Kasim Adetayo, Lin Dan, Van Sanden Suzy, Clevert Djork-Arné, Bijnens Luc, Göhlmann Hinrich, Amaratunga Dhammika, Hochreiter Sepp, Shkedy Ziv, Talloen Willem
Universiteit Hasselt & Katholieke Universiteit Leuven.
Stat Appl Genet Mol Biol. 2010;9:Article 4. doi: 10.2202/1544-6115.1460. Epub 2010 Jan 6.
The strength and weakness of microarray technology can be attributed to the enormous amount of information it is generating. To fully enhance the benefit of microarray technology for testing differentially expressed genes and classification, there is a need to minimize the amount of irrelevant genes present in microarray data. A major interest is to use probe-level data to call genes informative or noninformative based on the trade-off between the array-to-array variability and the measurement error. Existing works in this direction include filtering likely uninformative sets of hybridization (FLUSH; Calza et al., 2007) and I/NI calls for the exclusion of noninformative genes using FARMS (I/NI calls; Talloen et al., 2007; Hochreiter et al., 2006). In this paper, we propose a linear mixed model as a more flexible method that performs equally good as I/NI calls and outperforms FLUSH. We also introduce other criteria for gene filtering, such as, R2 and intra-cluster correlation. Additionally, we include some objective criteria based on likelihood ratio testing, the Akaike information criteria (AIC; Akaike, 1973) and the Bayesian information criterion (BIC; Schwarz, 1978 ). Based on the HGU-133A Spiked-in data set, it is shown that the linear mixed model approach outperforms FLUSH, a method that filters genes based on a quantile regression. The linear model is equivalent to a factor analysis model when either the factor loadings are set to a constant with the variance of the latent factor equal to one, or if the factor loadings are set to one together with unconstrained variance of the latent factor. Filtering based on conditional variance calls a probe set informative when the intensity of one or more probes is consistent across the arrays, while filtering using R2 or intra-cluster correlation calls a probe set informative only when average intensity of a probe set is consistent across the arrays. Filtering based on likelihood ratio test AIC and BIC are less stringent compared to the other criteria.
微阵列技术的优势与不足可归因于它所产生的海量信息。为了充分提高微阵列技术在检测差异表达基因和分类方面的效益，有必要尽量减少微阵列数据中无关基因的数量。一个主要的关注点是根据阵列间变异性与测量误差之间的权衡，利用探针水平的数据来判断基因是否具有信息性。这一方向上现有的工作包括过滤可能无信息的杂交集（FLUSH；卡尔扎等人，2007年）以及使用FARMS进行I/NI调用以排除无信息基因（I/NI调用；塔洛恩等人，2007年；霍赫雷特等人，2006年）。在本文中，我们提出一种线性混合模型，作为一种更灵活的方法，其性能与I/NI调用相当且优于FLUSH。我们还引入了其他基因过滤标准，如R2和簇内相关性。此外，我们纳入了一些基于似然比检验、赤池信息准则（AIC；赤池，1973年）和贝叶斯信息准则（BIC；施瓦茨，1978年）的客观标准。基于HGU - 133A加标数据集的结果表明，线性混合模型方法优于FLUSH，后者是一种基于分位数回归过滤基因的方法。当因子载荷设置为常数且潜在因子的方差等于1时，或者当因子载荷设置为1且潜在因子的方差不受约束时，线性模型等同于因子分析模型。基于条件方差进行过滤时，当一个或多个探针的强度在各阵列间一致时，就判定一个探针集具有信息性；而使用R2或簇内相关性进行过滤时，只有当一个探针集的平均强度在各阵列间一致时，才判定该探针集具有信息性。与其他标准相比，基于似然比检验、AIC和BIC的过滤要求没那么严格。
