Signature Evaluation Tool (SET): a Java-based tool to evaluate and visualize the sample discrimination abilities of gene expression signatures

BackgroundThe identification of specific gene expression signature for distinguishing sample groups is a dominant field in cancer research. Although a number of tools have been developed to identify optimal gene expression signatures, the number of signature genes obtained is often overly large to be applied clinically. Furthermore, experimental verification is sometimes limited by the availability of wet-lab materials such as antibodies and reagents. A tool to evaluate the discrimination power of candidate genes is therefore in high demand by clinical researchers.ResultsSignature Evaluation Tool (SET) is a Java-based tool adopting the Golub's weighted voting algorithm as well as incorporating the visual presentation of prediction strength for each array sample. SET provides a flexible and easy-to-follow platform to evaluate the discrimination power of a gene signature. Here, we demonstrated the application of SET for several purposes: (1) for signatures consisting of a large number of genes, SET offers the ability to rapidly narrow down the number of genes; (2) for a given signature (from third party analyses or user-defined), SET can re-evaluate and re-adjust its discrimination power by selecting/de-selecting genes repeatedly; (3) for multiple microarray datasets, SET can evaluate the classification capability of a signature among datasets; and (4) by providing a module to visualize the prediction strength for each sample, SET allows users to re-evaluate the discrimination power on mis-grouped or less-certain samples. Information obtained from the above applications could be useful in prognostic analyses or clinical management decisions.ConclusionHere we present SET to evaluate and visualize the sample-discrimination ability of a given gene expression signature. This tool provides a filtration function for signature identification and lies between clinical analyses and class prediction (or feature selection) tools. The simplicity, flexibility and brevity of SET could make it an invaluable tool for marker identification in clinical research.

[1]  Li Liu,et al.  Improved breast cancer prognosis through the combination of clinical and genetic markers , 2007, Bioinform..

[2]  Chi-Hung Lin,et al.  ArrayFusion: a web application for multi-dimensional analysis of CGH, SNP and microarray data , 2006, Bioinform..

[3]  T. Yeatman,et al.  Osteopontin and colon cancer progression , 2004, Clinical & Experimental Metastasis.

[4]  Chris H. Q. Ding,et al.  Minimum redundancy feature selection from microarray gene expression data , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.

[5]  N. Hanna.  A Five-Gene Signature and Clinical Outcome in Non–Small-Cell Lung Cancer , 2008 .

[6]  Joaquín Dopazo,et al.  Prophet, a web-based tool for class prediction using microarray data , 2007, Bioinform..

[7]  Kathleen Marchal,et al.  M@cbeth: a Microarray Classification Benchmarking Tool , 2005 .

[8]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[9]  E. Kruithof,et al.  Modulation of the plasminogen activation system by inflammatory cytokines in human colon carcinoma cells. , 1996, British Journal of Cancer.

[10]  J. Mesirov,et al.  GenePattern 2.0 , 2006, Nature Genetics.

[11]  J. Stuart Aitken,et al.  Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes , 2005, BMC Bioinformatics.

[12]  Sayan Mukherjee,et al.  Molecular classification of multiple tumor types , 2001, ISMB.

[13]  D Timmerman,et al.  Predicting the clinical behavior of ovarian cancer from gene expression profiles , 2005, International Journal of Gynecologic Cancer.

[14]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[15]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[16]  Richard M. Simon,et al.  A Paradigm for Class Prediction Using Gene Expression Profiles , 2003, J. Comput. Biol..

[17]  Vladimir Pavlovic,et al.  RankGene: identification of diagnostic genes based on expression data , 2003, Bioinform..

[18]  Jeremy J. W. Chen,et al.  A five-gene signature and clinical outcome in non-small-cell lung cancer. , 2007, The New England journal of medicine.

[19]  Bonnie LaFleur,et al.  Expression profiling of medulloblastoma: PDGFRA and the RAS/MAPK pathway as therapeutic targets for metastatic disease , 2001, Nature Genetics.

[20]  R. Verhaak,et al.  Prognostically useful gene-expression profiles in acute myeloid leukemia. , 2004, The New England journal of medicine.

[21]  X. Wang,et al.  Predicting hepatitis B virus–positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning , 2003, Nature Medicine.

[22]  M. Radmacher,et al.  Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. , 2003, Journal of the National Cancer Institute.

[23]  K. Deb,et al.  Reliable classification of two-class cancer data using evolutionary algorithms. , 2003, Bio Systems.

[24]  Jill P. Mesirov,et al.  GeneCluster 2.0: an advanced toolset for bioarray analysis , 2004, Bioinform..

[25]  S. Henderson,et al.  Kaposi sarcoma herpesvirus–induced cellular reprogramming contributes to the lymphatic endothelial gene expression in Kaposi sarcoma , 2004, Nature Genetics.

[26]  S. Elledge,et al.  Expression profiling of medulloblastoma: PDGFRA and the RAS/MAPK pathway as therapeutic targets for metastatic disease , 2003, Nature Genetics.

[27]  E. Lander,et al.  A molecular signature of metastasis in primary solid tumors , 2003, Nature Genetics.

[28]  Fillia Makedon,et al.  HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data , 2005, Bioinform..

[29]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.