Using RNA sample titrations to assess microarray platform performance and normalization techniques

We have assessed the utility of RNA titration samples for evaluating microarray platform performance and the impact of different normalization methods on the results obtained. As part of the MicroArray Quality Control project, we investigated the performance of five commercial microarray platforms using two independent RNA samples and two titration mixtures of these samples. Focusing on 12,091 genes common across all platforms, we determined the ability of each platform to detect the correct titration response across the samples. Global deviations from the response predicted by the titration ratios were observed. These differences could be explained by variations in relative amounts of messenger RNA as a fraction of total RNA between the two independent samples. Overall, both the qualitative and quantitative correspondence across platforms was high. In summary, titration samples may be regarded as a valuable tool, not only for assessing microarray platform performance and different analysis methods, but also for determining some underlying biological features of the samples.     

Rat mitochondrion-neuron focused microarray (rMNChip) and bioinformatics tools for rapid identification of differential pathways in brain tissues

Mitochondrial function is of particular importance in brain because of its high demand for energy (ATP) and efficient removal of reactive oxygen species (ROS). We developed rat mitochondrion-neuron focused microarray (rMNChip) and integrated bioinformatics tools for rapid identification of differential pathways in brain tissues. rMNChip contains 1,500 genes involved in mitochondrial functions, stress response, circadian rhythms and signal transduction. The bioinformatics tool includes an algorithm for computing of differentially expressed genes, and a database for straightforward and intuitive interpretation for microarray results. Our application of these tools to RNA samples derived from rat frontal cortex (FC), hippocampus (HC) and hypothalamus (HT) led to the identification of differentially-expressed signal-transduction-bioenergenesis and neurotransmitter-synthesis pathways with a dominant number of genes (FC/HC = 55/6; FC/HT = 55/4) having significantly (p<0.05, FDR<10.70%) higher (≥1.25 fold) RNA levels in the frontal cortex than the others, strongly suggesting active generation of ATP and neurotransmitters and efficient removal of ROS. Thus, these tools for rapid and efficient identification of differential pathways in brain regions will greatly facilitate our systems-biological study and understanding of molecular mechanisms underlying complex and multifactorial neurodegenerative diseases.     

利用GenMAPP筛查鼻咽癌差异表达基因

利用GenMAPP软件对鼻咽癌和正常鼻咽上皮基因微阵列表达谱结果进行分析,筛查鼻咽癌差异表达基因. 结果显示：在17 000个基因中,与正常鼻咽上皮相比,在鼻咽癌中发生2倍以上差异表达的基因共有339个,其中有160个基因在鼻咽癌中表达上调,179个表达下调. 这些基因分别与细胞增殖、基因转录、凋亡、信号转导、DNA损伤修复、肿瘤分化和浸润转移及细胞周期调节等相关. 鼻咽癌的发生发展存在多基因表达调控的改变,对其差异表达基因的研究有助于阐明鼻咽癌发生发展机制.     

A model of technical variation of microarray signals.

We describe a mathematical model of signal from single-channel direct hybridization microarray platforms. The model establishes a linear relationship between microarray signals and their standard deviations from a minimum set of assumptions. We use the model to precisely define important microarray quality characteristics: resolved fold change and dynamic range. The definitions lead to closed form expressions relating these characteristics to physical parameters of the microarray experiment in the case when both specific and nonspecific binding of target to probe are governed by the Langmuir hybridization isotherm. The predictions of the model are in close agreement to data obtained from spike-in experiments. Given the generality of the model, the introduced definitions of dynamic range and resolved concentration fold-change can be used to conduct cross-platform comparisons and to guide improvement of the microarray platform.     

Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation

Background

Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study.

Results

Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this “gold-standard” comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues.

Conclusions

Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-649) contains supplementary material, which is available to authorized users.     

Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data

MOTIVATION: DNA microarray data analysis has been used previously to identify marker genes which discriminate cancer from normal samples. However, due to the limited sample size of each study, there are few common markers among different studies of the same cancer. With the rapid accumulation of microarray data, it is of great interest to integrate inter-study microarray data to increase sample size, which could lead to the discovery of more reliable markers. RESULTS: We present a novel, simple method of integrating different microarray datasets to identify marker genes and apply the method to prostate cancer datasets. In this study, by applying a new statistical method, referred to as the top-scoring pair (TSP) classifier, we have identified a pair of robust marker genes (HPN and STAT6) by integrating microarray datasets from three different prostate cancer studies. Cross-platform validation shows that the TSP classifier built from the marker gene pair, which simply compares relative expression values, achieves high accuracy, sensitivity and specificity on independent datasets generated using various array platforms. Our findings suggest a new model for the discovery of marker genes from accumulated microarray data and demonstrate how the great wealth of microarray data can be exploited to increase the power of statistical analysis. CONTACT: leixu@jhu.edu.     

Assessing the Reliability of Amplified RNA Used in Microarrays

A certain minimal amount of RNA from biological samples is necessary to perform a microarray experiment with suitable replication. In some cases, the amount of RNA available is insufficient, necessitating RNA amplification prior to target synthesis. However, there is some uncertainty about the reliability of targets that have been generated from amplified RNA, because of nonlinearity and preferential amplification. This current work develops a straightforward strategy to assess the reliability of microarray data obtained from amplified RNA. The tabular method we developed, which utilises a Down-Up-Missing-Below (DUMB) classification scheme, shows that microarrays generated with amplified RNA targets are reliable within constraints. There was an increase in false negatives because of the need for increased filtering. Furthermore, this analysis method is generic and can be broadly applied to evaluate all microarray data. A copy of the Microsoft Excel spreadsheet is available upon request from Edward Bearden.     

Standardizing global gene expression analysis between laboratories and across platforms

To facilitate collaborative research efforts between multi-investigator teams using DNA microarrays, we identified sources of error and data variability between laboratories and across microarray platforms, and methods to accommodate this variability. RNA expression data were generated in seven laboratories, which compared two standard RNA samples using 12 microarray platforms. At least two standard microarray types (one spotted, one commercial) were used by all laboratories. Reproducibility for most platforms within any laboratory was typically good, but reproducibility between platforms and across laboratories was generally poor. Reproducibility between laboratories increased markedly when standardized protocols were implemented for RNA labeling, hybridization, microarray processing, data acquisition and data normalization. Reproducibility was highest when analysis was based on biological themes defined by enriched Gene Ontology (GO) categories. These findings indicate that microarray results can be comparable across multiple laboratories, especially when a common platform and set of procedures are used.     

Microarray-driven validation of reference genes for quantitative real-time polymerase chain reaction in a rat vocal fold model of mucosal injury

Relative quantification by normalization against a stably expressed reference gene is a widely used data analysis method in microarray and quantitative real-time polymerase chain reaction (qRT-PCR) platforms; however, recent evidence suggests that many commonly utilized reference genes are unstable in certain experimental systems and situations. The primary aim of this study, therefore, was to screen and identify stably expressed reference genes in a well-established rat model of vocal fold mucosal injury. We selected and evaluated the expression stability of nine candidate reference genes. Ablim1, Sptbn1, and Wrnip1 were identified as stably expressed in a model-specific microarray dataset and were further validated as suitable reference genes in an independent qRT-PCR experiment using 2^−ΔCT and pairwise comparison-based (geNorm) analyses. Parallel analysis of six commonly used reference genes identified Sdha as the only stably expressed candidate in this group. Sdha, Sptbn1, and the geometric mean of Sdha and Sptbn1 each provided accurate normalization of target gene Tgfb1; Gapdh, the least stable candidate gene in our dataset, provided inaccurate normalization and an invalid experimental result. The stable reference genes identified here are suitable for accurate normalization of target gene expression in vocal fold mucosal injury experiments.     

Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells

ABSTRACT: BACKGROUND: In the postgenome era, a prediction of response to treatment could lead to better dose selection for patients in radiotherapy. To identify a radiosensitive gene signature and elucidate related signaling pathways, four different microarray experiments were reanalyzed before radiotherapy. RESULTS: Radiosensitivity profiling data using clonogenic assay and gene expression profiling data from four published microarray platforms applied to NCI-60 cancer cell panel were used. The survival fraction at 2 Gy (SF2, range from 0 to 1) was calculated as a measure of radiosensitivity and a linear regression model was applied to identify genes or a gene set with a correlation between expression and radiosensitivity (SF2). Radiosensitivity signature genes were identified using significant analysis of microarrays (SAM) and gene set analysis was performed using a global test using linear regression model. Using the radiation-related signaling pathway and identified genes, a genetic network was generated. According to SAM, 31 genes were identified as common to all the microarray platforms and therefore a common radiosensitivity signature. In gene set analysis, functions in the cell cycle, DNA replication, and cell junction, including adherence and gap junctions were related to radiosensitivity. The integrin, VEGF, MAPK, p53, JAK-STAT and Wnt signaling pathways were overrepresented in radiosensitivity. Significant genes including ACTN1, CCND1, HCLS1, ITGB5, PFN2, PTPRC, RAB13, and WAS, which are adhesion-related molecules that were identified by both SAM and gene set analysis, and showed interaction in the genetic network with the integrin signaling pathway. CONCLUSIONS: Integration of four different microarray experiments and gene selection using gene set analysis discovered possible target genes and pathways relevant to radiosensitivity. Our results suggested that the identified genes are candidates for radiosensitivity biomarkers and that integrin signaling via adhesion molecules could be a target for radiosensitization.     

Independence and reproducibility across microarray platforms

Microarrays have been widely used for the analysis of gene expression, but the issue of reproducibility across platforms has yet to be fully resolved. To address this apparent problem, we compared gene expression between two microarray platforms: the short oligonucleotide Affymetrix Mouse Genome 430 2.0 GeneChip and a spotted cDNA array using a mouse model of angiotensin II-induced hypertension. RNA extracted from treated mice was analyzed using Affymetrix and cDNA platforms and then by quantitative RT-PCR (qRT-PCR) for validation of specific genes. For the 11,710 genes present on both arrays, we assessed the relative impact of experimental treatment and platform on measured expression and found that biological treatment had a far greater impact on measured expression than did platform for more than 90% of genes, a result validated by qRT-PCR. In the small number of cases in which platforms yielded discrepant results, qRT-PCR generally did not confirm either set of data, suggesting that sequence-specific effects may make expression predictions difficult to make using any technique.     

Identification and Validation of Suitable Housekeeping Genes for Normalizing Quantitative Real-Time PCR Assays in Injured Peripheral Nerves

Injury to the peripheral nerve induces dramatic changes in terms of cellular composition that are reflected on RNA quality and quantity, making messenger RNA expression analysis very complex. Several commonly used housekeeping genes are regulated following peripheral nerve injury and are thus not suitable for quantitative real-time PCR normalization; moreover, the presence of pseudogenes in some of them impairs their use. To deal with this problem, we have developed a new method to identify new stable housekeeping genes based on publicly available microarray data on normal and injured nerves. Four new candidate stable genes were identified and validated by quantitative real-time PCR analysis on nerves during the different phases after nerve injury: nerve degeneration, regeneration and remyelination. The stability measure of these genes was calculated with both NormFinder and geNorm algorithms and compared with six commonly used housekeeping genes. This procedure allowed us to identify two new and highly stable genes that can be employed for normalizing injured peripheral nerve data: ANKRD27 and RICTOR. Besides providing a tool for peripheral nerve research, our study also describes a simple and cheap procedure that can be used to identify suitable housekeeping genes in other tissues and organs.     

Arrow Plot: a new graphical tool for selecting up and down regulated genes and genes differentially expressed on sample subgroups

ABSTRACT: BACKGROUND: A common task in analyzing microarray data is to determine which genes are differentially expressed across two (or more) kind of tissue samples or samples submitted under experimental conditions. Several statistical methods have been proposed to accomplish this goal, generally based on measures of distance between classes. It is well known that biological samples are heterogeneous because of factors such as molecular subtypes or genetic background that are often unknown to the experimenter. For instance, in experiments which involve molecular classification of tumors it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures. Consequently, there can be genes differentially expressed on sample subgroups which are missed if usual statistical approaches are used. In this paper we propose a new graphical tool which not only identifies genes with up and down regulations, but also genes with differential expression in different subclasses, that are usually missed if current statistical methods are used. This tool is based on two measures of distance between samples, namely the overlapping coefficient (OVL) between two densities and the area under the receiver operating characteristic (ROC) curve. The methodology proposed here was implemented in the open-source R software. RESULTS: This method was applied to a publicly available dataset, as well as to a simulated dataset. We compared our results with the ones obtained using some of the standard methods for detecting differentially expressed genes, namely Welch t-statistic, fold change (FC), rank products (RP), average difference (AD), weighted average difference (WAD), moderated t-statistic (modT), intensity-based moderated t-statistic (ibmT), significance analysis of microarrays (samT) and area under the ROC curve (AUC). On both datasets all differentially expressed genes with bimodal or multimodal distributions were not selected by all standard selection procedures. We also compared our results with (i) area between ROC curve and rising area (ABCR) and (ii) the test for not proper ROC curves (TNRC). We found our methodology more comprehensive, because it detects both bimodal and multimodal distributions and different variances can be considered on both samples. Another advantage of our method is that we can analyze graphically the behavior of different kinds of differentially expressed genes. CONCLUSION: Our results indicate that the arrow plot represents a new flexible and useful tool for the analysis of gene expression profiles from microarrays.     

Microarrays: technologies overview and data analysis

DNA microarrays are a powerful tool to investigate differential gene expression for thousands of genes simultaneously. In this review, recent advances in DNA microarray technologies and their applications are examined. Various DNA microarray platforms are described along with their methods for fabrication and their use. In addition some algorithms and tools for the analysis of microarray expression data, including clustering methods, partitioning and machine learning methods are discussed.