phenomis

phenomis performs post-processing and univariate statistical analysis of single and multi-omics data sets within the Bioconductor framework.

Key Features:

• Quality Control Metrics: Computes quality control metrics for omics data assessment.
• Signal Drift and Batch Effect Correction: Corrects signal drift and batch effects in omics data.
• Intensity Transformation: Applies intensity transformation methods to omics data.
• Univariate Hypothesis Testing: Performs univariate hypothesis testing on omics features.
• Clustering: Performs clustering analysis of omics data.
• Metabolomics Annotation: Supports annotation of metabolomics data.
• Bioconductor Format Compatibility: Handles data in SummarizedExperiment, MultiAssayExperiment, ExpressionSet, and MultiDataSet formats.
• Workflow Integration: Methods can be chained as workflows and enriched with ropls and biosigner packages for multivariate analysis and feature selection.
• Data Import and Export: Supports import from and export to text files.

Scientific Applications:

• Metabolomics Data Analysis: Originally targeted to metabolomics data with comprehensive post-processing and statistical analysis capabilities.
• Transcriptomics Data Analysis: Methods are applicable to transcriptomics data sets.
• Proteomics Data Analysis: Methods are applicable to proteomics data sets.
• Multi-Omics Integration: Supports analysis of multi-omics data sets through MultiAssayExperiment format compatibility.

Methodology:

The package implements quality control metrics, signal drift and batch effect correction, intensity transformation, univariate hypothesis testing, clustering, and metabolomics annotation, with methods chainable as workflows using standard Bioconductor data formats.