SEA

SEA performs comprehensive analysis of serial transcriptomic data to characterize gene expression dynamics associated with quantitative variables such as time or dosage.

Key Features:

• Serial transcriptomic analysis: Provides comprehensive analysis of serial transcriptomic datasets to study changes across ordered measurements.
• Quantitative-variable modeling: Models gene expression dynamics associated with quantitative variables such as time or dosage.
• Global and gene-specific trend analysis: Facilitates examination and interpretation of both global and gene-specific expression trends.
• Detection of significant changes: Identifies significant changes in expression over serial measurements.
• Profile comparison: Compares various expression profiles across serial measurements.
• Functional association with cellular processes: Evaluates transcriptional alterations in relation to cellular processes.
• Algorithmic approaches: Incorporates a suite of five distinct algorithms leveraging univariate, multivariate, and functional profiling strategies.
• Statistical methodologies: Employs robust statistical methodologies for analysis of serial expression data.

Scientific Applications:

• Time-series transcriptomics: Analysis of gene expression dynamics in time-course experiments.
• Dosage-response studies: Analysis of transcriptional responses across different dosage levels.
• Transcriptional regulation dynamics: Investigation of how genes are differentially expressed over time or under varying conditions.
• Linking expression to cellular processes: Associating expression changes with underlying biological mechanisms and cellular processes in genomics and transcriptomics research.

Methodology:

Implements a suite of five algorithms employing univariate, multivariate, and functional profiling statistical strategies.