BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTSTAMP:20260712T023906Z
UID:8e3c7f3f-9d90-4e29-9883-e95aed987a3d
DTSTART:20260907T080000Z
DTEND:20260910T140000Z
DESCRIPTION:**This free course is a collaboration between ELIXIR Estonia an
 d [ELIXIR Czech Republic](https://www.elixir-czech.cz/).** Single-cell RNA
  sequencing (scRNA-seq) allows researchers to study gene expression at the
  level of individual cells. This approach can\, for example\, help to iden
 tify different cell populations in a complex sample and describe their exp
 ression patterns. To generate and analyse scRNA-seq data\, several methods
  are available\, all with their strengths and weaknesses depending on the 
 researchers’ needs. This 3-day course will cover the main technologies a
 s well as the main aspects to consider while designing an scRNA-seq experi
 ment. In particular\, it will combine the theoretical background of analyt
 ical methods with hands-on data analysis sessions focused on data generate
 d by droplet-based platforms.\n\n\n### Requirements\n\nThis course is desi
 gned for life scientists and bioinformaticians with experience in next-gen
 eration sequencing who aspire to analyse scRNA-seq gene expression data.\n
 \nThe course exercises are conducted in the R statistical language\, so a 
 basic understanding of R and RStudio is essential and strictly required.\n
 \n### Attribution\n\nThis course is heavily based on the course developed 
 by the Swiss Institute of Bioinformatics ([https://sib-swiss.github.io/sin
 gle-cell-r-training/](https://sib-swiss.github.io/single-cell-r-training/)
 ). It also draws inspiration from the Broad Institute Single Cell Workshop
  and the CRUK CI Introduction to Single-Cell RNA-Seq Data Analysis course.
 \n\n### Programme\n\nDay 1 – Monday\, 7th of September\n\n    9:00 – 9
 :30 Introduction\n    9:30 – 10:30 Introduction to scRNA-seq\n    10:30 
 – 11:00 Break\n    11:00 – 12:30 10× and Cellranger\n    12:30 – 13
 :30 Lunch\n    13:30 – 15:00 Analysis tools and QC\n    15:00 – 15:30 
 Break\n    15:30 – 17:00 Group work\n\nDay 2 – Tuesday 8th of Septembe
 r\n\n    9:00 – 10:30 Normalisation and scaling\n    10:30 – 11:00 Bre
 ak\n    11:00 – 12:30 Dimensionality reduction and integration\n    12:3
 0 – 13:30 Lunch\n    13:30 – 15:00 Clustering\n    15:00 – 15:30 Bre
 ak\n    15:30 – 17:00 Group work\n\nDay 3 – Wednesday 9th of September
 \n\n    9:00 – 10:30 Cell annotation\n    10:30 – 11:00 Break\n    11:
 00 – 12:30 Differential gene expression\n    12:30 – 13:30 Lunch\n    
 13:30 – 15:00 Group work\n\nDay 4 - Thursday 10th of September\n\n    10
 :00 – 12:00 Group work\n    12:00 – 13:00 Lunch\n    14:00 – 15:00 P
 resentations\n\n### Topics\n\n- **Introduction to Single-Cell RNA Sequenci
 ng** Jan Kubovciak\n  - Topics covered: Overview of single-cell RNA sequen
 cing (scRNA-seq) technologies and applications. Key advantages and limitat
 ions of scRNA-seq approaches. Experimental design considerations and intro
 duction to droplet-based technologies such as 10× Genomics.\n- **scRNA-se
 q Data Processing and Quality Control** Jan Kubovciak\n  - Topics covered:
  Introduction to the 10× Genomics workflow and the Cell Ranger pipeline. 
 Overview of commonly used analysis tools for scRNA-seq data. Quality contr
 ol metrics and strategies for identifying low-quality cells and technical 
 artefacts.\n- **Data Normalisation and Scaling** Jan Kubovciak/Lucie Pfeif
 erova\n  - Topics covered: Methods for normalising and scaling scRNA-seq d
 ata. Handling technical variability and preparing datasets for downstream 
 analysis using R-based workflows.\n- **Dimensionality Reduction and Data I
 ntegration** Lucie Pfeiferova\n  - Topics covered: Techniques for reducing
  data dimensionality (e.g.\, PCA\, UMAP\, t-SNE) and integrating multiple 
 datasets. Strategies for correcting batch effects and combining datasets f
 rom different experiments.\n- **Clustering of Single Cells** Lucie Pfeifer
 ova\n  - Topics covered: Clustering algorithms used to identify cell popul
 ations in scRNA-seq data. Interpretation of clustering results and strateg
 ies for identifying biologically meaningful groups.\n- **Cell Annotation a
 nd Biological Interpretation** Lucie Pfeiferova\n  - Topics covered: Appro
 aches for annotating cell types using marker genes\, reference datasets\, 
 and automated annotation tools. Interpretation of cell population identiti
 es.\n- **Differential Gene Expression Analysis**\n  - Topics covered: Meth
 ods for identifying differentially expressed genes between cell population
 s. Considerations specific to scRNA-seq datasets and interpretation of res
 ults.\n- **Group Work: scRNA-seq Analysis Workflow**\n  - Topics covered: 
 Hands-on analysis of scRNA-seq datasets. Participants will apply the full 
 workflow\, including quality control\, normalisation\, clustering\, annota
 tion\, and differential expression analysis. Results will be discussed in 
 group presentations.
LOCATION:Narva mnt 18\, room 2029
SUMMARY:scRNA-seq Data Analysis
URL;VALUE=URI:https://elixir.ut.ee/news/2026/09/07/scRNA-seq_Data_Analysis/
END:VEVENT
END:VCALENDAR