Optimizing and Implementing Single-Cell Technologies to Uncover Complex Biology

Date: Day 2, Thursday 30th January 2025
Time: 11:20 - 12.50
Location: The Single-Cell & Spatial Dome

Single-cell analysis has long been used to detect cellular heterogeneity, signalling pathways, and disease mechanisms. Despite the advent of spatial analysis and new techniques, single-cell continues to be the dominant tool in revealing cell population differences and cellular evolutionary relationships. This workshop aims to help participants decipher single-cell data, improve analysis workflows and demonstrate how to integrate existing datasets to uncover novel biological insights.

Learning Objectives

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  1. Recognize how sharing phenotype-linked variant data advances rare disease diagnosis and research, enhancing the identification and understanding of rare genetic conditions.
  2. Familiarize yourself with tools like DECIPHER that offer powerful interfaces and databases for accurate genomic variant interpretation, supporting diagnosis and research efforts.
  3. Learn and apply ACGS guidelines to score and sub-divide variants of uncertain significance, determine reportable variants, follow recommendations for additional testing, and navigate reclassification for precise genetic analysis.

Workshop Format

Your Title Here

Computational analyses of single-cell: technology landscape and its best practices

  • Dreamcatcher allows accurate identification and quantification of bacterial reads in single-cell, spatial, and bulk RNA-seq
  • Discuss different possibilities enabled by technology using single-cell. Discuss the best practices in choosing the right pipeline for an analysis
  • A workshop on developing a small pipeline for an analysis


Dreamcatcher allows accurate identification and quantification of bacterial reads in single-cell, spatial, and bulk RNA-seq

  • Microbial reads are often discovered in RNA-seq samples; however, accurate identification of relevant bacteria is very challenging
  • We have developed a pipeline that allows us to remove computational false positives, evaluate "contaminome" contribution, and analyze single cell/spatial reads together with the host data
  • We will present a tutorial on performing this analysis and the types of biological information that can be learned from our approach


Q&A

This Q&A session is your chance to ask any burning questions Q&A

Workshop Leaders

Alexander Predeus

Principal Bioinformatician

Wellcome Sanger Institute

Bodhayan Prasad

Bioinformatician

University of Glasgow