Literature DB >> 12620398

Receiver operating characteristic analysis: a general tool for DNA array data filtration and performance estimation.

Nikolai N Khodarev1, James Park, Yasushi Kataoka, Edwardine Nodzenski, Samuel Hellman, Bernard Roizman, Ralph R Weichselbaum, Charles A Pelizzari.   

Abstract

A critical step for DNA array analysis is data filtration, which can reduce thousands of detected signals to limited sets of genes. Commonly accepted rules for such filtration are still absent. We present a rational approach, based on thresholding of intensities with cutoff levels that are estimated by receiver operating characteristic (ROC) analysis. The technique compares test results with known distributions of positive and negative signals. We apply the method to Atlas cDNA arrays, GeneFilters, and Affymetrix GeneChip. ROC analysis demonstrates similarities in the distribution of false and true positive data for these different systems. We illustrate the estimation of an optimal cutoff level for intensity-based filtration, providing the highest ratio of true to false signals. For GeneChip arrays, we derived filtration thresholds consistent with the reported data based on replicate hybridizations. Intensity-based filtration optimized with ROC combined with other types of filtration (for example, based on significances of differences and/or ratios), should improve DNA array analysis. ROC methodology is also demonstrated for comparison of the performance of different types of arrays, imagers, and analysis software.

Mesh:

Year:  2003        PMID: 12620398     DOI: 10.1016/s0888-7543(02)00042-3

Source DB:  PubMed          Journal:  Genomics        ISSN: 0888-7543            Impact factor:   5.736


  12 in total

1.  Ad.Egr-TNF and local ionizing radiation suppress metastases by interferon-beta-dependent activation of antigen-specific CD8+ T cells.

Authors:  Yuru Meng; Helena J Mauceri; Nikolai N Khodarev; Thomas E Darga; Sean P Pitroda; Michael A Beckett; Donald W Kufe; Ralph R Weichselbaum
Journal:  Mol Ther       Date:  2010-03-02       Impact factor: 11.454

2.  MUC1-associated proliferation signature predicts outcomes in lung adenocarcinoma patients.

Authors:  Dhara M MacDermed; Nikolai N Khodarev; Sean P Pitroda; Darrin C Edwards; Charles A Pelizzari; Lei Huang; Donald W Kufe; Ralph R Weichselbaum
Journal:  BMC Med Genomics       Date:  2010-05-06       Impact factor: 3.063

3.  A comprehensive and universal method for assessing the performance of differential gene expression analyses.

Authors:  Mikhail G Dozmorov; Joel M Guthridge; Robert E Hurst; Igor M Dozmorov
Journal:  PLoS One       Date:  2010-09-09       Impact factor: 3.240

4.  MUC1-induced alterations in a lipid metabolic gene network predict response of human breast cancers to tamoxifen treatment.

Authors:  Sean P Pitroda; Nikolai N Khodarev; Michael A Beckett; Donald W Kufe; Ralph R Weichselbaum
Journal:  Proc Natl Acad Sci U S A       Date:  2009-03-16       Impact factor: 11.205

5.  STAT1 is overexpressed in tumors selected for radioresistance and confers protection from radiation in transduced sensitive cells.

Authors:  Nikolai N Khodarev; Michael Beckett; Edwardine Labay; Thomas Darga; Bernard Roizman; Ralph R Weichselbaum
Journal:  Proc Natl Acad Sci U S A       Date:  2004-01-30       Impact factor: 11.205

6.  MUC1-induced transcriptional programs associated with tumorigenesis predict outcome in breast and lung cancer.

Authors:  Nikolai N Khodarev; Sean P Pitroda; Michael A Beckett; Dhara M MacDermed; Lei Huang; Donald W Kufe; Ralph R Weichselbaum
Journal:  Cancer Res       Date:  2009-03-24       Impact factor: 12.701

7.  Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressions.

Authors:  Igor Dozmorov; Ivan Lefkovits
Journal:  Nucleic Acids Res       Date:  2009-08-31       Impact factor: 16.971

8.  STAT1-dependent expression of energy metabolic pathways links tumour growth and radioresistance to the Warburg effect.

Authors:  Sean P Pitroda; Bassam T Wakim; Ravi F Sood; Mara G Beveridge; Michael A Beckett; Dhara M MacDermed; Ralph R Weichselbaum; Nikolai N Khodarev
Journal:  BMC Med       Date:  2009-11-05       Impact factor: 8.775

9.  Cooperativity of the MUC1 oncoprotein and STAT1 pathway in poor prognosis human breast cancer.

Authors:  N Khodarev; R Ahmad; H Rajabi; S Pitroda; T Kufe; C McClary; M D Joshi; D MacDermed; R Weichselbaum; D Kufe
Journal:  Oncogene       Date:  2009-11-16       Impact factor: 9.867

10.  Tentacle probes: eliminating false positives without sacrificing sensitivity.

Authors:  Brent C Satterfield; Jay A A West; Michael R Caplan
Journal:  Nucleic Acids Res       Date:  2007-05-21       Impact factor: 16.971

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