Empirical Evaluation of a New Method for Calculating Signal-to-Noise Ratio for Microarray Data Analysis

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 the signal-to-both-standard-deviations ratio (SSDR), was developed and evaluated, along with other two methods, the signal-to-standard-deviation ratio (SSR) and the signal-to-background ratio (SBR). At a low stringency, the thresholds of the SSR, SBR, and SSDR were 2.5, 1.60, and 0.80 with an oligonucleotide and a PCR amplicon as target templates and 2.0, 1.60, and 0.70 with genomic DNAs as target templates. Slightly higher thresholds were obtained under highstringency conditions. The thresholds of the 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 compositions of targets, while the SBR remained unchanged in all situations. The lowest percentage of false positives and false negatives 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.

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“…SNR was calculated using the SSDR method (He and Zhou 2008), m i /(s iF + s iB ) (where m equals spot intensity; s, pixel standard deviation; i, spot; F, foreground; and B, background).…”

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“…Images were then processed by ImaGene 6.0 software (BioDiscovery, El Segundo, CA, USA) in which a grid of individual circles defining the location of each DNA spot on the array was superimposed on the image to designate each fluorescent spot to be quantified. Hybridization spots with a signal-noise ratio X2.0 (He and Zhou, 2008) were kept for further analysis. Normalization among slides was performed based on the mean signals of all spots.…”

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“…2), demonstrating that spots on the array without specific formation of polymer yielded SNR values < 2.5. In addition, previous studies have shown that an SNR value of 3 is considered to be the minimum signal intensity for accurate quantification (Verdnick et al, 2002;He and Zhou, 2008;Park et al, 2008). Based on these observations an SNR value of 3 was established as our detection limit for positive signals (Fig.…”

“…Polymer formation was clearly evident only for those probes targeting virulence genes known to be present in the reference strains. Quantification of the specificity by measuring the SNR values for each probe on the array demonstrated that there was a clear distinction between signal intensities and background noise, an important aspect for data analysis and interpretation of results with DNA microarrays (Verdnick et al, 2002;He and Zhou, 2008). These results were achieved by using a 30-mer oligonucleotide probe allowing a more specific detection of the target gene.…”

“…For each spot, the quantification of signal and background intensities was quantified by using ampliVIEW software and expressed as signal-tonoise ratio (SNR). SNR values were calculated as the difference between the mean pixel intensities of the signal minus the mean pixel intensities of the background divided by the standard deviation of the background (He and Zhou, 2008).…”