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Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer

Overview of attention for article published in BMC Genomics, January 2018
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Title
Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer
Published in
BMC Genomics, January 2018
DOI 10.1186/s12864-018-4446-y
Pubmed ID
Authors

Qingzhou Guan, Haidan Yan, Yanhua Chen, Baotong Zheng, Hao Cai, Jun He, Kai Song, You Guo, Lu Ao, Huaping Liu, Wenyuan Zhao, Xianlong Wang, Zheng Guo

Abstract

Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences with the non-cancer states in the gene expression pattern. Therefore, it is reasonable to explore the power of qualitative diagnostic signatures which are robust against experimental batch effects and other random factors. Firstly, using data of technical replicate samples from the MicroArray Quality Control (MAQC) project, we demonstrated that the low-throughput PCR-based technologies also exist large measurement variations for gene expression even when the samples were measured in the same test site. Then, we demonstrated the critical limitation of low stability for classifiers based on quantitative transcriptional signatures in applications to individual samples through a case study using a support vector machine and a naïve Bayesian classifier to discriminate colorectal cancer tissues from normal tissues. To address this problem, we identified a signature consisting of three gene pairs for discriminating colorectal cancer tissues from non-cancer (normal and inflammatory bowel disease) tissues based on within-sample relative expression orderings (REOs) of these gene pairs. The signature was well verified using 22 independent datasets measured by different microarray and RNA_seq platforms, obviating the need of inter-sample data normalization. Subtle quantitative information of gene expression measurements tends to be unstable under current technical conditions, which will introduce uncertainty to clinical applications of the quantitative transcriptional diagnostic signatures. For diagnosis of disease states with qualitative transcriptional characteristics, the qualitative REO-based signatures could be robustly applied to individual samples measured by different platforms.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 17%
Student > Ph. D. Student 3 13%
Student > Master 3 13%
Lecturer 1 4%
Professor 1 4%
Other 3 13%
Unknown 8 35%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 26%
Computer Science 3 13%
Medicine and Dentistry 3 13%
Nursing and Health Professions 1 4%
Psychology 1 4%
Other 1 4%
Unknown 8 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 February 2018.
All research outputs
#20,462,806
of 23,020,670 outputs
Outputs from BMC Genomics
#9,326
of 10,698 outputs
Outputs of similar age
#378,905
of 441,601 outputs
Outputs of similar age from BMC Genomics
#187
of 207 outputs
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