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Updated: Mar 27, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Multiple functional linear model for association analysis of RNA-seq with imaging.

Junhai Jiang1, Nan Lin1, Shicheng Guo2

  • 1Human Genetics Center, Division of Biostatistics, The University of Texas School of Public Health, Houston, TX 77030, USA.

Quantitative Biology (Beijing, China)
|January 12, 2016
PubMed
Summary

This study introduces a new method integrating genomic and imaging data to find disease genes. It successfully identified novel gene associations with imaging variations in ovarian cancer and kidney cancer.

Keywords:
RNA-seqfunctional linear modelfunctional principal component analysisimagingimaging genomics

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Area of Science:

  • Bioinformatics
  • Genomics
  • Medical Imaging

Background:

  • Integrative analysis of genomic and anatomical imaging data is crucial for disease gene discovery but remains underdeveloped.
  • Current methods for analyzing imaging and RNA-seq data often neglect spatial information and gene expression variations at the genomic level.

Purpose of the Study:

  • To develop novel methods for integrating imaging and genomic data to identify disease susceptibility genes.
  • To overcome limitations in existing dimension reduction techniques for imaging and genomic data analysis.

Main Methods:

  • Extended functional principal component analysis (2DFPCA) for imaging data dimension reduction.
  • Developed a multiple functional linear model (MFLM) to associate image functional principal scores with RNA-seq data.
  • Applied the methods to ovarian cancer and kidney renal clear cell carcinoma (KIRC) datasets.

Main Results:

  • Identified 24 genes associated with imaging variations in ovarian cancer and 84 in KIRC.
  • Discovered genes not differentially expressed but with significant morphological and metabolic functions.
  • The MFLM identified splicing sites and multiple gene isoforms based on regression coefficient function peaks.

Conclusions:

  • The developed integrative approach enhances the discovery of disease-related genes by combining genomic and imaging data.
  • The method reveals functional insights into genes associated with imaging phenotypes, including those not detected by traditional differential expression analysis.
  • This approach offers a new avenue for identifying novel disease susceptibility genes and understanding gene expression complexities.