ArticleBMC bioinformatics2008
Not proper ROC curves as new tool for the analysis of differentially expressed genes in microarray experiments.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Complementary ROC-Derived Indices for Screening Improper Expression Profiles in RNA-Seq Differential Expression Analysis.Balkan medical journal · 2026Article
- Area under the ROC Curve has the most consistent evaluation for binary classification.PloS one · 2024Article
- The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification.BioData mining · 2023Article
- The clinical meaning of the area under a receiver operating characteristic curve for the evaluation of the performance of disease markers.Epidemiology and health · 2022Article
- Prognostic signatures associated with high infiltration of Tregs in bone metastatic prostate cancer.Aging · 2021Article
- Comprehensive genomic and immunophenotypic analysis of CD4 T cell infiltrating human triple-negative breast cancer.Cancer immunology, immunotherapy : CII · 2021Article
- The length of the receiver operating characteristic curve and the two cutoff Youden index within a robust framework for discovery, evaluation, and cutoff estimation in biomarker studies involving improper receiver operating characteristic curves.Statistics in medicine · 2021Article
- An Ensemble Feature Selection Method for Biomarker Discovery.Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology · 2017Article
- Arrow plot: a new graphical tool for selecting up and down regulated genes and genes differentially expressed on sample subgroups.BMC bioinformatics · 2012Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
unlabelledMost microarray experiments are carried out with the purpose of identifying genes whose expression varies in relation with specific conditions or in response to environmental stimuli. In such studies, genes showing similar mean expression values between two or more groups are considered as not differentially expressed, even if hidden subclasses with different expression values may exist. In this paper we propose a new method for identifying differentially expressed genes, based on the area between the ROC curve and the rising diagonal (ABCR). ABCR represents a more general approach than the standard area under the ROC curve (AUC), because it can identify both proper (i.e., concave) and not proper ROC curves (NPRC). In particular, NPRC may correspond to those genes that tend to escape standard selection methods.
resultsWe assessed the performance of our method using data from a publicly available database of 4026 genes, including 14 normal B cell samples (NBC) and 20 heterogeneous lymphomas (namely: 9 follicular lymphomas and 11 chronic lymphocytic leukemias). Moreover, NBC also included two sub-classes, i.e., 6 heavily stimulated and 8 slightly or not stimulated samples. We identified 1607 differentially expressed genes with an estimated False Discovery Rate of 15%. Among them, 16 corresponded to NPRC and all escaped standard selection procedures based on AUC and t statistics. Moreover, a simple inspection to the shape of such plots allowed to identify the two subclasses in either one class in 13 cases (81%).
conclusionNPRC represent a new useful tool for the analysis of microarray data.
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