Decompose given hierarchical clustering tree into non-overlapping clusters in a semi-supervised way by using available patients follow-up information as guidance. Contains functions for snipping HC tree, various cluster quality evaluation criteria, assigning new patients to one of the two given HC trees, testing the significance of clusters with permutation argument and clusters visualization using sample's molecular entropy.
|Date of publication||None|
|Maintainer||Askar Obulkasim <email@example.com>|
|License||GPL (>= 2)|
BullingerLeukemia: Leukemia data
cluster_pred: Semi-supervised clustering
EnvioPlot: Visualize cluster's molecular entropy by violin plot
HCsnip-package: Semi-supervised adaptive-height snipping of the Hierarchical...
HCsnipper: HC tree snipper
measure: Evaluate cluster quality
perm_test: A function to select an optimal partition (clustering) from...
RSF_eval: Function to calculate error rate using the Random Survival...
surv_measure: Cluster quality evaluation using follow-up data
TcgaGBM: Glioblastoma multiforme gene expression data
TwoHC_assign: Function to assign new samples to one of the two given...
TwoHC_perm: Function to assess the significance of group assignmetn from...