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 2021/02/01 via mail to teaching@bioinf.uni-sb.de.
Also, you have to register officially in HISPOS until 2021/02/15.
| # | Paper | Authors | |
|---|---|---|---|
| 1 | Recovering gene interactions from single-cell data using data diffusion | Van Dijk et al. | |
| 2 | SciBet as a portable and fast single cell type identifier | Li et al. | |
| 3 | Single cell clustering based on cell-pair differentiability correlation and variance analysis | Jiang et al. | |
| 4 | Random forest based similarity learning for single cell RNA sequencing data | Pouyan and Kostka | |
| 5 | Variance-adjusted Mahalanobis (VAM): a fast and accurate method for cell-specific gene set scoring | Frost | |
| 6 | CMF-Impute: an accurate imputation tool for single-cell RNA-seq data | Xu et al. | |
| 7 | An accurate and robust imputation method scImpute for single-cell RNA-seq data | Li and Li | |
| 8 | Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning | Wang et al. | |
| 9 | Identification of spatial expression trends in single-cell gene expression data | Edsgärd et al. | |
| 10 | Reversed graph embedding resolves complex single-cell trajectories | Qiu et al. | |
| 11 | Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis | Li et al. | |
| 12 | Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data | Barron and Li |