Empirical evaluation of a new method for calculating signal-to-noise ratio for microarray data analysis

Appl Environ Microbiol. 2008 May;74(10):2957-66. doi: 10.1128/AEM.02536-07. Epub 2008 Mar 14.

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 high-stringency 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.

Publication types

  • Evaluation Study
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Base Pair Mismatch
  • DNA Primers / genetics
  • False Negative Reactions
  • False Positive Reactions
  • Gene Expression Profiling / methods*
  • Genes, Bacterial
  • Microarray Analysis / methods*
  • Nucleic Acid Hybridization
  • Oligonucleotide Array Sequence Analysis
  • Sensitivity and Specificity
  • Shewanella / genetics
  • Statistics as Topic / methods*

Substances

  • DNA Primers