attract

attract identifies gene expression modules that act as drivers in Kauffman's attractor landscape to characterize transcriptionally coordinated pathways underlying cell fate transitions.

Key Features:

• Gene Expression Modules: Identifies modules representing drivers of Kauffman's attractor landscape that capture coordinated changes in gene expression.
• Core Attractor Pathways: Extracts pathways that discriminate between cell types by highlighting transcriptionally coordinated differences.
• Synexpression Groups: Delineates synexpression groups within pathways that exhibit coordinated expression dynamics.
• Dynamic Network Models: Represents and analyzes complex biological networks to facilitate creation of dynamic network models for processes and pathways.
• Gene Expression State Space Trajectories: Utilizes genome-wide gene expression state space trajectories to capture cell fate transitions.
• Functional Enrichment Analyses: Applies functional enrichment analyses to validate core and transient processes implicated in transitions.

Scientific Applications:

• Cell Fate Transitions: Studies cell fate transitions, including differentiation of promyelocytes into neutrophil-like cells, by analyzing gene expression datasets.
• Transcriptional Regulatory Programs: Characterizes transcriptional regulatory programs underlying phenotypic changes and coordinated expression patterns.
• Validation of Hypotheses: Validates hypotheses about core and transient processes in cell differentiation using trajectory analyses and enrichment results.

Methodology:

Identification of gene expression modules; extraction of core attractor pathways and synexpression groups; representation and analysis of networks as dynamic models; analysis of genome-wide gene expression state space trajectories; and application of functional enrichment analyses.