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Related Experiment Video

Updated: Dec 12, 2025

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

Mapping Tumor-Specific Expression QTLs in Impure Tumor Samples.

Douglas R Wilson¹, Joseph G Ibrahim², Wei Sun³

¹Doug R. Wilson is a graduate student, Department of Biostatistics, UNC Chapel Hill, NC 27599.

Journal of the American Statistical Association

|August 11, 2020

Summary

This summary is machine-generated.

This study introduces a novel computational method for gene expression quantitative trait loci (eQTL) mapping in tumor samples, improving accuracy by distinguishing effects in tumor versus normal cells.

Keywords:

Allele Specific Expression RNA-Seq Tumor Purity eQTL

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Last Updated: Dec 12, 2025

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

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Published on: March 21, 2018

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Gene expression quantitative trait loci (eQTL) analysis is crucial for understanding genetic variant functions.
Existing eQTL mapping methods face challenges with heterogeneous tumor tissues containing both tumor and infiltrating normal cells.
eQTL effects can differ significantly between tumor and normal cell populations within a sample.

Purpose of the Study:

To develop a novel computational method for accurate eQTL mapping in tumor samples.
To differentiate and estimate eQTL effects specifically within tumor cells and infiltrating normal cells.
To compare the performance of the new method against existing approaches.

Main Methods:

Utilized RNA-sequencing (RNA-seq) data from tumor samples.
Developed a method that analyzes both total and allele-specific expression (ASE).
Separately estimated eQTL effects in tumor and infiltrating normal cell components.

Main Results:

The new method demonstrated effective control of type I error rates.
Achieved higher statistical power compared to alternative eQTL mapping methods.
Revealed similarities and differences in eQTL effects between tumor and normal cells using The Cancer Genome Atlas data.

Conclusions:

The developed method provides a robust approach for eQTL analysis in complex tumor microenvironments.
This technique enhances the understanding of genetic variant functions in both cancerous and normal cells within tumors.
The findings contribute to more precise genetic association studies in cancer research.