This course will introduce methods and approaches to analyse longitudinal data, i.e. data which are repeated in time or space (or any other dimensions, for that matter!). Longitudinal data present specific challenges in all aspects of processing and analysis, from visualization to exploratory data analysis, to modelling and validation. The course will outline the main challenges related to dealing with longitudinal data, from the classical statistical perspective and from the machine learning perspective. Specific areas that will be covered include forecasting (prediction of time-series data), survival analysis, epidemiology and gene-expression experiments.

The course is structured in modules over four days. Each day will include lectures with class discussions of key concepts and practical hands-on sessions with collaborative exercises where students will interact with the whole class and instructors to apply the acquired skills. After and during each exercise, results will be interpreted and discussed. At the end of the course, a quiz will be taken together to recap and highlight the most important concepts covered, and there will be room to discuss specific research problems and questions from participants.

The course is aimed at advanced students, researchers and professionals interested in learning what longitudinal data are and how to analyze them in the context of real life applications in biology. It will include information useful for both absolute beginners and more advanced users willing to delve into some aspects of the implementation of longitudinal models and scripting code. We will start by introducing the general concepts and approaches to deal with longitudinal data; we will then explore applications to specific scientific domains where longitudinal data are common: forecasting, epidemiology, gene expression.
Attendees are expected to have a background in biology and the research problems involving prediction, inference, pattern discovery; previous exposure to inferential and predictive experiments would be beneficial. There will be a mix of lectures and hands-on practical exercises using mainly R and Markdown. Some basic understanding of R programming will be advantageous, but is not required.

At the end of the course the student will have an understanding of:

• how to recognise and treat spatial and temporal dependencies in the data

• the most common methods to analyse data with repeated records

• methods and principles of data forecasting

• specific applications to life-science domains like epidemiology and gene expression experiments

• how to design, analyse and interpret scientific experiments with a time component

Monday– Classes from 2-8 PM Berlin time

-Longitudinal data: examples and challenges

- The classical statistical perspective

- Models to analyse data with repeated records over time (multiple time points) and space (multiple locations)


Tuesday– Classes from 2-8 PM Berlin time

- The machine-learning perspective: Deep Learning and Transformer Models for the analysis of sequence data "NEW"

- Cross-validation with temporal and spatial data structures

Wednesday– Classes from 2-8 PM Berlin time

- A primer on longitudinal data in epidemiology: times series of disease incidence/prevalence, survival analysis)

- Univariate (ANOVA) and multivariate (MANOVA) analysis of variance for longitudinal data, their limits, and how to improve the analysis

- Mixed-effect regression model (MRM) vs. generalised estimating equation (GEE) models. Analysis of residuals and model diagnostics.

- Quantifications of covariation between measurement: cross-sectional (inter-individual) vs. longitudinal (intra-individual)


Thursday– Classes from 2-8 PM Berlin time

- Multi-omics analysis: a study in interpretability on HeLa Cell Cycling for integration of mRNA,   Translation Data and Proteomics

Final recap quiz

Discussing your own research problems and wrap-up discussion

Should you have any further questions, please send an email to info@physalia-courses.org