Webinar 2019 3: June 2019

DATE: Friday, 28 June at 16:00 h (CET)
TITLE: How to uncover biological patterns in omics data- an overview
HOST: Maren Büttner, MSc, (Helmholtz Center Munich, DE)

REGISRATION: Attendance is FREE but registration is required
Please register at: GoToWebinar

Space is limited and priority is given to ECN members on a first-come-first-served basis.

Webinar resumé
This webinar, the first of our three-part series on multivariate statistics in nutrition, is targeted at researchers who are using (or plan to use) omic data such as metabolomics, proteomics or transcriptomics to address their research questions. The first part of the webinar will cover how to use principal component analysis (PCA) as a tool for dimensionality reduction and detection of biases. In addition, guidance will be provided on how to select the number of meaningful components and how to interpret the loadings of principal components (if necessary). The second part of the webinar will explore how to complete basic clustering, including hierarchical clustering (e.g.as used in dendrograms), and how to extract groups from it. A practical example of the typical workflow for these analyses will be provided with an R script example for those who use R programming.

Extensive knowledge in R programming is not essential to participate in the webinar but the application of some of the methods is demonstrated in the R environment.

Webinar online
You can visit the webinar online on Vimeo here


ICB, Institute of Computational Biology, frei, alle Nutzungsrechte bei Helmholtz Zentrum München

Maren has a strong background in mathematics and the application of mathematics to bioscience, completing undergraduate and masters level training at the Technical University Munich. During her PhD in the lab of Fabian J Theis (Technical University Munich), she has focused on the analysis of single-cell transcriptomics data and the integration of different data types from several experimental sources. Her projects address in particular the development of statistical methods to deal with measurement noise, confounding factors and issues arising from high-dimensionality of the data. Recently, she has been establishing a single-cell RNA sequencing data analysis support unit with standardised analysis pipelines according to the current best practices in the field. Methodologically, her research focus has shifted towards the application of deep learning models in single-cell transcriptomics. Maren has experience in teaching from mathematics to transcriptomics analysis.