Interdisciplinary Centre for Bioinformatics; Universität Leipzig, D-4107 Leipzig, Haertelstr 16-18, Germany.
BMC Bioinformatics. 2010 May 28;11:291. doi: 10.1186/1471-2105-11-291.
Post-hybridization washing is an essential part of microarray experiments. Both the quality of the experimental washing protocol and adequate consideration of washing in intensity calibration ultimately affect the quality of the expression estimates extracted from the microarray intensities.
We conducted experiments on GeneChip microarrays with altered protocols for washing, scanning and staining to study the probe-level intensity changes as a function of the number of washing cycles. For calibration and analysis of the intensity data we make use of the 'hook' method which allows intensity contributions due to non-specific and specific hybridization of perfect match (PM) and mismatch (MM) probes to be disentangled in a sequence specific manner. On average, washing according to the standard protocol removes about 90% of the non-specific background and about 30-50% and less than 10% of the specific targets from the MM and PM, respectively. Analysis of the washing kinetics shows that the signal-to-noise ratio doubles roughly every ten stringent washing cycles. Washing can be characterized by time-dependent rate constants which reflect the heterogeneous character of target binding to microarray probes. We propose an empirical washing function which estimates the survival of probe bound targets. It depends on the intensity contribution due to specific and non-specific hybridization per probe which can be estimated for each probe using existing methods. The washing function allows probe intensities to be calibrated for the effect of washing. On a relative scale, proper calibration for washing markedly increases expression measures, especially in the limit of small and large values.
Washing is among the factors which potentially distort expression measures. The proposed first-order correction method allows direct implementation in existing calibration algorithms for microarray data. We provide an experimental 'washing data set' which might be used by the community for developing amendments of the washing correction.
杂交后洗涤是微阵列实验的一个重要组成部分。实验洗涤方案的质量和对洗涤强度的充分考虑最终会影响从微阵列强度中提取的表达估计的质量。
我们在 GeneChip 微阵列上进行了改变洗涤、扫描和染色方案的实验，以研究探针级强度随洗涤循环次数的变化。为了校准和分析强度数据，我们使用“钩”方法，该方法允许以序列特异性的方式分离由于完美匹配（PM）和错配（MM）探针的非特异性和特异性杂交而导致的强度贡献。平均而言，根据标准方案洗涤可去除约 90%的非特异性背景，分别去除 MM 和 PM 中约 30-50%和小于 10%的特异性靶标。洗涤动力学分析表明，信噪比大约每十次严格洗涤循环增加一倍。洗涤可以用时间相关的速率常数来描述，该常数反映了靶标与微阵列探针结合的异质性。我们提出了一个经验洗涤函数，用于估计探针结合靶标存活的概率。它取决于每个探针的特异性和非特异性杂交的强度贡献，可以使用现有方法对每个探针进行估计。洗涤函数允许对洗涤的影响进行探针强度校准。在相对尺度上，适当的洗涤校准显著增加了表达测量值，尤其是在小值和大值的极限情况下。
洗涤是潜在影响表达测量值的因素之一。所提出的一阶修正方法允许在现有的微阵列数据校准算法中直接实现。我们提供了一个实验“洗涤数据集”，可以供社区用于开发洗涤修正的改进。
