Sparse Multiple Canonical Correlation Network Analysis Tool
Aggregate and Save Cross-validation Result for Single-omics Analysis
Evaluation of Binary Classifier with Different Evaluation Metrics
preprocess a omics dataset before running omics SmCCNet
Automated SmCCNet to Streamline the SmCCNet Pipeline
Calculate similarity matrix based on canonical weights.
Canonical Correlation Value for SmCCA
Get Canonical Weight SmCCA Algorithm (No Subsampling)
Internal functions called by getRobustPseudoWeights_single.
Extract Omics Modules based on Similarity Matrix.
Run Sparse multiple Canonical Correlation Analysis and Obtain Canonica...
Run Sparse multiple Canonical Correlation Analysis and Obtain Canonica...
Single-omics SmCCA with Quantitative Phenotype
Single-omics SmCCA with Binary Phenotype
Prunes Subnetwork and Return Final Pruned Subnetwork Module
Pipe operator
Scaling Factor Input Prompt
NetSHy Summarization Score
A synthetic mRNA expression dataset.
A synthetic miRNA expression dataset.
A synthetic phenotype dataset.
A canonical correlation based framework (SmCCNet) designed for the construction of phenotype-specific multi-omics networks. This framework adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. It offers a streamlined setup process that can be tailored manually or configured automatically, ensuring a flexible and user-friendly experience.
Useful links