Recursive Consensus Clustering

RCC GitHub

RCC installation command

Datasets with clustering results
Biase dataset
Darmanis dataset
Human tissue dataset
Ivy GAP dataset
Neftel dataset
Pollen dataset
TCGA pan-cancer dataset

Recursive Consensus Clustering (RCC) is a user-friendly algorithm which allows a researcher to find novel and biologically meaningful subtypes in transcriptomic datasets without requiring computational expertise. The recursive clustering of the dataset reveals finer structures in the data leading to identification of novel subtypes

RCC input parameters
The link should have expression matrix file and optionally annotation file
The minimum number of samples required to further cluster the dataset
The minimum allowed value for slopes in order to select the optimal k at each level. The input value should be in radian
The percentage of genes to be used for clustering. The default for single cell data is 1 and for bulk RNA Seq data is 5
The minimum percentage of genes to be differentially expressed in order to consider clustering the data