Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Anzio Road, Observatory, Cape Town, 7925, South Africa.
BMC Genomics. 2013 Jan 16;14:14. doi: 10.1186/1471-2164-14-14.
RNA and microarray quality assessment form an integral part of gene expression analysis and, although methods such as the RNA integrity number (RIN) algorithm reliably asses RNA integrity, the relevance of RNA integrity in gene expression analysis as well as analysis methods to accommodate the possible effects of degradation requires further investigation. We investigated the relationship between RNA integrity and array quality on the commonly used Affymetrix Gene 1.0 ST array platform using reliable within-array and between-array quality assessment measures. The possibility of a transcript specific bias in the apparent effect of RNA degradation on the measured gene expression signal was evaluated after either excluding quality-flagged arrays or compensation for RNA degradation at different steps in the analysis.
Using probe-level and inter-array quality metrics to assess 34 Gene 1.0 ST array datasets derived from historical, paired tumour and normal primary colorectal cancer samples, 7 arrays (20.6%), with a mean sample RIN of 3.2 (SD = 0.42), were flagged during array quality assessment while 10 arrays from samples with RINs < 7 passed quality assessment, including one sample with a RIN < 3. We detected a transcript length bias in RNA degradation in only 5.8% of annotated transcript clusters (p-value 0.05, FC ≥ |2|), with longer and shorter than average transcripts under- and overrepresented in quality-flagged samples respectively. Applying compensatory measures for RNA degradation performed at least as well as excluding quality-flagged arrays, as judged by hierarchical clustering, gene expression analysis and Ingenuity Pathway Analysis; importantly, use of these compensatory measures had the significant benefit of enabling lower quality array data from irreplaceable clinical samples to be retained in downstream analyses.
Here, we demonstrate an effective array-quality assessment strategy, which will allow the user to recognize lower quality arrays that can be included in the analysis once appropriate measures are applied to account for known or unknown sources of variation, such as array quality- and batch- effects, by implementing ComBat or Surrogate Variable Analysis. This approach of quality control and analysis will be especially useful for clinical samples with variable and low RNA qualities, with RIN scores ≥ 2.
RNA 和微阵列质量评估是基因表达分析的一个组成部分，虽然 RNA 完整性数量 (RIN) 算法等方法可靠地评估 RNA 完整性，但 RNA 完整性在基因表达分析中的相关性以及适应降解可能影响的分析方法需要进一步研究。我们使用可靠的内部和外部阵列质量评估方法，研究了 RNA 完整性与常用的 Affymetrix Gene 1.0 ST 阵列平台上的阵列质量之间的关系。在分析的不同步骤排除质量标记的阵列或补偿 RNA 降解后，评估了 RNA 降解对测量基因表达信号的明显影响中是否存在转录特异性偏差的可能性。
使用探针级和阵列间质量指标评估 34 个源自历史、配对肿瘤和正常原发性结直肠癌样本的 Gene 1.0 ST 阵列数据集，在阵列质量评估过程中标记了 7 个（20.6%）阵列，其平均样本 RIN 为 3.2（SD = 0.42），而 10 个 RIN < 7 的样本通过质量评估，其中包括一个 RIN < 3 的样本。我们仅在 5.8%的注释转录簇中检测到 RNA 降解的转录长度偏差（p 值 0.05，FC ≥ |2|），质量标记样本中长于和短于平均值的转录物分别被低估和高估。通过层次聚类、基因表达分析和 Ingenuity Pathway Analysis 判断，应用 RNA 降解的补偿措施至少与排除质量标记的阵列一样有效；重要的是，这些补偿措施的显著优势是能够保留下游分析中无法替代的临床样本中质量较低的阵列数据。
在这里，我们展示了一种有效的阵列质量评估策略，该策略将允许用户识别较低质量的阵列，一旦应用适当的措施来解释已知或未知的变化来源，例如阵列质量和批次效应，就可以将其包含在分析中，例如通过实施 ComBat 或 Surrogate Variable Analysis。这种质量控制和分析方法对于 RNA 质量可变且较低的临床样本特别有用，RIN 评分≥2。
