eegc

eegc evaluates transcriptome and functional data to assess the fidelity of cellular engineering processes, including differentiation of induced pluripotent stem cells and direct lineage reprogramming.

Key Features:

• Systematic evaluation: Integrates transcriptome profiling with functional and network analyses to provide a systems-level assessment of differentiation and transdifferentiation.
• Transcriptome profiling: Uses high-throughput gene expression data from DNA microarray or RNA sequencing for downstream analysis.
• Differential expression (DE) analysis: Identifies transcriptional differences via DE analysis across pairwise sample comparisons.
• Gene clustering: Clusters genes into categories representing distinct differentiation or cellular engineering states.
• Functional and gene regulatory network analyses: Performs functional analyses and gene regulatory network analyses on gene clusters to highlight pathway- and regulator-level differences and potential shortcomings in reprogramming protocols.
• Bioconductor integration: Distributed as a Bioconductor package under the GNU General Public License.

Scientific Applications:

• Regenerative Medicine: Evaluates transcriptional and functional fidelity of engineered cells to inform optimization of regenerative therapy protocols.
• Cellular Engineering Research: Systematically analyzes and refines experimental protocols for cell differentiation and transdifferentiation to improve reliability and functionality of derived cells.

Methodology:

Performs differential expression analysis of DNA microarray or RNA sequencing data across pairwise comparisons, clusters differentially expressed genes into categories representing distinct cellular engineering states, and conducts functional and gene regulatory network analyses on those clusters.