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GiniClust2 is a clustering algorithm for the simultaneous detection of common and rare cell types from single-cell gene expression data. It uses a novel cluster-aware weighted consensus clustering algorithm to combine GiniClust and Fano-based k-means clustering results, by maximizing the strengths of these individual clustering methods in detecting rare and common clusters, respectively.

GiniClust2 is written in the R programming language. To use, please download the GiniClust2_download folder and follow the instructions provided in the Reference_Manual.pdf.

The following additional folders are included, which contain GiniClust2 applications to several data sets:

• Main: This folder contains the following main scripts for running GiniClust2 on four simulated and real data sets:

  • Main_Simulation.R: Data simulated from intestinal cells [1], to create both common and rare cell types.
  • Main_10X_subsample.R: Data subsampled at various proportions from 10X PBMC data [2], to create 140 datasets with cell types of varying rarity.
  • Main_inDrop_day4.R: inDrop data from mouse embryonic stem cells 4 days post-LIF [3].
  • Main_10X_full.R: The full 68k-cell 10X PBMC data set [2].

• Proj: This folder contains four subfolders for each of the data sets mentioned above. Each of these contain data, results, and figures from the GiniClust2 analyses.

• Rfunction: This folder contains the R scripts for GiniClust2, called in the Main files.

Tsoucas D, Yuan GC. GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection. Genome Biol. 2018 May 10;19(1):58