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Introduction\r\n- Single-cell RNA sequencing\r\n- From raw sequencing data to count matrices\r\n- Software tools\r\n\r\nII. Preprocessing of the expression matrix (Theory and Practice)\r\n- Quality control\r\n- Normalization\r\n- Dimensionality reduction (HVG, PCA, UMAP)\r\n- Detection of expression biases\r\n\r\nIII. Annotation (Theory and Practice)\r\n- Clustering\r\n- Marker genes\r\n- Cell type identification\r\n- Analysis of marker gene lists with the R package ClusterProfiler\r\n\r\nIV. Practical Workshop “Bring your own data”\r\n- Semi-autonomous execution of primary analysis on learners’ own data","homepage":"https://pf-bird.univ-nantes.fr/training/singlecell/","is_draft":false,"costs":["Priced"],"topics":[],"keywords":[],"prerequisites":[],"openTo":"Everyone","accessConditions":"1. Good knowledge of R or having completed the training “Bioanalysis Training: Introduction to the R Programming Language.”\r\n\r\n2. 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