the course aims to introduce transcriptomics data (bulk, single-cell, and spatial transcriptomics) and the inferential techniques used to analyze them. The course will be mainly theoretical and will be complemented by several hands-on laboratories using the R software, in which transcriptomics data will be analyzed and the methods learned during the course will be applied.

Guest Lecture. Simone Tiber, University of Bologna The instructor is a statistician with extensive experience in developing statistical methods for the analysis of omics data. In particular, they are the author of five R packages (BANDITS, distinct, DifferentialRegulation, DESpace, and IsoBayes), which collectively recorded approximately 16,800 downloads between January and September 2025 (source: Bioconductor). Introduction to basic biological concepts (transcription, splicing, and translation), and to bulk, single-cell and spatial transcriptomics data. Pre-processing: alignment of RNA-sequencing reads, quantification of gene and transcript abundance, and data normalization. Exploratory plots: correlation plot, heatmap (i.e., hierarchical clustering of samples and genes), low dimensional representations (PCA and MDS plots), and identification of outliers. Models for gene expression data: the negative binomial, and the dirichlet multinomial. Analyses of transcriptomics data: differential gene expression, differential alternative splicing, eQTL and sQTL, pathway analyses, clustering of cells (from single-cell data), spatial clustering of spots (from spatial data), and identification of spatially variable genes. Visual comparison of models’ performance, via ROC curves, TPR vs. FDR plots, and the Venn diagram. Application, on real data, of the methods studied, via the R software language.

In presenza

Prova di laboratorio di natura pratica sperimentale o informatico

P.D.2-2 - Marzo