A. García Galindo, O. González Velasco, J. M. Sánchez Santos, J. De Las Rivas Sanz, E. Sánchez Luis
Modern single-cell sequencing techniques serve to obtain individualized information from each cell, better visualization of cell differences and better understanding of the function of a cell in the context of its microenvironment. The sequencing of single-cell RNA (scRNA-seq) allows us to study new biological questions, in which the specific changes of the cells in the transcriptome are important, e.g., identification of the cell type, heterogeneity of the cellular responses or stochasticity of gene expression. There are several protocols for the bioinformatic analysis of scRNA-seq data (Hubert, Seurat, SmartSeq2, CellSeq, DropSeq…). Following a common workflow to several of them (i.e. quality control, signal normalization/quantification, dimensional reduction, classification of samples and features, identification of cell subpopulations and DE genes) we have elaborated a procedure that we applied to the analysis of a scRNA-seq dataset of cells from the human central nervous system.
Palabras clave: classification, clustering, PCA, single-cell, scRNA-seq
Programado
PO-1 Sesión Pósters
4 de septiembre de 2019 10:40
Sala Multiusos. Edificio Carbonell