Recursive Consensus Clustering

RCC installation command
devtools::install_github(“MSCTR/RecursiveConsensusClustering”)

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

FTP or google link for input files *

The link should have expression matrix file and optionally annotation file

Type of dataset *

Bulk RNA Seq data
Single cell RNA Seq data

Minimum number of samples *

The minimum number of samples required to further cluster the dataset

Minimum line length

Minimum slope threshold

The minimum allowed value for slopes in order to select the optimal k at each level. The input value should be in radian

Percentage of genes to be selected for clustering

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

Minimum percentage of genes which are differentially expressed

The minimum percentage of genes to be differentially expressed in order to consider clustering the data

Email *

Name

Call Us: +91 80 7122 2351

Email Us: msctr@ms-mf.org