Project description:Microarray technology provides a powerful tool for defining gene expression profiles of airway epithelium that lend insight into the pathogenesis of human airway disorders. The focus of this study was to establish rigorous quality control parameters to ensure that microarray assessment of the airway epithelium is not confounded by experimental artifact. Samples (total n=223) of trachea, large and small airway epithelium were collected by fiberoptic bronchoscopy of 144 individuals (42 healthy non-smokers, 49 healthy smokers, 11 symptomatic smokers, 22 smokers with lone emphysema with normal spirometry, and 20 smokers with COPD) were processed and hybridized to Affymetrix HG-U133 2.0 Plus microarrays. The pre- and post-chip quality control (QC) criteria established, included: (1) RNA quality, assessed by RNA Integrity Number (RIN) ≥7.0 using Agilent 2100 Bioanalyzer software; (2) cRNA transcript integrity, assessed by signal intensity ratio of GAPDH 3' to 5' probe sets ≤3.0; and (3) the multi-chip normalization scaling factor ≤10.0 Of the 223 samples, 213 (95.5%) passed the QC criteria. In a data set of 34 arrays (10 samples failing QC criteria, 24 randomly chosen samples passing QC criteria), correlation coefficients for pairwise comparisons of expression levels for 100 housekeeping genes in which at least one array failed the QC criteria were significantly lower (average Pearson r = 0.90 ± 0.04) and more broadly dispersed than correlation coefficients for pairwise comparisons between any two arrays that passed the QC criteria (average Pearson r = 0.97 ± 0.01). By using the QC cutoff criteria, the inter-array variability, as assessed by the coefficient of variation in the expression levels for 100 housekeeping genes, was reduced from 35.7% to 21.7%. Based on the aberrant housekeeping gene data generated from samples failing the established QC criteria, we propose that the QC criteria outlined in this study can accurately distinguish high quality from low quality data and can be used to delete poor quality microarray samples before proceeding to higher-order biological analyses and interpretation.

Project description:To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided.

Project description:Microarray technology provides a powerful tool for defining gene expression profiles of airway epithelium that lend insight into the pathogenesis of human airway disorders. The focus of this study was to establish rigorous quality control parameters to ensure that microarray assessment of the airway epithelium is not confounded by experimental artifact. Samples (total n=223) of trachea, large and small airway epithelium were collected by fiberoptic bronchoscopy of 144 individuals (42 healthy non-smokers, 49 healthy smokers, 11 symptomatic smokers, 22 smokers with lone emphysema with normal spirometry, and 20 smokers with COPD) were processed and hybridized to Affymetrix HG-U133 2.0 Plus microarrays. The pre- and post-chip quality control (QC) criteria established, included: (1) RNA quality, assessed by RNA Integrity Number (RIN) ≥7.0 using Agilent 2100 Bioanalyzer software; (2) cRNA transcript integrity, assessed by signal intensity ratio of GAPDH 3' to 5' probe sets ≤3.0; and (3) the multi-chip normalization scaling factor ≤10.0 Of the 223 samples, 213 (95.5%) passed the QC criteria. In a data set of 34 arrays (10 samples failing QC criteria, 24 randomly chosen samples passing QC criteria), correlation coefficients for pairwise comparisons of expression levels for 100 housekeeping genes in which at least one array failed the QC criteria were significantly lower (average Pearson r = 0.90 ± 0.04) and more broadly dispersed than correlation coefficients for pairwise comparisons between any two arrays that passed the QC criteria (average Pearson r = 0.97 ± 0.01). By using the QC cutoff criteria, the inter-array variability, as assessed by the coefficient of variation in the expression levels for 100 housekeeping genes, was reduced from 35.7% to 21.7%. Based on the aberrant housekeeping gene data generated from samples failing the established QC criteria, we propose that the QC criteria outlined in this study can accurately distinguish high quality from low quality data and can be used to delete poor quality microarray samples before proceeding to higher-order biological analyses and interpretation. Affymetrix arrays were used to assess the quality of gene expression data in trachea, large airway and small airway epithelium obtained by fiberoptic bronchoscopy of 42 healthy non-smokers, 49 healthy smokers, 11 symptomatic smokers, 22 smokers with lone emphysema with normal spirometry, and 20 smokers with COPD.

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RawBeans: a simple, vendor independent, raw-data quality control tool

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RawBeans: A Simple, Vendor-Independent, Raw-Data Quality-Control Tool.

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