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SUMMARY:PHAROS Training Series - Course 15 "Workshop in single-cell RNA-Se
 quencing Analysis"
DTSTART;VALUE=DATE-TIME:20261125T090000Z
DTEND;VALUE=DATE-TIME:20261125T120000Z
DTSTAMP;VALUE=DATE-TIME:20261101T002216Z
UID:indico-event-223@events.grnet.gr
DESCRIPTION:\n\nPHAROS AI Factory announces the 15th Course of its Trainin
 g Series\, under the title "Workshop in single-cell RNA-Sequencing Analysi
 s" under the topic AI4Health\, held online via Zoom.  \n\nDate: Novemb
 er 25th\, 2026\, at 11:00 EET \n\nLocation: Online via Zoom\n\nPresentat
 ion Language: English\n\nCourse Description: This workshop provides a com
 prehensive introduction to single-cell RNA sequencing (scRNA-seq) data ana
 lysis using Scanpy\, a Python-based toolkit for analyzing single-cell tran
 scriptomics data. Through a combination of theoretical lectures and hands-
 on exercises\, participants will become familiar with the key concepts\, c
 omputational methods\, and analytical steps involved in a standard scRNA-s
 eq analysis workflow. The workshop covers essential stages of single-cell 
 data analysis\, including quality control\, preprocessing\, highly variabl
 e gene selection\, dimensionality reduction\, clustering\, differential ge
 ne expression analysis\, and cell-type annotation. Particular emphasis is 
 placed on understanding the rationale behind analytical choices\, the impa
 ct of parameter selection\, and common challenges and pitfalls in interpre
 ting results. By the end of the workshop\, participants will have gained p
 ractical experience in processing and exploring scRNA-seq datasets\, ident
 ifying distinct cellular populations\, and annotating cell types using mar
 ker genes and enrichment-based approaches. The workshop aims to equip part
 icipants with the foundational knowledge and practical skills needed to pe
 rform and critically evaluate single-cell transcriptomics analyses.\n\nAud
 ience:  Bioinformaticians\, Biomedical Data Scientists\, Academic Researc
 hers\, Master Students\, PhD students \n\nPrerequisites: Omics\, Python \
 n\nLearning Objectives: \n\n\n	Gain a deeper understanding of scRNA-seq d
 ata structures\, formats\, complexities (like sparsity)\, and standard ana
 lysis pipelines.\n	Understand the mathematical and biological rationale be
 hind essential steps such as quality control metrics\, normalization techn
 iques\, dimensionality reduction\, clustering\, and marker gene discovery.
 \n	Apply learned concepts in Python using Scanpy to process\, analyze\, an
 d interpret single-cell datasets.\n	Successfully execute differential gene
  expression analysis to identify marker genes and annotate distinct cell t
 ypes within a heterogeneous dataset.\n	Interact directly to clarify best p
 ractices\, parameter tuning\, and how to avoid common pitfalls in single-c
 ell bioinformatics.\n\n\nInstructors' profiles:\n\n\n	George Georgakilas i
 s a PhD Scientific Associate\, at the Information Management Systems Insti
 tute of the Athena Research Center\, with expertise on computational biolo
 gy\, single-cell and spatial omics\, regulatory genomics\, machine learnin
 g\, and XAI. He holds a PhD in Bioinformatics and Machine Learning from th
 e 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 Europ
 ean research projects.\n	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 com
 putational and statistical approaches to investigate cellular heterogeneit
 y\, characterize cell populations\, and extract meaningful biological insi
 ghts from high-dimensional molecular datasets.\n	Haris Manousaki is an MS
 c Scientific Associate\, at the Information Management Systems Institute o
 f the Athena Research Center\, with expertise on bioinformatics\, single-c
 ell and spatial omics and machine learning. She holds an MSc in Data Scien
 ce and Information Technologies from the National and Kapodistrian Univers
 ity of Athens (NKUA) and an Integrated master in Applied Biology and Biote
 chnology from the Agricultural University of Athens. Her current research 
 focuses on spatial transcriptomics\, investigating the spatial organizatio
 n of gene expression and cellular populations within complex tissues.\n\n\
 n \n\nNote: Please enter your institutional/corporate email when register
 ing.\n\n \n\nhttps://events.grnet.gr/event/223/
LOCATION:
URL:https://events.grnet.gr/event/223/
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