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DTSTAMP:20260728T033701Z
UID:410f8b29-b9a8-4132-aa43-72d74936500e
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\n- **scRNA-
 seq Data Processing and Quality Control** Jan Kubovciak\n  - Topics covere
 d: Introduction to the 10× Genomics workflow and the Cell Ranger pipeline
 . Overview of commonly used analysis tools for scRNA-seq data. Quality con
 trol metrics and strategies for identifying low-quality cells and technica
 l artefacts.\n\n- **Data Normalisation and Scaling** Jan Kubovciak/Lucie P
 feiferova\n  - Topics covered: Methods for normalising and scaling scRNA-s
 eq data. Handling technical variability and preparing datasets for downstr
 eam analysis using R-based workflows.\n\n- **Dimensionality Reduction and 
 Data Integration** Lucie Pfeiferova\n  - Topics covered: Techniques for re
 ducing data dimensionality (e.g.\, PCA\, UMAP\, t-SNE) and integrating mul
 tiple datasets. Strategies for correcting batch effects and combining data
 sets from different experiments.\n\n- **Clustering of Single Cells** Lucie
  Pfeiferova\n  - Topics covered: Clustering algorithms used to identify ce
 ll populations in scRNA-seq data. Interpretation of clustering results and
  strategies for identifying biologically meaningful groups.\n\n- **Cell An
 notation and Biological Interpretation** Lucie Pfeiferova\n  - Topics cove
 red: Approaches for annotating cell types using marker genes\, reference d
 atasets\, and automated annotation tools. Interpretation of cell populatio
 n identities.\n\n- **Differential Gene Expression Analysis**\n  - Topics c
 overed: Methods for identifying differentially expressed genes between cel
 l populations. Considerations specific to scRNA-seq datasets and interpret
 ation of results.\n\n- **Group Work: scRNA-seq Analysis Workflow**\n  - To
 pics covered: Hands-on analysis of scRNA-seq datasets. Participants will a
 pply the full workflow\, including quality control\, normalisation\, clust
 ering\, annotation\, 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/
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