a single gene on embryonic development.
For this, we will use state-of-the-art tools, such as Cell Ranger and Seurat (R-based), or scanpy (python-based). Experience in programming languages, such as shell scripts, R (or Python) is advantageous. For the advanced data analysis, the students will work with a wide range of data-analysis techniques, for example, modularity-based clustering algorithms, dimensionality-reduction techniques (PCA, t-SNE, and UMAP), differential expression identification, gene-ontology enrichment, and trajectory analysis. Students will work in groups of 3 throughout the course. The final report will be written in form of a brief scientific paper and finally all groups will give a short talk to present their results.
Quantitative Aufteilung:
Praktische Programmierarbeit: ca. 60 %
Soft Skills: ca. 40 %
Verwendete Programmiersprache(n): präferiert R, alternativ python, bash/shell
Schwierigkeitsgrad (Acht Sterne verteilt auf drei Bereiche):
Programmieren ****
Biologie/Chemie **
Projektmanagement **
Unbedingt erforderliche Vorkenntnisse: Statistische Grundlagen, Modul "Algorithmische Bioinformatik"
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