Identify differentially expressed genes with large background samples

Jennifer Fowler, Jonathan Stubblefield, Jason Causey, Jake Qualls, Wei Dong, Hongmei Jiang, Karl Walker, Yuanfang Guan, Xiuzhen Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


To identify differentially expressed genes related to diseases is important but challenging. The challenges include the inherent noisy nature of the collected data, as well as the imbalance between the very large number of genes and the relatively small number of collected study samples. To address some of these challenges, here we implemented the method of AUCg (Area Under the Curve gene ranking). The novelty of the implementation of AUCg is that it not only utilises the study samples information but also makes good use of the large amount of publicly available gene expression samples as “background”. We applied AUCg to a private dataset of 217 multiple myeloma samples, compared to 36,754 publicly available gene expression samples. The analysis identified genes that could be potentially unique to multiple myeloma. The AUCg gene ranking method can be applied for studying many other cancers and human diseases, taking advantage of large publicly available data.

Original languageEnglish (US)
Pages (from-to)411-428
Number of pages18
JournalInternational Journal of Computational Biology and Drug Design
Issue number6
StatePublished - 2021


  • differentially expressed genes
  • gene expression
  • genes
  • multiple myeloma
  • samples

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications


Dive into the research topics of 'Identify differentially expressed genes with large background samples'. Together they form a unique fingerprint.

Cite this