A Simulation Study of Statistical Methods for Evaluation of Systematic Bias in Gene Expression Data from Microarray Experiments
Date Issued
2006
Date
2006
Author(s)
Lu, Hsin-Pei
DOI
en-US
Abstract
Microarray technology is one of the breakthrough technologies in the twenty-first century. But only recently, the US Food and Drug Administration (FDA) approved the first biochip product based on the microarray technology. One of the primary reasons is the systematic bias of intensity measurements on gene expression data obtained from different laboratories and between different array platforms. Ideally the replicated intensity measurements of the same genes obtained from different laboratories or between different platforms should be same. Therefore, regression approaches for method comparison can be applied to assess the systematic bias, especially at the medical decision thresholds. However, replicated intensity measurements from different laboratories or between different platforms are subject to random errors. Consequently, ordinary linear regression (OLR) is not appropriate and simple Deming regression (SDR), and iteratively reweighted general Deming regression (IRGDR) should be used when both measurements contain random error. On the other hand, expression data of different genes are not independent. Impact of correlation of intensity measurements among different genes upon the estimation of slope, intercept, systematic bias, and their corresponding confidence intervals is not known. Under various combinations of intercept, slope, systematic bias, decision points, structures of random errors, correlations, and sample size, we conducted a simulation study to empirically compare performance of OLR, SDR, and IRGDR in estimating bias of the parameters and coverage probability. Numeric data from published papers illustrate the applications.
Subjects
方法比較
系統誤差
實驗決策閥值
量測誤差
戴明迴歸
相關
Method comparison
Systematic bias
Medical decision threshold
Measurement errors
Deming regression
Correlation
Type
thesis