Package: survClust 1.7.0

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]

survClust_1.7.0.tar.gz
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survClust_1.7.0.tgz(r-4.6-x86_64)survClust_1.7.0.tgz(r-4.6-arm64)survClust_1.7.0.tgz(r-4.5-x86_64)survClust_1.7.0.tgz(r-4.5-arm64)
survClust_1.7.0.tar.gz(r-4.7-arm64)survClust_1.7.0.tar.gz(r-4.7-x86_64)survClust_1.7.0.tar.gz(r-4.6-arm64)survClust_1.7.0.tar.gz(r-4.6-x86_64)
survClust_1.7.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
survClust/json (API)

# 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-1.7.0(bioc 3.24)survClust-1.6.0(bioc 3.23)

software clustering survival classification cpp

4.76 score 16 stars 18 scripts 8 exports 41 dependencies

Last updated from:7ea5c157fb. Checks:1 NOTE, 11 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	168
linux-devel-arm64	WARNING	255
linux-devel-x86_64	WARNING	290
source / vignettes	OK	375
linux-release-arm64	WARNING	242
linux-release-x86_64	WARNING	336
macos-release-arm64	WARNING	141
macos-release-x86_64	WARNING	312
macos-oldrel-arm64	WARNING	147
macos-oldrel-x86_64	WARNING	508
windows-devel	WARNING	233
windows-release	WARNING	281
windows-oldrel	WARNING	211
wasm-release	OK	165

Exports:combineDist cv_survclust cv_voting dist_wtbinary getDist getStats plotStats survClust

Dependencies:abind Biobase BiocBaseUtils BiocGenerics cli cpp11 DelayedArray dplyr generics GenomicRanges glue IRanges lattice lifecycle magrittr Matrix MatrixGenerics matrixStats MultiAssayExperiment pdist pillar pkgconfig purrr R6 Rcpp rlang S4Arrays S4Vectors Seqinfo SparseArray stringi stringr SummarizedExperiment survival tibble tidyr tidyselect utf8 vctrs withr XVector

An introduction to survClust package

Rendered fromsurvClust_vignette.Rmdusingknitr::rmarkdown

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