autonomics

autonomics unifies statistical modeling of omics data across multiple analytical engines, coding systems, model formulae, omics platforms, projection methods, clustering methods, and survival methods.

Key Features:

• Linear Modeling Engines: Provides unified access to lm, lme, lmer, limma, and wilcoxon.
• Coding Systems: Supports treatment, difference, deviation, and other coding systems.
• Model Formulae: Accommodates models with/without intercept, random effect, interaction, or nesting.
• Omics Platforms: Supports microarray, rnaseq, msproteomics, affinity proteomics, and metabolomics data.
• Projection Methods: Implements pca, pls, sma, lda, spls, and opls.
• Clustering Methods: Implements hclust, pam, and cmeans.
• Survival Methods: Implements coxph, survdiff, and coin.
• Enrichment Analysis: Provides a fast enrichment analysis implementation.

Scientific Applications:

• Cross-Platform Omics Analysis: Enables consistent statistical modeling across diverse omics data types including transcriptomics, proteomics, and metabolomics.
• Multivariate Dimension Reduction: Applies projection methods for exploratory data analysis and feature extraction across omics datasets.
• Survival Analysis: Supports survival outcome modeling for clinical and time-to-event omics studies.
• Cluster-Based Discovery: Identifies sample or feature groupings through multiple clustering algorithms.

Methodology:

The package unifies access across linear modeling engines, coding systems, model formulae, omics platforms, projection methods, clustering methods, and survival methods, with a fast enrichment analysis implementation.