Single-cell Consensus Clustering (SC3)

SC3 is a method for unsupervised clustering of single-cell RNA-seq data. In addition to a graphical user-interface, SC3 provides additional information about potential outliers and marker genes for each cluster.

Different cell-types is one of the most fundamental aspects of multi-cellular organisms. Traditionally, cell-types are defined by morphological properties or surface markers. Single-cell RNA-seq experiments have made a more rigorous approach possible. Given a sample of cells from a tissue, one can use unsupervised clustering to identify groups of cells, i.e. cell-types, with similar expression profiles.

Due to the high levels of noise and the high-dimensionality of the transcriptome, clustering cells remains a challenging task. SC3 is a user-friendly computational tool which allows the user to explore different clustering options. Furthermore, SC3 identifies genes which are specific to each cluster as well as possible outliers. Importantly, SC3 includes a semi-supervised mode which means can be used for large-scale drop-seq experiments.

Downloads

Source code and documentation for SC3 can be found on github.