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Defining signal thresholds in DNA microarrays: exemplary application for invasive cancer.

Bilban M, Buehler LK, Head S, Desoye G, Quaranta V - BMC Genomics (2002)

Bottom Line: Most strategies have used fold-change threshold values, but variability at low signal intensities may invalidate this approach and it does not provide information about false-positives and false negatives.It demonstrated that ROC analysis yields a threshold with reduced missclassified genes in microarray experiments.The proposed method is applicable to both custom made and commercially available DNA microarrays and will help to improve the reliability of predictions from DNA microarray experiments.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Scripps Research Institute, Department of Cell Biology, 10550 North Torrey Pines Road, La Jolla, CA, USA. mbilban@scripps.edu

ABSTRACT

Background: Genome-wide or application-targeted microarrays containing a subset of genes of interest have become widely used as a research tool with the prospect of diagnostic application. Intrinsic variability of microarray measurements poses a major problem in defining signal thresholds for absent/present or differentially expressed genes. Most strategies have used fold-change threshold values, but variability at low signal intensities may invalidate this approach and it does not provide information about false-positives and false negatives.

Results: We introduce a method to filter false-positives and false-negatives from DNA microarray experiments. This is achieved by evaluating a set of positive and negative controls by receiver operating characteristic (ROC) analysis. As an advantage of this approach, users may define thresholds on the basis of sensitivity and specificity considerations. The area under the ROC curve allows quality control of microarray hybridizations. This method has been applied to custom made microarrays developed for the analysis of invasive melanoma derived tumor cells. It demonstrated that ROC analysis yields a threshold with reduced missclassified genes in microarray experiments.

Conclusions: Provided that a set of appropriate positive and negative controls is included on the microarray, ROC analysis obviates the inherent problem of arbitrarily selecting threshold levels in microarray experiments. The proposed method is applicable to both custom made and commercially available DNA microarrays and will help to improve the reliability of predictions from DNA microarray experiments.

No MeSH data available.


Related in: MedlinePlus

Signal distributions for specific and non-specific hybridizations overlap at low absolute intensities. The median intensity of 4 B.subtilis genes (n = 24 replicates per gene × 4 = 96) was used as a linear scaling factor to balance the Cy3 and Cy5 channels. Following this normalization step, normalized intensities were Log2 transformed for efficient graphical illustration. Positive control spots (open bars) and negative control spots (filled bars) from (A) array 1 and (B) array 2 microarray hybridizations. The positive control group includes seven housekeeping genes (n = 42) and four B.subtilis genes (24 repeats per sequence; n = 96) representing sequence-specific hybridization. The negative control sequences (six repeats per sequence) include three plant genes (n = 18), three E. coli genes (n = 18), and seven human cytomegalovirus (hCMV) genes (n = 42) representing non-specific hybridization events. Data for Cy3 and Cy5 signals were pooled. Signal distributions for test genes (n = 154) from (C) array 1 and (D) array 2.
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Figure 1: Signal distributions for specific and non-specific hybridizations overlap at low absolute intensities. The median intensity of 4 B.subtilis genes (n = 24 replicates per gene × 4 = 96) was used as a linear scaling factor to balance the Cy3 and Cy5 channels. Following this normalization step, normalized intensities were Log2 transformed for efficient graphical illustration. Positive control spots (open bars) and negative control spots (filled bars) from (A) array 1 and (B) array 2 microarray hybridizations. The positive control group includes seven housekeeping genes (n = 42) and four B.subtilis genes (24 repeats per sequence; n = 96) representing sequence-specific hybridization. The negative control sequences (six repeats per sequence) include three plant genes (n = 18), three E. coli genes (n = 18), and seven human cytomegalovirus (hCMV) genes (n = 42) representing non-specific hybridization events. Data for Cy3 and Cy5 signals were pooled. Signal distributions for test genes (n = 154) from (C) array 1 and (D) array 2.

Mentions: The reliability of ratios measured to describe changes in gene expression depends on the absolute signal intensities. While ratios from highly abundant transcripts may be accurate, rare transcripts give absolute intensities that may be obscured by non-specific hybridization. Thus both ratio and absolute signal intensity are important to evaluate differential gene expression properly. Calibrating the appropriate signal and noise intensity thresholds for a given microarray hybridization requires the analysis of a set of positive and negative reference genes. At low signal intensities, both reference groups yield overlapping signal distributions (Figure 1a and 1b). Test signals from array 1 and 2 falling within the overlap region cannot easily be categorized as either present or absent and calculating ratios may lead to the identification of false positives (or false negatives) (Figure 1c and 1d).


Defining signal thresholds in DNA microarrays: exemplary application for invasive cancer.

Bilban M, Buehler LK, Head S, Desoye G, Quaranta V - BMC Genomics (2002)

Signal distributions for specific and non-specific hybridizations overlap at low absolute intensities. The median intensity of 4 B.subtilis genes (n = 24 replicates per gene × 4 = 96) was used as a linear scaling factor to balance the Cy3 and Cy5 channels. Following this normalization step, normalized intensities were Log2 transformed for efficient graphical illustration. Positive control spots (open bars) and negative control spots (filled bars) from (A) array 1 and (B) array 2 microarray hybridizations. The positive control group includes seven housekeeping genes (n = 42) and four B.subtilis genes (24 repeats per sequence; n = 96) representing sequence-specific hybridization. The negative control sequences (six repeats per sequence) include three plant genes (n = 18), three E. coli genes (n = 18), and seven human cytomegalovirus (hCMV) genes (n = 42) representing non-specific hybridization events. Data for Cy3 and Cy5 signals were pooled. Signal distributions for test genes (n = 154) from (C) array 1 and (D) array 2.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC117791&req=5

Figure 1: Signal distributions for specific and non-specific hybridizations overlap at low absolute intensities. The median intensity of 4 B.subtilis genes (n = 24 replicates per gene × 4 = 96) was used as a linear scaling factor to balance the Cy3 and Cy5 channels. Following this normalization step, normalized intensities were Log2 transformed for efficient graphical illustration. Positive control spots (open bars) and negative control spots (filled bars) from (A) array 1 and (B) array 2 microarray hybridizations. The positive control group includes seven housekeeping genes (n = 42) and four B.subtilis genes (24 repeats per sequence; n = 96) representing sequence-specific hybridization. The negative control sequences (six repeats per sequence) include three plant genes (n = 18), three E. coli genes (n = 18), and seven human cytomegalovirus (hCMV) genes (n = 42) representing non-specific hybridization events. Data for Cy3 and Cy5 signals were pooled. Signal distributions for test genes (n = 154) from (C) array 1 and (D) array 2.
Mentions: The reliability of ratios measured to describe changes in gene expression depends on the absolute signal intensities. While ratios from highly abundant transcripts may be accurate, rare transcripts give absolute intensities that may be obscured by non-specific hybridization. Thus both ratio and absolute signal intensity are important to evaluate differential gene expression properly. Calibrating the appropriate signal and noise intensity thresholds for a given microarray hybridization requires the analysis of a set of positive and negative reference genes. At low signal intensities, both reference groups yield overlapping signal distributions (Figure 1a and 1b). Test signals from array 1 and 2 falling within the overlap region cannot easily be categorized as either present or absent and calculating ratios may lead to the identification of false positives (or false negatives) (Figure 1c and 1d).

Bottom Line: Most strategies have used fold-change threshold values, but variability at low signal intensities may invalidate this approach and it does not provide information about false-positives and false negatives.It demonstrated that ROC analysis yields a threshold with reduced missclassified genes in microarray experiments.The proposed method is applicable to both custom made and commercially available DNA microarrays and will help to improve the reliability of predictions from DNA microarray experiments.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Scripps Research Institute, Department of Cell Biology, 10550 North Torrey Pines Road, La Jolla, CA, USA. mbilban@scripps.edu

ABSTRACT

Background: Genome-wide or application-targeted microarrays containing a subset of genes of interest have become widely used as a research tool with the prospect of diagnostic application. Intrinsic variability of microarray measurements poses a major problem in defining signal thresholds for absent/present or differentially expressed genes. Most strategies have used fold-change threshold values, but variability at low signal intensities may invalidate this approach and it does not provide information about false-positives and false negatives.

Results: We introduce a method to filter false-positives and false-negatives from DNA microarray experiments. This is achieved by evaluating a set of positive and negative controls by receiver operating characteristic (ROC) analysis. As an advantage of this approach, users may define thresholds on the basis of sensitivity and specificity considerations. The area under the ROC curve allows quality control of microarray hybridizations. This method has been applied to custom made microarrays developed for the analysis of invasive melanoma derived tumor cells. It demonstrated that ROC analysis yields a threshold with reduced missclassified genes in microarray experiments.

Conclusions: Provided that a set of appropriate positive and negative controls is included on the microarray, ROC analysis obviates the inherent problem of arbitrarily selecting threshold levels in microarray experiments. The proposed method is applicable to both custom made and commercially available DNA microarrays and will help to improve the reliability of predictions from DNA microarray experiments.

No MeSH data available.


Related in: MedlinePlus