Inferring Molecular Processes Heterogeneity from Transcriptional Data

Area of Science:

• Transcriptomics
• Computational Biology
• Cell Biology

Background:

• Standard transcriptomic analyses (RNA microarrays, RNA-sequencing) average cellular behavior, potentially masking subpopulation differences.
• Even homogeneous cell lines can contain subpopulations with distinct transcriptomic profiles and regulatory pathways.

Purpose of the Study:

• To develop a novel computational method for inferring the proportions of functionally distinct cell subpopulations within a sample.
• To address internal functional heterogeneity in homogeneous cell lines, including cancer cell lines.

Main Methods:

• A novel computational method was developed to analyze transcriptomic data.
• The method infers the proportion of subpopulations exhibiting varied functional behavior.
• Validation was performed using two RNA microarray datasets focused on cell viability.

Main Results:

• The computational method successfully inferred subpopulation proportions from RNA microarray data.
• The approach demonstrated the presence of functional heterogeneity in tested cell lines.
• The methodology is adaptable to RNA-sequencing data and other molecular processes.

Conclusions:

• The proposed method effectively identifies and quantifies functional heterogeneity in cell populations.
• This approach complements existing transcriptomic analysis tools.
• It holds significant potential for analyzing cancer cell lines treated with drugs or biologically active compounds.