MIC-SQTL

MIC-SQTL performs multimodal deconvolution of tissue-matched transcriptome and proteome data to quantify cell-type abundance and map cs-protein quantitative trait loci (cspQTLs) linking genetic variants to protein expression.

Key Features:

• Deconvolution Without Reference Panels: MIC-SQTL performs deconvolution without requiring reference profiles from the same molecular source or tissue type.
• Integration of Transcriptome and Proteome Data: MIC-SQTL integrates transcriptomic and proteomic data to identify proteins that contribute to cellular heterogeneity within bulk samples.
• cs-Protein Quantitative Trait Loci (cspQTL) Mapping: MIC-SQTL maps cell-specific protein QTLs to identify genetic variants associated with protein expression in specific cell types.
• Robust Performance Across Datasets: MIC-SQTL has been benchmarked on CITE-seq pseudo-bulk data, simulation studies, and real-world multi-omics datasets from human brain normal tissues and breast cancer tumors.
• Multi-Omics Integrative Visualization: MIC-SQTL provides integrative visualization for multi-omics data to support exploration and interpretation of deconvolution and QTL results.

Scientific Applications:

• Tissue Heterogeneity Studies: MIC-SQTL enables precise deconvolution of bulk samples to resolve cellular composition in heterogeneous tissues.
• Cancer Biology: MIC-SQTL can uncover cell-type-specific molecular signatures and genetic associations in tumors such as breast cancer.
• Neuroscience: MIC-SQTL can dissect cellular heterogeneity and protein-level genetic effects in human brain tissues.
• Developmental Biology: MIC-SQTL can identify cell-type-specific molecular patterns and genotype–phenotype links during development.

Methodology:

MIC-SQTL leverages tissue-matched transcriptome and proteome data to perform multimodal deconvolution without external reference panels, identify proteins driving cellular heterogeneity, quantify cell abundance, and map cs-protein QTLs.