Mathematical algorithm for discovering states of expression from direct genetic comparison by microarrays.

Highly specific direct genome-scale expression discovery from two biological samples facilitates functional discovery of molecular systems. Here, expression data from cDNA arrays are ranked and curve-fitted. The algorithm uses filters based on the derivatives (slopes) of the curve fits. The rules are set to (i) filter the largest number of artifactual ratios from same-to-same datasets and (ii) maximize discovery from direct comparisons of different samples. The unsupervised discovery is optimized without lowering specificity. The false discovery rates are significantly lower than other methods. The discovered states of genetic expression facilitate functional discovery and are validated by real-time RT-PCR. Better quality improves sensitivity.

[1]  H. Fathallah-Shaykh Darts in the dark cure animal, but not human, brain tumors. , 2002, Archives of neurology.

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

[3]  John Quackenbush,et al.  Microarray gene expression data analysis - a beginner's guide , 2003 .

[4]  John A. Swets,et al.  Evaluation of diagnostic systems : methods from signal detection theory , 1982 .

[5]  N. Obuchowski Receiver operating characteristic curves and their use in radiology. , 2003, Radiology.

[6]  Jerry Li,et al.  Within the fold: assessing differential expression measures and reproducibility in microarray assays , 2002, Genome Biology.

[7]  J. Swets ROC analysis applied to the evaluation of medical imaging techniques. , 1979, Investigative radiology.

[8]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[9]  Michael R. Green,et al.  Dissecting the Regulatory Circuitry of a Eukaryotic Genome , 1998, Cell.

[10]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[11]  Christian A. Rees,et al.  Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[12]  T. Hughes,et al.  Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles. , 2000, Science.

[13]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[14]  R. Lee Use of microarrays to identify targets in cardiovascular disease. , 2000, Drug news & perspectives.

[15]  P. Brown,et al.  Exploring the metabolic and genetic control of gene expression on a genomic scale. , 1997, Science.

[16]  D. Botstein,et al.  Copyright © American Society for Investigative Pathology Tissue Microarray Validation of Epidermal Growth Factor Receptor and SALL2 in Synovial Sarcoma with Comparison to Tumors of Similar Histology , 2022 .

[17]  L. Hood,et al.  Leroy Hood expounds the principles, practice and future of systems biology. , 2003, Drug discovery today.

[18]  Y. Chen,et al.  Ratio-based decisions and the quantitative analysis of cDNA microarray images. , 1997, Journal of biomedical optics.

[19]  T. Ideker,et al.  A new approach to decoding life: systems biology. , 2001, Annual review of genomics and human genetics.

[20]  S. Dudoit,et al.  Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. , 2002, Nucleic acids research.

[21]  Hassan M Fathallah-Shaykh,et al.  Mathematical modeling of noise and discovery of genetic expression classes in gliomas , 2002, Oncogene.

[22]  Ronald W. Davis,et al.  A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.

[23]  Nirag Jhala,et al.  Diagnostic Markers That Distinguish Colon and Ovarian Adenocarcinomas: Identification by Genomic, Proteomic, and Tissue Array Profiling , 2004 .

[24]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[25]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[26]  L. Hood Systems biology: integrating technology, biology, and computation , 2003, Mechanisms of Ageing and Development.

[27]  S H Kim,et al.  Exploiting chemical libraries, structure, and genomics in the search for kinase inhibitors. , 1998, Science.

[28]  Yudong D. He,et al.  Functional Discovery via a Compendium of Expression Profiles , 2000, Cell.

[29]  Doulaye Dembélé,et al.  Quality indicators increase the reliability of microarray data. , 2002, Genomics.

[30]  John Quackenbush,et al.  Open source software for the analysis of microarray data. , 2003, BioTechniques.

[31]  R. Lempicki,et al.  Evaluation of gene expression measurements from commercial microarray platforms. , 2003, Nucleic acids research.

[32]  Hassan M Fathallah-Shaykh,et al.  Genomic Expression Discovery Predicts Pathways and Opposing Functions behind Phenotypes* , 2003, Journal of Biological Chemistry.

[33]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[34]  D. Lockhart,et al.  Expression monitoring by hybridization to high-density oligonucleotide arrays , 1996, Nature Biotechnology.

[35]  P. Brown,et al.  Drug target validation and identification of secondary drug target effects using DNA microarrays , 1998, Nature Medicine.

[36]  Stephen L Carney,et al.  Leroy Hood expounds the principles, practice and future of systems biology. , 2003 .

[37]  N. Sampas,et al.  Molecular classification of cutaneous malignant melanoma by gene expression profiling , 2000, Nature.

[38]  John Quackenbush Microarray data normalization and transformation , 2002, Nature Genetics.

[39]  A I Saeed,et al.  TM4: a free, open-source system for microarray data management and analysis. , 2003, BioTechniques.

[40]  P. S. Pine,et al.  Dye bias correction in dual-labeled cDNA microarray gene expression measurements. , 2004, Environmental health perspectives.