Important! The first meeting on 2020/07/03/ will be held only as Zoom-meeting. If you want to participate in the seminar, please write us an e-mail, so we can send you the Zoom invitation link.
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 registration or reservation is possible.
Please confirm your participation in the seminar until 2020/07/10 via mail to teaching@bioinf.uni-sb.de.
Also, you have to register in HISPOS until 2020/07/24.
| # | Paper | Autoren | |
|---|---|---|---|
| 1 | Recovering gene interactions from single-cell data using data diffusion | Van Dijk et al. | |
| 2 | DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data | Arisdakessian et al. | |
| 3 | An accurate and robust imputation method scImpute for single-cell RNA-seq data | Li and Li | |
| 4 | Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning | Wang et al. | |
| 5 | scmap: projection of single-cell RNA-seq data across data sets | Kiselev et al. | |
| 6 | Supervised classification enables rapid annotation of cell atlases | Pliner et al. | |
| 7 | Identification of spatial expression trends in single-cell gene expression data | Edsgärd et al. | |
| 8 | Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics | Street et al. | |
| 9 | Reversed graph embedding resolves complex single-cell trajectories | Qiu et al. | |
| 10 | Gene regulatory network inference from single-cell data using multivariate information measures | Chan et al. |