SAFE-clustering: Single-cell Aggregated (From Ensemble) Clustering for Single-cell RNA-seq Data
Getting Start with SAFE-clustering
To get started, the R package can be downloaded from here, and be installed from the unzipped file following the command:install.packages("local_path/SAFEclustering/", repos = NULL, type = "sources")
Or it can be directly installed from GitHub yycunc/SAFEclustering following the instruction.
After loading the package library, the examples of input expression matrix to SAFEclustering are easily to be set up by the command:
data("data_SAFE")
Note that hypergraph partitioning algorithm (HGPA) is performed using the shmetis program from hMETIS package v. 1.5 (Karypis et al., IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 1999), and meta-clustering algorithm (MCLA) and cluster-based similarity partitioning algorithm (CSPA) are performed using gpmetis program from METIS v. 5.1.0 (Karypis and Kumar, SIAM Journal on Scientific Computing, 1998). Please download the two programs corresponding to the operating systems you are using and put them in the working directory or provide the directory where these two programs are.
Guided Analyses
In this tutorial, we will analyze two datasets: one from Zheng et al. (Nature Communications, 2016) and the other from Biase et al. (Genome Research, 2014). Zheng dataset contains 500 human peripheral blood mononuclear cells (PBMCs) sequenced using GemCode platform, which consists of three cell types, CD56+ natural killer cells, CD19+ B cells and CD4+/CD25+ regulatory T cells. The original data can be downloaded from 10X GENOMICS website. The Biase dataset has 49 mouse embryo cells, which were sequenced by SMART-Seq and can be found at NCBI GEO:GSE57249.A guided walkthrough of the two analyses are provided by R markdown tutorial.