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Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

Cell Specific Gene Expression

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Updated: Nov 11, 2025

Single-cell Profiling of Developing and Mature Retinal Neurons

Single-cell Profiling of Developing and Mature Retinal Neurons

Published on: April 19, 2012

Nonlinear ridge regression improves cell-type-specific differential expression analysis.

Fumihiko Takeuchi¹, Norihiro Kato²

¹Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine (NCGM), 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan. fumihiko@takeuchi.name.

BMC Bioinformatics

|March 23, 2021

Summary

This summary is machine-generated.

This study introduces nonlinear ridge regression to analyze cell-type-specific omics data from bulk tissues. This method effectively addresses scaling and multicollinearity issues, enabling robust cell-type-specific association tests.

Keywords:

Cell type Differential gene expression analysis Epigenome-wide association study Nonlinear regression Ridge regression eQTL mQTL

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Published on: October 3, 2025

Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

Epigenome-wide association studies (EWAS) and gene expression analyses typically use bulk tissue, masking cell-type-specific effects.
Inferring cell type composition from bulk omics data is possible, but estimating cell-type-specific effects faces challenges like scaling and multicollinearity.

Purpose of the Study:

To develop a statistical method for accurately estimating cell-type-specific omics effects from bulk tissue data.
To overcome limitations of traditional linear regression in analyzing cell-type-specific omics data.

Main Methods:

Applied nonlinear regression to handle different data scales (linear for cell composition, logit/log for omics data).
Utilized ridge regularization to address multicollinearity among interaction terms.
Developed and implemented the 'omicwas' R package for these analyses.

Main Results:

Nonlinear ridge regression demonstrated well-balanced sensitivity, specificity, and precision in simulated data.
The proposed method successfully detected weak signals in real-world omics data.
Outperformed marginal models in precision while maintaining high sensitivity.

Conclusions:

Nonlinear ridge regression provides a robust approach for cell-type-specific association testing on bulk omics data.
The 'omicwas' package facilitates cell-type-specific EWAS, differential gene expression, and QTL analyses.
The software is publicly available for broader research application.