Jones Lesley, Goldstein Darlene R, Hughes Gareth, Strand Andrew D, Collin Francois, Dunnett Stephen B, Kooperberg Charles, Aragaki Aaron, Olson James M, Augood Sarah J, Faull Richard L M, Luthi-Carter Ruth, Moskvina Valentina, Hodges Angela K
Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK.
BMC Bioinformatics. 2006 Apr 19;7:211. doi: 10.1186/1471-2105-7-211.
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.
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 < 0.0001) correlated RNA integrity and yield with post chip quality control (QC) measures indexing 3'/5' RNA ratios, bias or scaling of the chip and scaling of the variability of the signal across the chip. Post-mortem interval was relatively unimportant. We also found that the RNA integrity number (RIN) could be moderately well predicted by post-chip measures B_ACTIN35, GAPDH35 and SF.
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.
基因表达微阵列实验成本高昂，且对于样本与芯片杂交前后中间步骤可接受的质量控制指南尚不明确。我们进行了一项实验，将来自人类大脑的RNA与117个Affymetrix U133A基因芯片进行杂交，并利用这些数据探索4个芯片前变量与22个芯片后结果及质量控制指标之间的关系。
我们发现芯片前变量之间存在显著相关性，但这种相关性在RNA质量和cRNA产量指标之间最为强烈。死后间隔时间与这些变量呈负相关。反映阵列异常值、阵列调整、杂交噪声和RNA完整性的四个主成分解释了芯片后测量总变异性的约75%。芯片前和芯片后变量之间存在两个显著的典型相关性，这些相关性来自MAS 5.0、dChip以及Bioconductor软件包affy和affyPLM。最强的相关性（典型相关系数0.838，p < 0.0001）是RNA完整性和产量与芯片后质量控制（QC）指标之间的相关性，这些指标用于衡量3'/5' RNA比率、芯片的偏差或缩放以及芯片上信号变异性的缩放。死后间隔时间相对不太重要。我们还发现，芯片后测量指标B_ACTIN35、GAPDH35和SF可以对RNA完整性数值（RIN）进行适度良好的预测。
我们发现，与杂交前可测量的量关联最强的芯片后变量是那些反映RNA完整性的变量。质量的其他方面，如噪声指标（反映实验的执行情况）或反映数据质量的指标（异常值状态和阵列调整变量），无法通过我们能够提前确定的变量得到很好的预测。可能存在其他杂交前可测量的变量，它们可能与表达数据质量指标有更好的关联。揭示这些联系可以通过在杂交前剔除质量差的样本，节省昂贵的微阵列实验成本。
