Pattern-selection based power analysis and discrimination of low- and high-grade myelodysplastic syndromes study using SNP arrays.
Pattern-selection based power analysis and discrimination of low- and high-grade myelodysplastic syndromes study using SNP arrays.
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DOI：
10.1371/journal.pone.0005054
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发表时间：
2009
期刊：
影响因子：
3.7
通讯作者：
Wong ST
中科院分区：
文献类型：
--
作者：
Yang X;Zhou X;Huang WT;Wu L;Monzon FA;Chang CC;Wong ST
近年来，应用单核苷酸多态性（SNP）芯片研究骨髓增生异常综合征（MDS）的拷贝数畸变（CNA）受到越来越多的关注。在本研究中，一种新的约束移动平均（CMA）算法被用来确定CNA区域的区域。除了CNA的大区域之外，使用所提出的CMA算法，还可以检测CNA的小区域。实时聚合酶链反应（qPCR）结果证明CMA算法对大区域和细微区域都有深刻的发现。基于恒模算法的结果，研究了两个独立的应用。第一个是样本估计的功效分析。准确估计实验所需的样本量对于努力效率和成本效益至关重要。进行功效分析以确定确保至少（）个检测区域与正常参考存在统计学差异所需的最小样本量。正如预期的那样，对于固定的显著性水平，功效随着样本量的增加而增加。第二个应用是从低级别MDS患者中分离出高级别MDS患者。我们建议计算一般变异水平（GVL）评分，以整合每个患者在基因型水平上的一般信息，并将其作为分类的统一度量。传统的MDS分类通常参考细胞形态学和国际预后评分系统（IPSS），属于表型水平的分类。GVL评分综合了CNA区域的信息、异常染色体的数目和基因型水平上改变的SNPs的总数。统计学检验表明，GVL评分可以很好地区分高、低级别MDS患者，与表型水平上使用形态学和IPSS评分的传统分类方法相比，GVL评分与临床结局的相关性更好。
Copy Number Aberration (CNA) in myelodysplastic syndromes (MDS) study using single nucleotide polymorphism (SNP) arrays have been received increasingly attentions in the recent years. In the current study, a new Constraint Moving Average (CMA) algorithm is adopted to determine the regions of CNA regions first. In addition to large regions of CNA, using the proposed CMA algorithm, small regions of CNA can also be detected. Real-time Polymerase Chain Reaction (qPCR) results prove that the CMA algorithm presents an insightful discovery of both large and subtle regions. Based on the results of CMA, two independent applications are studied. The first one is power analysis for sample estimation. An accurate estimation of sample size needed for the desired purpose of an experiment will be important for effort-efficiency and cost-effectiveness. The power analysis is performed to determine the minimum sample size required for ensuring at least () detected regions statistically different from normal references. As expected, power increase with increasing sample size for a fixed significance level. The second application is the distinguishment of high-grade MDS patients from low-grade ones. We propose to calculate the General Variant Level (GVL) score to integrate the general information of each patient at genotype level, and use it as the unified measurement for the classification. Traditional MDS classifications usually refer to cell morphology and The International Prognostic Scoring System (IPSS), which belongs to the classification at the phenotype level. The proposed GVL score integrates the information of CNA region, the number of abnormal chromosomes and the total number of the altered SNPs at the genotype level. Statistical tests indicate that the high and low grade MDS patients can be well separated by GVL score, which appears to correlate better with clinical outcome than the traditional classification approaches using morphology and IPSS sore at the phenotype level.
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DOI：
10.1073/pnas.011404098
发表时间：
2001-01-02
影响因子：
11.1
作者：
Li, C;Wong, WH
通讯作者：
Wong, WH
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
Tusher, VG;Tibshirani, R;Chu, G
通讯作者：
Chu, G
影响因子：
20.3
作者：
Greenberg, P;Cox, C;Bennett, J
通讯作者：
Bennett, J
影响因子：
7
作者：
Thomas, JG;Olson, JM;Zhao, LP
通讯作者：
Zhao, LP