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Assessment of the relationship between pre-chip and post-chip quality measures for Affymetrix GeneChip expression data

机译:评估芯片预片和芯片后芯片质量措施的关系

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Background Gene expression microarray experiments are expensive to conduct and guidelines for acceptable quality control at intermediate steps before and after the samples are hybridised to chips are vague. We conducted an experiment hybridising RNA from human brain to 117 U133A Affymetrix GeneChips and used these data to explore the relationship between 4 pre-chip variables and 22 post-chip outcomes and quality control measures. Results We found that the pre-chip variables were significantly correlated with each other but that this correlation was strongest between measures of RNA quality and cRNA yield. Post-mortem interval was negatively correlated with these variables. Four principal components, reflecting array outliers, array adjustment, hybridisation noise and RNA integrity, explain about 75% of the total post-chip measure variability. Two significant canonical correlations existed between the pre-chip and post-chip variables, derived from MAS 5.0, dChip and the Bioconductor packages affy and affyPLM. The strongest (CANCOR 0.838, p Conclusion We have found that the post-chip variables having the strongest association with quantities measurable before hybridisation are those reflecting RNA integrity. Other aspects of quality, such as noise measures (reflecting the execution of the assay) or measures reflecting data quality (outlier status and array adjustment variables) are not well predicted by the variables we were able to determine ahead of time. There could be other variables measurable pre-hybridisation which may be better associated with expression data quality measures. Uncovering such connections could create savings on costly microarray experiments by eliminating poor samples before hybridisation.
机译:背景技术基因表达微阵列实验是昂贵的,用于对样品杂交之前和之后的中间步骤在中间步骤中可接受的质量控制的准则含糊不清。我们将一种从人脑进行杂交RNA的实验RNA至117 U133A Affymetrix Geachips,并使用这些数据来探讨4个芯片成分和22种后果后果和质量控制措施之间的关系。结果发现,芯片预变量彼此显着相关,但这种相关性在RNA质量和CRNA产量的测量之间最强。后验证间隔与这些变量负相关。四个主要成分,反映阵列异常值,阵列调整,杂交噪声和RNA完整性,解释了芯片后芯片总尺寸变异性的约75%。在芯片上和芯片后变量之间存在两种显着的规范相关性,源自MAS 5.0,DCHIP和Biocumond封装累积和补贴。最强的(癌症0.838,P结论我们发现,在杂交前可测量的数量具有最强的芯片变量是反映RNA完整性的那些。质量的其他方面,例如噪声测量(反映测定的执行)或反映数据质量的措施(异常值状态和阵列调整变量)不受我们能够提前确定的变量预测的。可能还有其他变量可测量的预杂交,这可能与表达数据质量措施更好。揭示通过在杂交前消除差的样品,连接可以节省昂贵的微阵列实验。

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