Fierro Ana C, Thuret Raphael, Engelen Kristof, Bernot Gilles, Marchal Kathleen, Pollet Nicolas
CNRS UMR 8080, Laboratoire Développement et Evolution, Bat 445, F-91405 Orsay, France.
BMC Bioinformatics. 2008 Jan 3;9:1. doi: 10.1186/1471-2105-9-1.
As an alternative to the frequently used "reference design" for two-channel microarrays, other designs have been proposed. These designs have been shown to be more profitable from a theoretical point of view (more replicates of the conditions of interest for the same number of arrays). However, the interpretation of the measurements is less straightforward and a reconstruction method is needed to convert the observed ratios into the genuine profile of interest (e.g. a time profile). The potential advantages of using these alternative designs thus largely depend on the success of the profile reconstruction. Therefore, we compared to what extent different linear models agree with each other in reconstructing expression ratios and corresponding time profiles from a complex design.
On average the correlation between the estimated ratios was high, and all methods agreed with each other in predicting the same profile, especially for genes of which the expression profile showed a large variance across the different time points. Assessing the similarity in profile shape, it appears that, the more similar the underlying principles of the methods (model and input data), the more similar their results. Methods with a dye effect seemed more robust against array failure. The influence of a different normalization was not drastic and independent of the method used.
Including a dye effect such as in the methods lmbr_dye, anovaFix and anovaMix compensates for residual dye related inconsistencies in the data and renders the results more robust against array failure. Including random effects requires more parameters to be estimated and is only advised when a design is used with a sufficient number of replicates. Because of this, we believe lmbr_dye, anovaFix and anovaMix are most appropriate for practical use.
作为双通道微阵列常用“参考设计”的替代方案，人们提出了其他设计。从理论角度来看，这些设计已被证明更具优势（在相同数量的阵列下，对感兴趣的条件有更多重复）。然而，测量结果的解释并非那么直接，需要一种重构方法将观察到的比率转换为真正感兴趣的概况（例如时间概况）。因此，使用这些替代设计的潜在优势在很大程度上取决于概况重构的成功与否。所以，我们比较了不同线性模型在从复杂设计中重构表达比率和相应时间概况时彼此的一致程度。
平均而言，估计比率之间的相关性很高，并且所有方法在预测相同概况方面相互一致，特别是对于那些在不同时间点表达概况差异较大的基因。在评估概况形状的相似性时，似乎方法的基本原理（模型和输入数据）越相似，其结果就越相似。具有染料效应的方法似乎对阵列失败更具鲁棒性。不同归一化的影响并不显著，且与所使用的方法无关。
像在lmbr_dye、anovaFix和anovaMix方法中那样纳入染料效应，可补偿数据中与染料相关的残留不一致性，并使结果对阵列失败更具鲁棒性。纳入随机效应需要估计更多参数，仅在设计有足够数量重复时才建议使用。因此，我们认为lmbr_dye、anovaFix和anovaMix最适合实际应用。
