PHAROS Training Series - Course 15 "Workshop in single-cell RNA-Sequencing Analysis"
PHAROS AI Factory announces the 15th Course of its Training Series, under the title "Workshop in single-cell RNA-Sequencing Analysis" under the topic AI4Health, held online via Zoom.
Date: November 25th, 2026, at 11:00 EET
Location: Online via Zoom
Presentation Language: English
Course Description: This workshop provides a comprehensive introduction to single-cell RNA sequencing (scRNA-seq) data analysis using Scanpy, a Python-based toolkit for analyzing single-cell transcriptomics data. Through a combination of theoretical lectures and hands-on exercises, participants will become familiar with the key concepts, computational methods, and analytical steps involved in a standard scRNA-seq analysis workflow. The workshop covers essential stages of single-cell data analysis, including quality control, preprocessing, highly variable gene selection, dimensionality reduction, clustering, differential gene expression analysis, and cell-type annotation. Particular emphasis is placed on understanding the rationale behind analytical choices, the impact of parameter selection, and common challenges and pitfalls in interpreting results. By the end of the workshop, participants will have gained practical experience in processing and exploring scRNA-seq datasets, identifying distinct cellular populations, and annotating cell types using marker genes and enrichment-based approaches. The workshop aims to equip participants with the foundational knowledge and practical skills needed to perform and critically evaluate single-cell transcriptomics analyses.
Audience: Bioinformaticians, Biomedical Data Scientists, Academic Researchers, Master Students, PhD students
Prerequisites: Omics, Python
Learning Objectives:
- Gain a deeper understanding of scRNA-seq data structures, formats, complexities (like sparsity), and standard analysis pipelines.
- Understand the mathematical and biological rationale behind essential steps such as quality control metrics, normalization techniques, dimensionality reduction, clustering, and marker gene discovery.
- Apply learned concepts in Python using Scanpy to process, analyze, and interpret single-cell datasets.
- Successfully execute differential gene expression analysis to identify marker genes and annotate distinct cell types within a heterogeneous dataset.
- Interact directly to clarify best practices, parameter tuning, and how to avoid common pitfalls in single-cell bioinformatics.
Instructors' profiles:
- George Georgakilas is a PhD Scientific Associate, at the Information Management Systems Institute of the Athena Research Center, with expertise on computational biology, single-cell and spatial omics, regulatory genomics, machine learning, and XAI. He holds a PhD in Bioinformatics and Machine Learning from the University of Thessaly, an MSc in Bioinformatics from the National and Kapodistrian University of Athens, and a Diploma in Computer Engineering and Informatics from the University of Patras. He currently leads research activities in AI-driven biological modeling, FAIR data management, and scientific computing infrastructures, participating in national and European research projects.
- Charis Sinnis is a PhD Scientific Associate, at the Information Management Systems Institute of the Athena Research Center, with expertise on quantitative sciences, computational methods, data analysis and single-cell omics. He holds an MSc and a PhD in Astrophysics and a Diploma in Physics from the National and Kapodistrian University of Athens. His work focuses on single-cell omics data analysis, applying computational and statistical approaches to investigate cellular heterogeneity, characterize cell populations, and extract meaningful biological insights from high-dimensional molecular datasets.
- Haris Manousaki is an MSc Scientific Associate, at the Information Management Systems Institute of the Athena Research Center, with expertise on bioinformatics, single-cell and spatial omics and machine learning. She holds an MSc in Data Science and Information Technologies from the National and Kapodistrian University of Athens (NKUA) and an Integrated master in Applied Biology and Biotechnology from the Agricultural University of Athens. Her current research focuses on spatial transcriptomics, investigating the spatial organization of gene expression and cellular populations within complex tissues.
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