Package index • MOFSR

All Functions

All package functions

CV(): Calculate Coefficient of Variation for a Numeric Vector
CV.df(): Calculate Coefficient of Variation for a Data Frame
CalCHI(): Calinski-Harabasz Index Calculation
CalPAC(): Calculate Proportion of Ambiguous Clustering (PAC)
Classifier.Adaboost(): AdaBoost Classifier for Cluster Prediction
Classifier.DT(): Decision Tree Classifier for Cluster Prediction
Classifier.Enet(): Elastic Net Classifier for Cluster Prediction
Classifier.Enrichment(): Enrichment-Based Neural Network Classifier for Cluster Prediction
Classifier.GBDT(): Gradient Boosted Decision Trees (GBDT) Classifier for Cluster Prediction
Classifier.LASSO(): LASSO Classifier for Cluster Prediction
Classifier.LDA(): Linear Discriminant Analysis (LDA) Classifier for Cluster Prediction
Classifier.NBayes(): Naive Bayes Classifier for Cluster Prediction
Classifier.NNet(): Neural Network Classifier for Cluster Prediction
Classifier.PCA(): PCA-Based Neural Network Classifier for Cluster Prediction
Classifier.RF(): Random Forest Classifier for Cluster Prediction
Classifier.Ridge(): Ridge Classifier for Cluster Prediction
Classifier.SVD(): SVD-Based Neural Network Classifier for Cluster Prediction
Classifier.SVM(): Support Vector Machine (SVM) Classifier for Cluster Prediction
Classifier.StepLR(): Stepwise Logistic Regression Classifier for Cluster Prediction
Classifier.XGBoost(): XGBoost Classifier for Cluster Prediction
Classifier.kNN(): k-Nearest Neighbors (kNN) Classifier for Cluster Prediction
Classifier.ssGSEA(): Perform ssGSEA-based Subtyping Using Marker Gene Sets
FeatureSelectionWithBootstrap(): Feature Selection with Bootstrap for Each Cluster
Find.OptClusterFeatures(): Optimal Feature Combination for Multi-Modality Clustering
MAD.df(): Calculate Median Absolute Deviation for a Data Frame
Mean.df(): Calculate Mean Value for a Data Frame
Median.df(): Calculate Median Value for a Data Frame
PathDEA(): Pathway Differential Expression Analysis (PathDEA)
RunBCC(): Run Bayesian Consensus Clustering (BCC)
RunCC(): Run Consensus Clustering
RunCIMLR(): Run Cancer Integrative Multi-kernel Learning (CIMLR)
RunCOCA(): Run Consensus Clustering Analysis (COCA)
RunCPCA(): Run Consensus Principal Component Analysis (CPCA)
RunClassifier(): Run Classifiers for Cluster Prediction
RunEnsemble(): Run Ensemble of Multiple Classifiers for Cluster Prediction
RunGSVA(): Generate Single-Sample Gene-Set Enrichment Score
RunIntNMF(): Run Integrative Non-negative Matrix Factorization (IntNMF)
RunIntegration() RunIF(): Run Multi-Omics Integration Clustering
RunLRAcluster(): Run Low-Rank Approximation Clustering (LRAcluster)
RunMCIA(): Run Multiple Co-Inertia Analysis (MCIA)
RunMOFS(): Run MultiModality Fusion Subtyping (MOFS) for Multi-Modality Data Integration
RunNEMO(): Run Neighborhood-based Multi-Omics clustering (NEMO)
RunPCA(): Run Principal Component Analysis
RunPINSPlus(): Run Perturbation Clustering (PINSPlus)
RunRGCCA(): Run Regularized Generalized Canonical Correlation Analysis (RGCCA)
RunSGCCA(): Run Sparse Generalized Canonical Correlation Analysis (SGCCA)
RunSNF(): Run Similarity Network Fusion (SNF) for Multi-Modality Data Integration
RuniClusterBayes(): Run Bayesian Integrative Clustering (iClusterBayes)
SD.df(): Calculate Standard Deviation for a Data Frame
Select.Features(): Select Hypervariable Features
WangGBM(): Perform ssGSEA-based Subtyping for GBM Samples (Wang et al. 2017)
WuGBM(): Perform ssGSEA-based Subtyping Using Wu et al. 2024 Markers
multi_view_factor_analysis(): Multi-View Factor Analysis
icluster_bayes(): Bayesian Integrative Clustering (iClusterBayes)
.NEMO_NEIGHBORS_RATIO: Neighborhood-based Multi-Omics clustering (NEMO)
snf_affinity_matrix(): Similarity Network Fusion (SNF) Algorithm
align_samples(): Align Samples Across Datasets
bcc_cluster(): BCC Core Algorithm
bcc_cluster_fast(): Fast BCC
calc_chi(): Calculate Calinski-Harabasz Index
calc_pac(): Calculate Proportion of Ambiguous Clustering (PAC)
check_sample_alignment(): Check Sample Alignment
cimlr_cluster(): CIMLR Core Algorithm
cimlr_feature_ranking(): CIMLR Feature Ranking
compare_clusterings(): Create Cluster Comparison Matrix
compute_umap(): UMAP Dimensionality Reduction
consensus_cluster(): Consensus Clustering
correct_batch(): Simple Batch Correction
cpca(): Consensus PCA
filter_by_mad(): Filter by MAD (Median Absolute Deviation)
filter_low_variance(): Filter Low-Variance Features
gene_sets: Functional Gene Sets
get.Jaccard.Distance(): Jaccard Distance Calculation for Binary Matrix
get.binary.clusters(): Get Binary Clusters from Clustering Results
get.class(): Get Cluster Assignments
get_consensus_class(): Get Cluster Assignments from Consensus Results
handle_missing(): Handle Missing Values
icluster_bayes_fast(): Simplified iClusterBayes
init(): Initialize MOFSR Optional Dependencies
intnmf_cluster(): IntNMF Core Algorithm
intnmf_opt_k(): Optimal K Selection for IntNMF
lracluster(): LRAcluster Core Algorithm
mcia(): Multiple Co-Inertia Analysis
minmax(): Minmax Normalization for a Numeric Vector
minmax.df(): Minmax Normalization for a Data Frame
nemo_affinity_graph(): NEMO Affinity Graph Construction
nemo_clustering(): NEMO Clustering
nemo_num_clusters(): Estimate Number of Clusters from Affinity Graph
setup_parallel(): Parallel Computing Support for MOFSR
parallel_bootstrap_features(): Parallel Bootstrap Feature Selection
parallel_consensus_cluster(): Parallel Consensus Clustering
perturbation_clustering(): Perturbation Clustering
plot_algorithm_comparison(): Plot Algorithm Comparison
plot_cluster_quality(): Plot Cluster Quality Metrics
plot_consensus_heatmap(): Plot Consensus Matrix Heatmap
plot_silhouette(): Plot Silhouette Analysis
plot_survival(): Plot Kaplan-Meier Survival Curves
plot_umap(): Plot UMAP with Clusters
normalize_omics(): Data Preprocessing Functions for MOFSR
qc_summary(): Quality Control Summary
rgcca(): Regularized Generalized Canonical Correlation Analysis
list_clustering_algorithms(): Unified Multi-Omics Clustering Interface
run_bcc(): Run BCC Clustering
run_cimlr(): Run CIMLR Clustering
run_cpca(): Run CPCA Clustering
run_iclusterbayes(): Run iClusterBayes Clustering
run_integration(): Run Multi-Omics Integration and Clustering
run_intnmf(): Run IntNMF Clustering
run_late_fusion(): Late Fusion Clustering
run_lracluster(): Run LRAcluster
run_mcia(): Run MCIA Clustering
run_mofa(): Run Factor-Based Clustering
run_multiple_algorithms(): Run Multiple Clustering Algorithms
run_nemo(): Run NEMO Clustering
run_parallel_algorithms(): Run Multiple Algorithms in Parallel
run_pinsplus(): Run PINSPlus Clustering
run_rgcca(): Run RGCCA Clustering
run_sgcca(): Run SGCCA Clustering
run_snf(): Run SNF Clustering
run_wsnf(): Weighted Similarity Network Fusion
sgcca(): Sparse Generalized Canonical Correlation Analysis
snf_fuse(): Similarity Network Fusion
spectral_clustering(): Spectral Clustering
ssMwwGST(): Single-Sample Pathway Activity Analysis Using MWW-GST
stop_parallel(): Stop Parallel Backend
subtyping_omics_data(): Subtyping Multi-Omics Data with PINSPlus