Single-Cell Gene Expression Data Analytics: A Hands-On Workshop


Single-Cell Gene Expression Data Analytics: A Hands-On Workshop




Dr. Blaž ZUPAN
University of Ljubljana | Baylor College of Medicine, Houston

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Single-cell RNA sequencing is a great new technology that can profile potentially large number of individual cells. The resulting expression data is a matrix with cells and genes, and — because of its sheer size and potential information content — is challenging for data analysis. Data filtering, projection, clustering, cell trajectory discovery, discovery of marker genes, and cell type classification are just a few approaches we can apply to scRNA data. In this talk, I will argue that despite this jungle of methods and data any biologists can be trained, within a few hours, to apply latest algorithms of data science and get insight into their own single cell data. The key to this endeavor are easy-to-use tools for data mining that feature interactive visualizations and intuitive, visual programming approaches for construction of data analysis workflows. I will demonstrate the use of one such tool, scOrange (, on a number of recent single cell data sets. I will show that single cell data analysis does not necessary require deep knowledge of computer science, statistics and machine learning, and that construction of advanced data analysis workflows can be as easy as playing with Lego bricks.

About the speaker: Dr. Blaž Zupan has worked on machine learning seemingly forever. He heads the bioinformatics lab at University of Ljubljana and is an Associate Professor at the Baylor College of Medicine in Houston. His research has focused on constructive induction, machine learning and epistasis approaches to reconstruction of gene networks, large-scale data fusion, and algorithms to propose informative data visualizations. He believes that crafting simple tools that anybody can use to understand data is essential to advancements of humanity and democracy. His lab developed Orange (, a fully open-source, ever evolving data mining suite with a visual programming environment. He also enjoys writing scripts for YouTube videos to explain data science (check out, and preparing courses that introduce data science.

Crediti Formativi:

È previsto l’accreditamento di 1 CFU per gli studenti del Dottorato universitario in tecnologie della salute, bioingegneria e bioinformatica.


Aula B2,
Department of Electrical Computer and Biomedical Engineering, University of Pavia
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