Tumours are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. A tumor’s response to treatments is governed by an interaction of the cancer cell’s intrinsic factors with external influences of the tumor microenvironment. Disentangling the heterogeneity within a tumor is a crucial step in the development and utilisation of effective cancer therapies. The single cell sequencing technology enables an effective molecular characterisation of single cells within the tumor. This technology can help deconvolute heterogeneous tumor samples and thus revolutionise personalised medicine. However, a governing challenge in cancer single cell analysis is cell annotation, that is, the assignment of a particular cell type or a cell state to each sequenced cell. The identification of tumor cells within single cell or spatial sequencing experiments remains a critical and limiting step for research, clinical, and commercial applications. In this webinar, we will discuss these challenges and a novel machine learning pipeline aimed at performing automatic annotation and distinguishing tumor cells from normal cells at the single cell level.

About the speaker

Altuna Akalin is a bioinformatics scientist and the head of Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center (MDC) in Berlin. He has developed computational methods for analysing and integrating large-scale genomics data sets since 2002. He uses machine learning and statistics to uncover biological patterns, for example those related to disease state and type. In his current work he uses complex molecular signatures to provide decision support systems for disease diagnostics and biomarker discovery. Additionally, he is actively involved in organising and teaching at computational genomics courses.

Who is this course for?

This webinar is part of PerMedCoE webinar series and is open for anyone interested in simulation of metabolic models, in applications of single cell and machine learning technologies, and in PerMedCoE tools and activities. The goal of PerMedCoE is to provide an efficient and sustainable entry point to the HPC/Exascale-upgraded methodology to translate omics analyses into actionable models of cellular functions of medical relevance. No prior knowledge is required.

Outcomes

By the end of this webinar, you will be able to:

• Exemplify the applications of single cell to identify tumour cells
• Describe a machine learning pipeline for distinguishing tumor cells from normal cells at the single cell level

This webinar took place on 03 March 2022. Please click the 'Watch video' button to view the recording.