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VERSION:2.0
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CALSCALE:GREGORIAN
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DTSTAMP:20260618T093437Z
UID:64d574d3-3ea4-4221-ac47-2714e7448105
DTSTART:20260907T080000Z
DTEND:20260910T140000Z
DESCRIPTION:Single-cell RNA sequencing (scRNA-seq) allows researchers to st
 udy gene expression at the level of individual cells. This approach can\, 
 for example\, help to identify different cell populations in a complex sam
 ple and describe their expression patterns. To generate and analyse scRNA-
 seq data\, several methods are available\, all with their strengths and we
 aknesses depending on the researchers’ needs. This 3-day course will cov
 er the main technologies as well as the main aspects to consider while des
 igning an scRNA-seq experiment. In particular\, it will combine the theore
 tical background of analytical methods with hands-on data analysis session
 s focused on data generated by droplet-based platforms.\n\n\n### Requireme
 nts\n\nThis course is designed for life scientists and bioinformaticians w
 ith experience in next-generation sequencing who aspire to analyse scRNA-s
 eq gene expression data.\n\nThe course exercises are conducted in the R st
 atistical language\, so a basic understanding of R and RStudio is essentia
 l and strictly required.\n\n### Attribution\n\nThis course is heavily base
 d on the course developed by the Swiss Institute of Bioinformatics ([https
 ://sib-swiss.github.io/single-cell-r-training/](https://sib-swiss.github.i
 o/single-cell-r-training/)). It also draws inspiration from the Broad Inst
 itute 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 S
 eptember\n\n    9:00 – 9:30 Introduction\n    9:30 – 10:30 Introductio
 n to scRNA-seq\n    10:30 – 11:00 Break\n    11:00 – 12:30 10× and Ce
 llranger\n    12:30 – 13:30 Lunch\n    13:30 – 15:00 Analysis tools an
 d QC\n    15:00 – 15:30 Break\n    15:30 – 17:00 Group work\n\nDay 2 
 – Tuesday 8th of September\n\n    9:00 – 10:30 Normalisation and scali
 ng\n    10:30 – 11:00 Break\n    11:00 – 12:30 Dimensionality reductio
 n and integration\n    12:30 – 13:30 Lunch\n    13:30 – 15:00 Clusteri
 ng\n    15:00 – 15:30 Break\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 L
 unch\n    14:00 – 15:00 Presentations\n\n### Topics\n\n- **Introduction 
 to Single-Cell RNA Sequencing** Jan Kubovciak\n  - Topics covered: Overvie
 w of single-cell RNA sequencing (scRNA-seq) technologies and applications.
  Key advantages and limitations of scRNA-seq approaches. Experimental desi
 gn considerations and introduction to droplet-based technologies such as 1
 0× Genomics.\n- **scRNA-seq Data Processing and Quality Control** Jan Kub
 ovciak\n  - Topics covered: Introduction to the 10× Genomics workflow and
  the Cell Ranger pipeline. Overview of commonly used analysis tools for sc
 RNA-seq data. Quality control metrics and strategies for identifying low-q
 uality cells and technical artefacts.\n- **Data Normalisation and Scaling*
 * Jan Kubovciak/Lucie Pfeiferova\n  - Topics covered: Methods for normalis
 ing and scaling scRNA-seq data. Handling technical variability and prepari
 ng datasets for downstream analysis using R-based workflows.\n- **Dimensio
 nality Reduction and Data Integration** Lucie Pfeiferova\n  - Topics cover
 ed: Techniques for reducing data dimensionality (e.g.\, PCA\, UMAP\, t-SNE
 ) and integrating multiple datasets. Strategies for correcting batch effec
 ts and combining datasets from different experiments.\n- **Clustering of S
 ingle Cells** Lucie Pfeiferova\n  - Topics covered: Clustering algorithms 
 used to identify cell populations in scRNA-seq data. Interpretation of clu
 stering results and strategies for identifying biologically meaningful gro
 ups.\n- **Cell Annotation and Biological Interpretation** Lucie Pfeiferova
 \n  - Topics covered: Approaches for annotating cell types using marker ge
 nes\, reference datasets\, and automated annotation tools. Interpretation 
 of cell population identities.\n- **Differential Gene Expression Analysis*
 *\n  - Topics covered: Methods for identifying differentially expressed ge
 nes between cell populations. Considerations specific to scRNA-seq dataset
 s and interpretation of results.\n- **Group Work: scRNA-seq Analysis Workf
 low**\n  - Topics covered: Hands-on analysis of scRNA-seq datasets. Partic
 ipants will apply the full workflow\, including quality control\, normalis
 ation\, clustering\, annotation\, and differential expression analysis. Re
 sults 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/
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