This Article Full Text (PDF) Other Versions of this Article: AEM.02536-07v1 74/10/2957    most recent Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Reprints and Permissions Copyright Information Books from ASM Press MicrobeWorld Citing Articles Citing Articles via Google Scholar Google Scholar Articles by He, Z. Articles by Zhou, J. Search for Related Content PubMed PubMed Citation Articles by He, Z. Articles by Zhou, J. Agricola Articles by He, Z. Articles by Zhou, J.

 Previous Article  |  Next Article 

Appl. Environ. Microbiol. doi:10.1128/AEM.02536-07
Copyright (c) 2008, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved.

Empirical evaluation of a new method for calculating signal to noise ratio (SNR) for microarray data analysis

Institute for Environmental Genomics, Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019

^* To whom correspondence should be addressed. Email: jzhou{at}ou.edu.

	Abstract

Signal-to-noise-ratio (SNR) thresholds for microarray data analysis were experimentally determined with an oligonucleotide array that contained perfect match (PM) and mismatch (MM) probes based upon four genes from Shewanella oneidensis MR-1. A new SNR calculation, called signal to both standard deviations ratio (SSDR) was developed, and evaluated along with other two methods, signal to standard deviation ratio (SSR), and signal to background ratio (SBR). At a low stringency, the thresholds of SSR, SBR, and SSDR were 2.5, 1.60 and 0.80 with oligonucleotide and PCR amplicon as target templates, and 2.0, 1.60 and 0.70 with genomic DNA as target templates. Slightly higher thresholds were obtained at the high stringency condition. The thresholds of SSR and SSDR decreased with an increase in the complexity of targets (e.g. target types), and the presence of background DNA, and a decrease in the composition of targets, while SBR remained unchanged under all situations. The lowest percentage of false positives (FP) and false negatives (FN) was observed with the SSDR calculation method, suggesting that it may be a better SNR calculation for more accurate determination of SNR thresholds. Positive spots identified by SNR thresholds were verified by the Student t-test, and consistent results were observed. This study provides general guidance for users to select appropriate SNR thresholds for different samples under different hybridization conditions.