A bioinformatic study of Single-Cell RNA-seq data analysis protocols for the characterization of cell types of the central nervous system
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
Otros trabajos en la misma sesión
Últimas noticias
-
04/07/19
Programa científico completo disponible -
31/05/19
Convocado Premio INE 2019 -
13/04/19
Inscripción ya abierta