Biomarker clustering to address correlations in proteomic data
Biomarker clustering to address correlations in proteomic data
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DOI：
10.1002/pmic.200600514
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发表时间：
2007-04-01
期刊：
影响因子：
3.4
通讯作者：
Cohen, Harvey J.
中科院分区：
文献类型：
--
作者：
Carlson, Scott M.;Najmi, Amir;Cohen, Harvey J.
相关变量已被证明会混淆微阵列实验中的统计分析。同样的效果在更大程度上适用于蛋白质组学，特别是使用 MS 进行平行测量时。 PTM、片段化和多聚体形成等生物效应可以产生强相关变量。在某些类型的 MS 中，由于技术影响（例如不完全的色谱分离、与多个表面的结合或多次电离），该问题变得更加复杂。现有的降维方法，特别是主成分分析和相关技术，并不总是令人满意，因为它们产生的数据往往缺乏清晰的生物学解释。我们提出了一种预处理算法，对高度相关的特征进行聚类，使用贝叶斯信息标准来选择最佳的聚类数量。集群的统计分析（而不是单个特征）受益于较低的噪声，并减少了与强相关数据相关的困难。这种预处理提高了使用模拟数据的错误发现率进行分析的统计能力。真实数据中经常存在强相关性，我们发现聚类可以改善川崎病患者血浆和急性髓系或急性淋巴细胞白血病患者骨髓细胞提取物的临床 SELDI-TOF-MS 数据集中的生物标志物发现。
Correlated variables have been shown to confound statistical analyses in microarray experiments. The same effect applies to an even greater degree in proteomics, especially with the use of MS for parallel measurements. Biological effects such as PTM, fragmentation, and multimer formation can produce strongly correlated variables. The problem is compounded in some types of MS by technical effects such as incomplete chromatographic separation, binding to multiple surfaces, or multiple ionizations. Existing methods for dimension reduction, notably principal components analysis and related techniques, are not always satisfactory because they produce data that often lack clear biological interpretation. We propose a preprocessing algorithm that clusters highly correlated features, using the Bayes information criterion to select an optimal number of clusters. Statistical analysis of clusters, instead of individual features, benefits from lower noise, and reduces the difficulties associated with strongly correlated data. This preprocessing increases the statistical power of analyses using false discovery rate on simulated data. Strong correlations are often present in real data, and we find that clustering improves biomarker discovery in clinical SELDI-TOF-MS datasets of plasma from patients with Kawasaki disease, and bone-marrow cell extracts from patients with acute myeloid or acute lymphoblastic leukemia.