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Sample entropy analysis of cervical neoplasia gene-expression signatures.

Botting SK, Trzeciakowski JP, Benoit MF, Salama SA, Diaz-Arrastia CR - BMC Bioinformatics (2009)

Bottom Line: Approximate entropy is applied here as a method to classify the complex gene expression patterns resultant of a clinical sample set.This may be measured in terms of the ApSE when compared to normal tissue.The success of the Approximate Sample Entropy approach in discerning alterations in complexity from biological system with such relatively small sample set, and extracting biologically relevant genes of interest hold great promise.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Obstetrics & Gynecology, University of Texas Medical Branch, Galveston, Texas, USA. shbarton@utmb.edu

ABSTRACT

Background: We introduce Approximate Entropy as a mathematical method of analysis for microarray data. Approximate entropy is applied here as a method to classify the complex gene expression patterns resultant of a clinical sample set. Since Entropy is a measure of disorder in a system, we believe that by choosing genes which display minimum entropy in normal controls and maximum entropy in the cancerous sample set we will be able to distinguish those genes which display the greatest variability in the cancerous set. Here we describe a method of utilizing Approximate Sample Entropy (ApSE) analysis to identify genes of interest with the highest probability of producing an accurate, predictive, classification model from our data set.

Results: In the development of a diagnostic gene-expression profile for cervical intraepithelial neoplasia (CIN) and squamous cell carcinoma of the cervix, we identified 208 genes which are unchanging in all normal tissue samples, yet exhibit a random pattern indicative of the genetic instability and heterogeneity of malignant cells. This may be measured in terms of the ApSE when compared to normal tissue. We have validated 10 of these genes on 10 Normal and 20 cancer and CIN3 samples. We report that the predictive value of the sample entropy calculation for these 10 genes of interest is promising (75% sensitivity, 80% specificity for prediction of cervical cancer over CIN3).

Conclusion: The success of the Approximate Sample Entropy approach in discerning alterations in complexity from biological system with such relatively small sample set, and extracting biologically relevant genes of interest hold great promise.

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Related in: MedlinePlus

Establishment of normal baseline for sample entropy calculations. Initially a normal baseline was established, 966 genes were found to be unchanging between the normal samples when limited to a variance of less then 0.5% of the log2 expression values, a variance below the technical noise level of the Affymetrix array.
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Figure 1: Establishment of normal baseline for sample entropy calculations. Initially a normal baseline was established, 966 genes were found to be unchanging between the normal samples when limited to a variance of less then 0.5% of the log2 expression values, a variance below the technical noise level of the Affymetrix array.

Mentions: Initially a normal baseline was established, 966 genes were found to be unchanging between patients with normal cervical biopsies when limited to a variance of less then 0.5% of the log2 expression values, a variance below the technical noise level of the Affymetrix array.[16] (Figure 1)


Sample entropy analysis of cervical neoplasia gene-expression signatures.

Botting SK, Trzeciakowski JP, Benoit MF, Salama SA, Diaz-Arrastia CR - BMC Bioinformatics (2009)

Establishment of normal baseline for sample entropy calculations. Initially a normal baseline was established, 966 genes were found to be unchanging between the normal samples when limited to a variance of less then 0.5% of the log2 expression values, a variance below the technical noise level of the Affymetrix array.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2656476&req=5

Figure 1: Establishment of normal baseline for sample entropy calculations. Initially a normal baseline was established, 966 genes were found to be unchanging between the normal samples when limited to a variance of less then 0.5% of the log2 expression values, a variance below the technical noise level of the Affymetrix array.
Mentions: Initially a normal baseline was established, 966 genes were found to be unchanging between patients with normal cervical biopsies when limited to a variance of less then 0.5% of the log2 expression values, a variance below the technical noise level of the Affymetrix array.[16] (Figure 1)

Bottom Line: Approximate entropy is applied here as a method to classify the complex gene expression patterns resultant of a clinical sample set.This may be measured in terms of the ApSE when compared to normal tissue.The success of the Approximate Sample Entropy approach in discerning alterations in complexity from biological system with such relatively small sample set, and extracting biologically relevant genes of interest hold great promise.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Obstetrics & Gynecology, University of Texas Medical Branch, Galveston, Texas, USA. shbarton@utmb.edu

ABSTRACT

Background: We introduce Approximate Entropy as a mathematical method of analysis for microarray data. Approximate entropy is applied here as a method to classify the complex gene expression patterns resultant of a clinical sample set. Since Entropy is a measure of disorder in a system, we believe that by choosing genes which display minimum entropy in normal controls and maximum entropy in the cancerous sample set we will be able to distinguish those genes which display the greatest variability in the cancerous set. Here we describe a method of utilizing Approximate Sample Entropy (ApSE) analysis to identify genes of interest with the highest probability of producing an accurate, predictive, classification model from our data set.

Results: In the development of a diagnostic gene-expression profile for cervical intraepithelial neoplasia (CIN) and squamous cell carcinoma of the cervix, we identified 208 genes which are unchanging in all normal tissue samples, yet exhibit a random pattern indicative of the genetic instability and heterogeneity of malignant cells. This may be measured in terms of the ApSE when compared to normal tissue. We have validated 10 of these genes on 10 Normal and 20 cancer and CIN3 samples. We report that the predictive value of the sample entropy calculation for these 10 genes of interest is promising (75% sensitivity, 80% specificity for prediction of cervical cancer over CIN3).

Conclusion: The success of the Approximate Sample Entropy approach in discerning alterations in complexity from biological system with such relatively small sample set, and extracting biologically relevant genes of interest hold great promise.

Show MeSH
Related in: MedlinePlus