MIND

MIND estimates subject- and cell-type-specific (CTS) gene expression from bulk gene expression data by applying empirical Bayes methods to integrate multiple tissue measurements per individual.

Key Features:

• Empirical Bayes integration: Applies empirical Bayes techniques to integrate multiple measurements from the same tissue per individual.
• Multi-region measurements: Combines measurements across multiple tissue regions per subject, such as various brain regions.
• Subject- and cell-type-specific estimation: Produces subject-level cell type-specific (CTS) gene expression estimates from bulk data.
• Strength borrowing: Borrows statistical strength across repeated tissue measurements to improve precision of CTS estimates.
• CTS eQTL support: Generates outputs suitable for identification of cell type-specific expression Quantitative Trait Loci (CTS eQTL).
• Validation: Validated using brain-region data from the Genotype-Tissue Expression project (GTEx).
• Implementation: Implemented as an R package.

Scientific Applications:

• CTS eQTL discovery: Enables identification of cell type-specific eQTLs to study genetic regulation of gene expression.
• Disease etiology analysis: Supports investigation of subject- and cell-type-specific expression patterns relevant to disease mechanisms.
• Integrative analysis with scRNA-seq: Facilitates reconciliation of bulk RNA sequencing datasets with single-cell RNA-sequencing (scRNA-seq) observations.

Methodology:

Applies empirical Bayes integration of multiple tissue measurements per individual to estimate cell type-specific gene expression and was validated using GTEx brain-region data.