Package: GSgalgoR 1.23.0

Carlos Catania

GSgalgoR: An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer

A multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The 'Galgo' framework combines the advantages of clustering algorithms for grouping heterogeneous 'omics' data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high.

Authors:Martin Guerrero [aut], Carlos Catania [cre]

GSgalgoR_1.23.0.tar.gz
GSgalgoR_1.23.0.zip(r-4.7-any)GSgalgoR_1.23.0.zip(r-4.6-any)GSgalgoR_1.23.0.zip(r-4.5-any)
GSgalgoR_1.23.0.tgz(r-4.6-any)GSgalgoR_1.23.0.tgz(r-4.5-any)
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GSgalgoR_1.23.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
GSgalgoR/json (API)

# Install 'GSgalgoR' in R:
install.packages('GSgalgoR', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/harpomaxx/gsgalgor/issues

On BioConductor:GSgalgoR-1.23.0(bioc 3.24)GSgalgoR-1.22.0(bioc 3.23)

geneexpressiontranscriptionclusteringclassificationsurvival

5.52 score 15 stars 11 scripts 360 downloads 21 exports 14 dependencies

Last updated from:e07306f1b4. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

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Exports:calculate_distance_euclidean_cpucalculate_distance_pearson_cpucalculate_distance_spearman_cpucalculate_distance_uncentered_cpucallback_base_reportcallback_base_return_popcallback_defaultcallback_no_reportclassify_multiplecluster_algorithmcluster_classifycosine_similaritycreate_centroidsgalgok_centroidsnon_dominated_summaryplot_paretoselect_distancesurv_fitnessto_dataframeto_list

Dependencies:clustercodetoolsdoParallelforeachiteratorslatticematchingRMatrixmconsga2RproxyRcppRcppArmadillosurvival

GSgalgoR user Guide
Overview | Algorithm | Installation | GSgalgoR library | Examples datasets | Examples | Loading data | Data tidying and preparation | Drop duplicates and NA's | Expand probesets that map for multiple genes | Rescale expression matrix | Survival Object | Run galgo() | Setting parameters | Run Galgo algorithm | Galgo Object | Solutions | ParetoFront | to_list() function | to_dataframe() function | plot_pareto() | Case study | Data Preprocessing | Breast cancer classification | Survival of UPP patients | Survival of TRANSBIG patients | Find breast cancer gene signatures with GSgalgoR | Set configuration parameters | Analyzing Galgo results | Pareto front | Summary of the results | Select best performing solutions | Create prototypic centroids | Test Galgo signatures in a test set | Classify train and test set into GSgalgoR subtypes | Calculate train and test set C.Index | Calculate C.Index for training and test set using the prediction models | Evaluate prediction survival of Galgo signatures | Comparison of Galgo vs PAM50 classifier | Session info
Rendered fromGSgalgoR.rmdusingknitr::rmarkdown

Last update: 2021-05-22
Started: 2020-08-03

GSgalgoR Callbacks Mechanism
Introduction | Example 1: A simple custom callback function definition | Example 2: Saving partial population pool using custom callback function | Callbacks implemented in GSgalgoR | Session info
Rendered fromGSgalgoR_callbacks.rmdusingknitr::rmarkdown

Last update: 2020-10-20
Started: 2020-08-03