Analysis of the Single-Cell Sequencing Data 320-ERS-1AOTz
Substantive content:
Single-cell sequencing technology allows for getting the insight into the processes of cell development and differentiation with the resolution which is unattainable using bulk data. In brief, this method consists of disassembling of tissue or organ into cells and encapsulating them separately within fluid droplets. Based on the barcode DNA fragments from each droplet, products of the subsequent sequencing can be assigned to individual cells. The technology is widely used in the studies of immune response, carcinogenesis and organism development. However, dealing with single-cell data raises the new problems we should be addressed to during data analysis. These are:
Data sparsity (high frequency of dropouts, that is zero data matrix entries which result from some of the mRNA or DNA molecules not being captured during library preparation)
Large datasets (the data is stored in the matrices whose size is number of cells x number of studied genes)
High data dimensionality (we try to investigate the differences in the expression of many genes simultaneously)
Multimodality (we want integrate datasets representing different modalities, for example gene expression and chromatin accessibility – ATACseq).
During the course students will be guided in performing the analysis using the output from single-cell technology. The analysis includes:
Cell annotation (identification of cell types)
Identification of highly variable genes
Data cleaning and quality control
Data clustering and visualization
This project will be done using Bioconductor ecosystem, which is being developed under R environment. Thus, students will be introduced to the basics of using R (if necessary).
W cyklu 2024:
Substantive content: During the course, students will be guided in performing the analysis using the output from single-cell technology. The analysis includes: |
Rodzaj przedmiotu
Koordynatorzy przedmiotu
Kryteria oceniania
Forms and conditions of credit:
- attendance
- tasks completion
Literatura
Literature:
Amezquita, R.A., Lun, A.T.L., Becht, E. et al. Orchestrating single-cell analysis with Bioconductor. Nat Methods 17, 137–145 (2020). https://doi.org/10.1038/s41592-019-0654-x
W cyklu 2024:
Literature: |
Więcej informacji
Dodatkowe informacje (np. o kalendarzu rejestracji, prowadzących zajęcia, lokalizacji i terminach zajęć) mogą być dostępne w serwisie USOSweb: