Modern high-throughput technologies have revolutionized biomedical research by enabling comprehensive molecular profiling of biological systems. Methods like high-throughput sequencing, microarrays, or mass spectrometry are now routinely applied to generate huge multi-omics data sets.
Current research efforts even allow for the molecular characterization of individual cells and as a consequence enable the analysis of biological systems at a resolution previously not reached by traditional bulk experiments. On the one hand, the generation of more and more complex data sets enables researches to gain novel insights into the molecular machinery. On the other hand, this development also entails new requirements and challenges for computational methods.
This seminar covers bioinformatics approaches for the analysis of single cell data sets. Each participant will present a research paper that discusses an interesting computational method with the goal to gain novel insights into complex biological systems.
It will be held as a block seminar the week before the lectures start.
In order to participate in the seminar you are required to attend the first meeting.
There the topics are distributed. No prior reservation is possible.
Due to the corona pandemic, the first meeting will be held via Zoom. If you like to participate, please register via mail (teaching@bioinf.uni-sb.de).
After you received a topic, please confirm your participation in the seminar until 2021/07/27 via mail to teaching@bioinf.uni-sb.de.
Also, you have to register officially in HISPOS until 2021/08/06.
# | Paper | Authors | |
---|---|---|---|
1 | Recovering gene interactions from single-cell data using data diffusion | Van Dijk et al. | |
2 | Variance-adjusted Mahalanobis (VAM): a fast and accurate method for cell-specific gene set scoring | Frost | |
3 | Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning | Wang et al. | |
4 | Gene regulatory network inference from single-cell data using multivariate information measures | Chan et al. | |
5 | muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data | Crowell et al. | |
6 | Linear-time cluster ensembles of large-scale single-cell RNA-seq and multimodal data | Do et al. | |
7 | CellO: comprehensive and hierarchical cell typeclassification of human cells with the Cell Ontology | Bernstein et al. | |
8 | Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe | Qiu et al. | |
9 | Model-based understanding of single-cell CRISPR screening | Duan et al. |