PSEA

PSEA deconvolves bulk gene expression measurements into cell population–specific components using marker-based inference to estimate how individual genes vary across underlying cellular populations and to detect biologically meaningful changes obscured in bulk tissue analyses.

Key Features:

• Marker-based deconvolution: Uses expression signatures of cell type–specific marker genes to attribute bulk expression to underlying cell populations.
• Population-specific expression estimation: Estimates gene-level variation across distinct cellular compartments rather than only in the bulk sample.
• Detection of obscured signals: Identifies transcriptional changes that may be masked in standard bulk differential expression analyses.
• Applicability to heterogeneous tissues: Applies to complex samples such as brain tissue, blood, and solid organs.
• Demonstrated in Huntington’s disease: Revealed myelin-associated transcriptional alterations in HD brain samples not detectable by bulk approaches.
• Application to transplant biology: Identified rejection-associated expression changes attributable to granulocytes in blood from kidney transplant recipients.
• Biomarker and signature refinement: Supports biomarker discovery and refinement of diagnostic or prognostic gene signatures.
• Complementarity with other workflows: Complements bulk and single-cell expression analyses for more precise interpretation of cellular contributions.

Scientific Applications:

• Heterogeneous tissue analysis: Deconvolving bulk transcriptomes from brain, blood, and solid organs to resolve cell-type contributions.
• Disease-specific transcriptional profiling: Detecting cell-subset–specific changes in conditions such as Huntington’s disease (myelin-associated alterations).
• Transplant rejection analysis: Attributing rejection-associated expression changes in kidney transplant recipient blood to granulocytes.
• Biomarker discovery: Identifying cell-type–specific biomarkers that are obscured in bulk measurements.
• Mechanistic interpretation and signature refinement: Enabling mechanistic insight into disease processes and refinement of diagnostic or prognostic gene signatures.

Methodology:

Performs marker-based inference by leveraging expression signatures of cell type–specific marker genes to deconvolve bulk gene expression measurements and estimate gene expression variability across cellular populations.