PHAROS Training Series - Course 15 "Workshop in single-cell RNA-Sequencing Analysis"

Wednesday 25 Nov 2026, 11:00 → 14:00 Europe/Athens

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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11:00 → 11:05 Introduction to Single-Cell Transcriptomics

This introductory session provides an overview of single-cell RNA sequencing (scRNA-seq), the Scanpy ecosystem, and the main objectives of the workshop.

Speaker: George Georgakilas (ΑΤΗΕΝΑ RC)

11:05 → 11:20 Pre-processing & HVGs Detection

This theoretical session introduces quality control, highlighting key factors to consider when defining QC thresholds. It also covers preprocessing strategies and highly variable gene (HVG) selection, explaining their importance in capturing biological variability and the computational approaches used to identify them.

Speaker: Charis Sinnis (ATHENA RC)

11:20 → 11:55 Hands-on 1: Pre-processing & HVGs Detection

This hands-on session guides participants through preprocessing and HVG selection, emphasizing important data characteristics and their influence on analytical decisions.

Speaker: Charis Sinnis (ATHENA RC)

11:55 → 12:10 Dimensionality Reduction & Clustering

This theoretical session introduces dimensionality reduction and clustering methods, explaining their underlying principles and their role in identifying cellular populations and exploring transcriptomic heterogeneity.

Speaker: Haris Manousaki (ATHENA RC)

12:10 → 12:45 Hands-on 2: Dimensionality Reduction & Clustering

This hands-on session focuses on principal component analysis (PCA), dimensionality reduction, and clustering, with particular emphasis on parameter selection and its impact on downstream results.

Speaker: Haris Manousaki (ATHENA RC)

12:45 → 13:00 Cell Type Annotation

This theoretical session introduces differential gene expression analysis for identifying cluster-specific marker genes. It also discusses cell-type annotation strategies, their underlying assumptions, and common challenges and pitfalls.

Speaker: George Georgakilas (ΑΤΗΕΝΑ RC)

13:00 → 13:35 Hands-on 3: Cell Type Annotation

This hands-on session guides participants through cell-type annotation using differentially expressed genes, known cell-type markers, and an enrichment-based approach.

Speaker: George Georgakilas (ΑΤΗΕΝΑ RC)

13:35 → 13:45 Closing remarks

The workshop concludes with an open discussion addressing participants' questions, coding issues, and common troubleshooting challenges encountered during the hands-on sessions.

Speaker: George Georgakilas (ΑΤΗΕΝΑ RC)