Package: survClust 0.99.8

Arshi Arora

survClust: Identification Of Clinically Relevant Genomic Subtypes Using Outcome Weighted Learning

survClust is an outcome weighted integrative clustering algorithm used to classify multi-omic samples on their available time to event information. The resulting clusters are cross-validated to avoid over overfitting and output classification of samples that are molecularly distinct and clinically meaningful. It takes in binary (mutation) as well as continuous data (other omic types).

Authors:Arshi Arora [aut, cre]

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survClust.pdf |survClust.html✨
survClust/json (API)
NEWS

# Install 'survClust' in R:

install.packages('survClust', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/arorarshi/survclust/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

simdat - Simulated dataset with 3-class solution
simsurvdat - Simulated survival dataset with accompanying 'simdat'
uvm_dat - TCGA UVM Mutation and Copy Number datasets
uvm_survClust_cv.fit - SurvClust cv.survclust output of integrated TCGA UVM Mutation and Copy Number datasets.
uvm_survdat - TCGA UVM Clinical file

On BioConductor:survClust-0.99.8(bioc 3.20)

bioconductor-package

8 exports 0.09 score 53 dependencies

Last updated 3 months agofrom:2ebe0fe7d8

Exports:combineDist cv_survclust cv_voting dist_wtbinary getDist getStats plotStats survClust

Dependencies:abind askpass Biobase BiocBaseUtils BiocGenerics cli cpp11 crayon curl DelayedArray dplyr fansi generics GenomeInfoDb GenomeInfoDbData GenomicRanges glue httr IRanges jsonlite lattice lifecycle magrittr Matrix MatrixGenerics matrixStats mime MultiAssayExperiment openssl pdist pillar pkgconfig purrr R6 Rcpp rlang S4Arrays S4Vectors SparseArray stringi stringr SummarizedExperiment survival sys tibble tidyr tidyselect UCSC.utils utf8 vctrs withr XVector zlibbioc

An introduction to survClust package

Arshi Arora <br> Department of Epidemiology and Biostatistics, MSKCC

Rendered fromsurvClust_vignette.Rmdusingknitr::rmarkdownon Jun 21 2024.

Last update: 2024-04-16
Started: 2024-04-16

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Integrates weighted distance matrices	combineDist
performs cross validation on supervised clustering, 'survClust' for a particular 'k'. 'cv_survclust' runs	cv_survclust
For a 'survClust' fit, return consolidated labels across rounds of cross validation for a specific 'k'. Note that cv.fit already has consolidated class labels across folds	cv_voting
Calculates weighted distance matrix of multiple genomic data types	getDist
Compute fit statistics after cross validation via 'cv_survclust'	getStats
Plot the output from 'getStats'	plotStats
Simulated dataset with 3-class solution	simdat
Simulated survival dataset with accompanying 'simdat'	simsurvdat
perform supervised clustering for a particular 'k'	survClust-package survClust
TCGA UVM Mutation and Copy Number datasets	uvm_dat
survClust cv.survclust output of integrated TCGA UVM Mutation and Copy Number datasets.	uvm_survClust_cv.fit
TCGA UVM Clinical file	uvm_survdat