Biostatistics workshops

Translate biological hypotheses into causal questions using potential outcomes and directed graphs. The practical programme identifies confounding, mediation and collider bias and implements matching, propensity scores, inverse weighting, g-computation and doubly robust estimation. It then develops causal machine-learning approaches, including forests, targeted learning, uncertainty quantification and conformal prediction, before applying mediation analysis to multi-omics integration and reproducible R/Python workflows. Sessions: 2–6 November, 14:00–18:00 daily, Europe/Berlin (CET, UTC+01:00), with Slack discussion between sessions. Live online training; fee €480. For advanced MSc, doctoral and postdoctoral researchers with R/Bioconductor, probability, statistics and regression experience.

Investigate whether conserved protein structures can improve phylogenetic inference for deeply diverged or rapidly evolving sequences. The workflow retrieves predicted structures, evaluates homology and alignment quality, and combines amino-acid and Foldseek 3Di representations for likelihood-based trees. Exercises compare structural alignment tools, evolutionary models, information criteria and distance methods, then visualise and statistically compare trees. Discussions examine assumptions and limitations of structure-based evidence. Live sessions: 2, 4, 5 and 6 November, 08:00–12:00, Europe/Madrid (CET, UTC+01:00). Total 22 hours: 16 live plus six independent. English-language live online course; Fee €560; Ambassador Institution rate €448. VAT included. Capacity: 15. R experience is expected and Unix familiarity is helpful. Participants install the listed analysis and visualisation software before the course. Registration requires acceptance; payment follows confirmation. Recordings are provided, but live attendance is required for a certificate.

Build a statistically rigorous RNA-seq analysis in R and Bioconductor. The workshop develops data import and visualisation skills, hypothesis testing and genomic interval analysis before covering read mapping, count normalisation, differential expression and multiple-testing correction. Practical comparisons include DESeq2, edgeR and limma/voom. The final session interprets functional pathways using overrepresentation and permutation-based gene-set enrichment analysis. Six live online sessions on 2, 3, 5, 6, 9 and 10 November, 15:00–18:00 Europe/Berlin (CET). Fee €530. Basic R programming and genomics knowledge are expected; previous statistical training is unnecessary.

This practical school introduces bulk RNA-seq analysis for biologists and data analysts with little computing experience. Participants work through Linux commands, sequencing technologies and file formats, quality assessment and trimming, read alignment, mapping diagnostics and visualization using an Illumina dataset. Later sessions compare methods for quantifying transcripts and detecting differential gene expression, splicing and isoform expression, with R graphics and exercises to interpret the results. Single-cell RNA-seq analysis is outside the programme. Teaching runs from 09:00 to 17:00 on 2–5 November 2026, local Berlin time (CET, UTC+1). A basic understanding of molecular biology is expected. Instruction is in English; workstations and course materials are supplied. The fee is EUR 1,449 excluding VAT, with travel and accommodation additional. The course has a 30-person capacity, allocated in order of registration and subject to seat confirmation; the form may place applicants on a waiting list. Registration closes on 30 October.

Quantify biological shape through a reproducible three-dimensional morphometrics workflow in 3D Slicer and R. Participants connect image acquisition and resolution to segmentation and cleaning, collect landmarks and semilandmarks, and derive shape variables using Procrustes alignment and sliding procedures. Statistical exercises use principal components and regression to interpret size and shape variation; visualisations include deformations, morphings and heatmaps. Live sessions: 9–13 November, 13:00–18:00 daily, Europe/Madrid (CET, UTC+01:00). Total 30 hours: 25 live plus five independent. English-language live online course; Fee €610; Ambassador Institution rate €488. VAT included. Capacity: 20. A Life Sciences degree and familiarity with R and geometric-morphometric concepts are expected. R/RStudio and 3D Slicer are required. Registration requires acceptance; payment follows confirmation. Recordings are provided, but live attendance is required for a certificate.

The second M4H workshop brings mathematics, data science and medical research together to improve care. Mathematical models, numerical methods, statistics and machine learning support precision medicine, healthcare-resource allocation and multimodal patient-data analysis. Keynotes examine evolutionary cancer treatment and model-driven digital twins, longitudinal health records, topology and geometry for cyclic gene expression, statistical approaches to individual mental-health variation and patient-specific blood-flow simulation. Panels connect these methods with cardiovascular medicine, health data science and sustainable brain health. The Department of Mathematics at Politecnico di Milano hosts the in-person workshop on 11–13 November 2026 in Aula Rogers, Building 11, Via Ampère 2, Milan. Posters use Aula Vetrata, Trifoglio Building 13, Via Bonardi 9. The programme runs 11 November 14:00–19:30, 12 November 09:00–18:10 and 13 November 09:00–16:00. Times are Europe/Rome, CET (UTC+01:00). Registration is free and open until 15 October through the form on the official workshop page. Speaking slots are invitation only; general attendance is open, and participants may submit poster abstracts.

This course develops a metabolomics investigation from experimental design through data preparation and statistical interpretation. Lectures, practical work and group discussion connect analytical chemistry and data collection with reproducible analysis. Exercises cover R data handling and visualisation, xcms processing of untargeted mass-spectrometry data, quality control, missing values and compound annotation. The final module compares univariate tests, multiple-testing correction, principal-component methods, partial least squares and random forests for metabolomic matrices. Sessions: 16, 17, 18 and 20 November, 09:30–13:30, Europe/Berlin (CET, UTC+01:00); no session on 19 November. Live online training; fee €480. Familiarity with R is assumed; prior metabolomics experience is useful.

A five-day practical course for experimentalists and computational biologists analysing single-cell RNA sequencing. Lectures and exercises follow data from sequencing files to expression matrices, quality control, normalisation, batch correction, dimensionality reduction, clustering, annotation and differential expression. Later sessions cover trajectory and pseudotime inference, RNA velocity, single-cell multi-omics integration and functional analysis. Participants finish with an independent dataset analysis and presentation. Bash and R/Bioconductor support reproducible workflows and interpretation. Online, 16–20 November 2026, 14:00–20:00 CET (Europe/Berlin) each day. Fee: €530. Participants need practical Unix command-line skills, R programming experience and familiarity with next-generation sequencing analysis.

Analyse microbial communities from shotgun sequencing using reproducible computational workflows. The workshop covers read preprocessing, taxonomic and strain-level profiling, functional characterisation, metagenome assembly and reconstruction of microbial genomes. Exercises use tools including MetaPhlAn, StrainPhlAn, PanPhlAn, HUMAnN, MEGAHIT and PhyloPhlAn, while comparing short- and long-read approaches. Statistical sessions address community diversity, high-dimensional testing, regression and classification, followed by visualisation and reusable scientific reports. Live online, 23–26 November, 14:00–20:00 Europe/Berlin (CET). Fee €530. Basic command-line knowledge is helpful; preparatory Unix material is provided.

Develop Bayesian analyses of biological data from prior specification and posterior estimation to computational diagnostics and multimodal integration. Practical work covers conjugate and non-conjugate models, Gibbs and Hamiltonian Monte Carlo, convergence checks, hierarchical regression, regularisation and variational inference. Later sessions compare data-fusion strategies and Bayesian factor models while quantifying uncertainty in integrated biomedical datasets. Sessions: 30 November–4 December, 14:00–18:00 daily, Europe/Berlin (CET, UTC+01:00), with Slack discussion between meetings. Live online training; fee €480. For advanced MSc, doctoral and postdoctoral researchers with R/Bioconductor, probability, statistics and regression experience.

Reconstruct evolutionary trees while representing prior knowledge and uncertainty in BEAST2. Lectures and tutorials explain prior selection, birth–death and coalescent models, fossil information, multispecies coalescence and rate variation across clades. Participants compare models and troubleshoot practical analyses, with time to develop their own datasets. The emphasis is on choosing and diagnosing complex models rather than treating software settings as a black box. Live Zoom sessions: 30 November; 2, 4, 7, 9 and 11 December, 15:00–18:30, Europe/Madrid (CET, UTC+01:00). Total 35 hours: 21 live plus 14 independent, with Slack-supported tasks. English-language live online course; Fee €680; Ambassador Institution rate €544. VAT included. Capacity: 20. A Biology degree, or palaeontological background, and familiarity with Bayesian phylogenetics are expected; an introductory video series can supply the prerequisite. Registration requires acceptance; payment follows confirmation. Recordings are provided, but live attendance is required for a certificate.

Estimate species occurrence while accounting for imperfect detection. Participants prepare repeated survey histories, distinguish occupancy from observation processes and fit single-season models with ecological and survey covariates. Exercises compare two-stage estimation in tsoccu with likelihood-based analysis in unmarked, then develop predictions, uncertainty estimates, diagnostics and reproducible reporting. The final day introduces dynamic, multispecies, spatial and false-positive extensions through ecological applications. Sessions: 1–4 December, 09:00–13:00 daily, Europe/Berlin (CET, UTC+01:00). Live online training; fee €420. No prior occupancy-modelling experience is required; practical work is in R.

Age, calendar period, and birth cohort can be difficult to separate when studying change over time. This workshop introduces approaches to their analysis in longitudinal aging research and explains how research design and data structure affect interpretation. It covers accelerated longitudinal panels, hierarchical age-period-cohort growth models, coordinated analysis across studies, synthesized cohort-sequential designs, and study heterogeneity. Empirical examples connect these methods to practical research questions. Interactive lectures, polls, breakout discussions, and moderated questions support applying the methods to participants’ studies. The session builds on the society’s 2025 coordinated-data-analysis workshop. Live online on 2026-12-08, 12:00–14:00 EST (America/New_York). Registration requires a GSA member or non-member account; the fee is US$15 for members or US$25 for non-members. Annual Scientific Meeting attendance is not required.

Solve shape-analysis problems in R using measurements, landmarks and outlines. The programme develops data organisation and image processing before geometric transformations, alternative superimposition methods and Procrustes analysis. Participants visualise shape differences, test allometry and multivariate hypotheses, and compare principal-component, discriminant and regression approaches. Outline analysis introduces several Fourier representations and their statistical interpretation; participants can bring their own morphological datasets. Live sessions: 10, 11, 14, 15, 16, 17 and 18 December, 12:00–17:00, Europe/Madrid (CET, UTC+01:00). Total 40 hours: 35 live plus five independent. English-language live online course; Fee €495 until 31 October, then €565; Ambassador rates €396/€452. VAT included. Capacity: 16. Basic R, multivariate statistics and geometric morphometrics are recommended; this is not an introductory morphometrics course. Registration requires acceptance; payment follows confirmation. Recordings are provided, but live attendance is required for a certificate.

Use existing evolutionary trees to investigate ancestral traits, geographic histories and diversification. Participants edit, plot and time-scale phylogenies, reconstruct discrete and continuous characters, assess phylogenetic signal and compare models of trait evolution. Practical work introduces comparative methods, lineage-through-time plots and variation in diversification rates through Mesquite, FigTree and R packages. The course addresses dependence among species and evolutionary processes over deep time; reconstructing trees is outside its scope. Live sessions: 11, 13, 14, 18 and 20 January, 10:00–14:00, Europe/Madrid (CET, UTC+01:00). Total 40 hours: 20 live plus 20 recorded lessons and assignments. English-language live online course; Fee €470 until 31 October, then €584; Ambassador rates €376/€467.20. VAT included. Capacity: 16. For Biology graduates and palaeontologists; preparatory recorded R lessons are provided. Registration requires acceptance; payment follows confirmation. Recordings are provided, but live attendance is required for a certificate.

Quantify biological shape variation and its covariation with the physical environment through geometric morphometrics. This introductory workshop develops landmark and semilandmark configurations, data acquisition and digitisation, Generalized Procrustes Analysis and shape space. Participants compare alternative alignment methods, visualise thin-plate-spline deformations in two and three dimensions, and use principal components, multivariate regression and allometry to interpret variation. Practical work uses TPS software and MorphoJ, with emphasis on interpreting biological questions through geometric and statistical methods. 14, 15, 18, 19 and 20 January 2027, each 14:00–19:00, Europe/Madrid (CET, UTC+1); 25 live hours. English; intended for graduates and researchers in biological sciences and related disciplines. No previous morphometrics experience required; familiarity with multivariate statistics recommended. Eighteen places. EUR 476 through 31 October 2026, then EUR 580; Ambassador Institution fees EUR 380.80 and EUR 464, respectively. Live attendance is required for the certificate. Payment follows acceptance; advertised fees include VAT.

Learn to quantify and interpret biological form using two- and three-dimensional geometric morphometrics. Lectures and practical sessions cover landmark, semilandmark, outline and surface data, digitisation methods, study design and data-quality checks. Generalized Procrustes Analysis provides the foundation for principal components, group comparisons, linear models and allometry. Further sessions address symmetric structures and the possibilities and limitations of machine-assisted data acquisition. Participants assemble a basic analysis workflow and can discuss their own datasets. Teaching emphasises accessible graphical software, with R implementations introduced according to participants’ interests. 25–27 January 2027, daily 13:00–19:00 Europe/Berlin (CET, UTC+1). Live online, taught in English. Fee EUR 330 for this three-day course. Intended for researchers beginning geometric morphometrics or seeking a more structured foundation.

Work through mass-spectrometry proteomics analysis in R and Bioconductor, from raw observations to statistical interpretation. Practical tutorials cover data formats, extraction and visualization, integration of identification results, quantitative data structures and preprocessing. The final session uses statistical analysis to identify proteins with different abundance between experimental groups. 2027-01-27–2027-01-29, 14:00–20:00 daily, Europe/Berlin (CET, UTC+01:00). Online course; fee €380. Working R knowledge is required. Mass-spectrometry or proteomics familiarity is useful but not essential; typical datasets and instruments are introduced during the course.

Researchers learn the assumptions and implementation principles behind machine-learning models used in cancer genomics. The five-day programme develops hidden Markov approaches for copy-number inference, mixture models for tumour subclones, hierarchical models for mutation signals and autoencoders for single-cell data. Lectures alternate with discussion, coding practice and a final project, emphasizing inference of latent biological states from noisy or incomplete measurements. Participants may discuss suitable datasets they own. Online teaching runs 14:00–19:00, Monday through Friday.

Combine complementary omics measurements to identify biological pathways and model cellular behaviour. Lectures and practicals compare supervised feature selection and integration using LASSO, PLS, LDA, mixOmics and DIABLO with unsupervised MOFA models and deep-learning autoencoders. The final day covers dimensionality reduction, graph-based integration, batch correction and multimodal single-cell analysis using Seurat and weighted nearest neighbours. Participants learn to select and implement an integration strategy suited to their research question. Live online, 15–17 February, 14:00–19:00 Europe/Berlin (CET). Fee €450. Basic Unix awareness and beginner-level R or Python are expected.