He Zhili, Zhou Jizhong
Institute for Environmental Genomics, Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019, USA.
Appl Environ Microbiol. 2008 May;74(10):2957-66. doi: 10.1128/AEM.02536-07. Epub 2008 Mar 14.
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.
基于来自嗜铁钩端螺旋菌MR-1的四个基因，使用包含完全匹配（PM）和错配（MM）探针的寡核苷酸阵列，通过实验确定了用于微阵列数据分析的信噪比（SNR）阈值。开发并评估了一种新的SNR计算方法，称为信号与双标准差比（SSDR），以及其他两种方法，即信号与标准差比（SSR）和信号与背景比（SBR）。在低严格度下，以寡核苷酸和PCR扩增子作为靶模板时，SSR、SBR和SSDR的阈值分别为2.5、1.60和0.80；以基因组DNA作为靶模板时，阈值分别为2.0、1.60和0.70。在高严格度条件下获得的阈值略高。SSR和SSDR的阈值随着靶标复杂性（例如靶标类型）的增加、背景DNA的存在以及靶标组成的减少而降低，而SBR在所有情况下均保持不变。使用SSDR计算方法观察到的假阳性和假阴性百分比最低，这表明它可能是一种更好的SNR计算方法，用于更准确地确定SNR阈值。通过信噪比阈值识别的阳性斑点通过学生t检验进行验证，并观察到一致的结果。本研究为用户在不同杂交条件下为不同样品选择合适的信噪比阈值提供了一般指导。
