waveTiling

waveTiling performs wavelet-based functional modeling of tiling array transcriptome data to identify expressed regions and differential expression across complex experimental designs, including time-course and multifactorial studies.

Key Features:

• R Bioconductor package: Implemented as an R Bioconductor package for analysis of tiling array data.
• Wavelet-Based Modeling: Uses wavelet transformations and functional models to capture genomic signals across the entire genome from probe-level data.
• Group-Wise Expression Analysis: Identifies regions expressed in specific groups within single-factor studies by modeling expression as functions of genomic position.
• Differential Expression Detection: Detects differentially expressed regions between any two groups based on wavelet-functional analysis of probe signals.
• Multifactorial Design Support: Accommodates multifactorial experimental designs to assess effects of multiple factors on expression profiles.
• Time-Course Experiment Analysis: Identifies linear time effects and circadian rhythms in transcript data from time-course experiments.
• Annotation-Independent Detection: Detects transcriptional effects in exonic, intronic, and intergenic regions without relying on existing annotations by treating expression as continuous functions of genomic position.

Scientific Applications:

• Time-Course Studies: Characterizes dynamic changes in gene expression over time, including identification of linear trends and circadian patterns.
• Comparative Genomics: Detects differentially expressed genomic regions across conditions to support comparative analyses of regulatory and transcriptional differences.
• Multifactorial Experiments: Analyzes interactions and combined effects of multiple biological factors on transcriptome profiles in complex study designs.

Methodology:

Applies wavelet transformations and wavelet-based functional models to tiling array probe expression values treated as continuous functions of genomic position to identify group-wise expression, differential regions between groups, linear time effects, and circadian rhythms while supporting multifactorial designs.