Analyze single-cell RNA-Seq data using normalization, dimensionality reduction, clustering and differential expression.
Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecedented opportunity to investigate fundamental biological questions at the cellular level, such as stem cell differentiation or the discovery and characterization of rare cell types. The majority of the computational methods to analyze single-cell RNA-Seq data are implemented in R making it a natural tool to start working with single-cell transcriptomic data. Using real single-cell datasets, this course provides a step-by-step tutorial to the methodology and associated R packages for the following four main tasks: (1) normalization, (2) dimensionality reduction, (3) clustering, (4) differential expression analysis.
Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecedented opportunity to investigate fundamental biological questions at the cellular level, such as stem cell differentiation or the discovery and characterization of rare cell types. The majority of the computational methods to analyze single-cell RNA-Seq data are implemented in R making it a natural tool to start working with single-cell transcriptomic data. Using real single-cell datasets, this course provides a step-by-step tutorial to the methodology and associated R packages for the following four main tasks: (1) normalization, (2) dimensionality reduction, (3) clustering, (4) differential expression analysis.
