Identify differentially expressed genes with large background samples
by Jennifer Fowler; Jonathan Stubblefield; Jason Causey; Jake Qualls; Wei Dong; Hongmei Jiang; Karl Walker; Yuanfang Guan; Xiuzhen Huang
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 14, No. 6, 2021

Abstract: 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.

Online publication date: Mon, 21-Mar-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Biology and Drug Design (IJCBDD):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com