Miecznikowski Jeffrey C, Gaile Daniel P, Liu Song, Shepherd Lori, Nowak Norma
Department of Biostatistics, University at Buffalo, Buffalo, NY 14214, USA.
J Biomed Biotechnol. 2011;2011:860732. doi: 10.1155/2011/860732. Epub 2011 Feb 21.
The main focus in pin-tip (or print-tip) microarray analysis is determining which probes, genes, or oligonucleotides are differentially expressed. Specifically in array comparative genomic hybridization (aCGH) experiments, researchers search for chromosomal imbalances in the genome. To model this data, scientists apply statistical methods to the structure of the experiment and assume that the data consist of the signal plus random noise. In this paper we propose "SmoothArray", a new method to preprocess comparative genomic hybridization (CGH) bacterial artificial chromosome (BAC) arrays and we show the effects on a cancer dataset. As part of our R software package "aCGHplus," this freely available algorithm removes the variation due to the intensity effects, pin/print-tip, the spatial location on the microarray chip, and the relative location from the well plate. removal of this variation improves the downstream analysis and subsequent inferences made on the data. Further, we present measures to evaluate the quality of the dataset according to the arrayer pins, 384-well plates, plate rows, and plate columns. We compare our method against competing methods using several metrics to measure the biological signal. With this novel normalization algorithm and quality control measures, the user can improve their inferences on datasets and pinpoint problems that may arise in their BAC aCGH technology.
针尖端（或打印尖端）微阵列分析的主要重点是确定哪些探针、基因或寡核苷酸存在差异表达。特别是在阵列比较基因组杂交（aCGH）实验中，研究人员会在基因组中寻找染色体失衡情况。为了对这些数据进行建模，科学家们将统计方法应用于实验结构，并假设数据由信号加随机噪声组成。在本文中，我们提出了“SmoothArray”，一种用于预处理比较基因组杂交（CGH）细菌人工染色体（BAC）阵列的新方法，并展示了其对癌症数据集的影响。作为我们R软件包“aCGHplus”的一部分，这种免费可用的算法消除了由于强度效应、针/打印尖端、微阵列芯片上的空间位置以及来自微孔板的相对位置所导致的变异。消除这种变异可改善下游分析以及对数据进行的后续推断。此外，我们还提出了根据阵列针、384孔板、板行和板列来评估数据集质量的方法。我们使用多种衡量生物信号的指标，将我们的方法与其他竞争方法进行比较。通过这种新颖的归一化算法和质量控制措施，用户可以改进对数据集的推断，并找出其BAC aCGH技术中可能出现的问题。
