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.


Sanger Institute Contributors

Photo of Dr Martin Hemberg, PhD

Dr Martin Hemberg, PhD

CDF Group Leader

Photo of Dr Vladimir Kiselev

Dr Vladimir Kiselev

Cellular Genetics Informatics Team Leader