Cross-platform comparison of microarray-based multiple-class prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets.     

Transcriptional signatures of normal human mammary epithelial cells in response to benzo[a]pyrene exposure: a comparison of three microarray platforms

Microarrays are used to study gene expression in a variety of biological systems. A number of different platforms have been developed, but few studies exist that have directly compared the performance of one platform with another. The goal of this study was to determine array variation by analyzing the same RNA samples with three different array platforms. Using gene expression responses to benzo[a]pyrene exposure in normal human mammary epithelial cells (NHMECs), we compared the results of gene expression profiling using three microarray platforms: photolithographic oligonucleotide arrays (Affymetrix), spotted oligonucleotide arrays (Amersham), and spotted cDNA arrays (NCI). While most previous reports comparing microarrays have analyzed pre-existing data from different platforms, this comparison study used the same sample assayed on all three platforms, allowing for analysis of variation from each array platform. In general, poor correlation was found with corresponding measurements from each platform. Each platform yielded different gene expression profiles, suggesting that while microarray analysis is a useful discovery tool, further validation is needed to extrapolate results for broad use of the data. Also, microarray variability needs to be taken into consideration, not only in the data analysis but also in specific probe selection for each array type.     

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.     

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.     

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.     

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.     

Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses

ABSTRACT: BACKGROUND: An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. RESULTS: We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. CONCLUSIONS: Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.     

The Accuracy,Feasibility and Challenges of Sequencing Short Tandem Repeats Using Next-Generation Sequencing Platforms

To date we have little knowledge of how accurate next-generation sequencing (NGS) technologies are in sequencing repetitive sequences beyond known limitations to accurately sequence homopolymers. Only a handful of previous reports have evaluated the potential of NGS for sequencing short tandem repeats (microsatellites) and no empirical study has compared and evaluated the performance of more than one NGS platform with the same dataset. Here we examined yeast microsatellite variants from both long-read (454-sequencing) and short-read (Illumina) NGS platforms and compared these to data derived through Sanger sequencing. In addition, we investigated any locus-specific biases and differences that might have resulted from variability in microsatellite repeat number, repeat motif or type of mutation. Out of 112 insertion/deletion variants identified among 45 microsatellite amplicons in our study, we found 87.5% agreement between the 454-platform and Sanger sequencing in frequency of variant detection after Benjamini-Hochberg correction for multiple tests. For a subset of 21 microsatellite amplicons derived from Illumina sequencing, the results of short-read platform were highly consistent with the other two platforms, with 100% agreement with 454-sequencing and 93.6% agreement with the Sanger method after Benjamini-Hochberg correction. We found that the microsatellite attributes copy number, repeat motif and type of mutation did not have a significant effect on differences seen between the sequencing platforms. We show that both long-read and short-read NGS platforms can be used to sequence short tandem repeats accurately, which makes it feasible to consider the use of these platforms in high-throughput genotyping. It appears the major requirement for achieving both high accuracy and rare variant detection in microsatellite genotyping is sufficient read depth coverage. This might be a challenge because each platform generates a consistent pattern of non-uniform sequence coverage, which, as our study suggests, may affect some types of tandem repeats more than others.     

Phase separation by the SARS-CoV-2 nucleocapsid protein: Consensus and open questions

In response to the recent SARS-CoV-2 pandemic, a number of labs across the world have reallocated their time and resources to better our understanding of the virus. For some viruses, including SARS-CoV-2, viral proteins can undergo phase separation: a biophysical process often related to the partitioning of protein and RNA into membraneless organelles in vivo. In this review, we discuss emerging observations of phase separation by the SARS-CoV-2 nucleocapsid (N) protein—an essential viral protein required for viral replication—and the possible in vivo functions that have been proposed for N-protein phase separation, including viral replication, viral genomic RNA packaging, and modulation of host-cell response to infection. Additionally, since a relatively large number of studies examining SARS-CoV-2 N-protein phase separation have been published in a short span of time, we take advantage of this situation to compare results from similar experiments across studies. Our evaluation highlights potential strengths and pitfalls of drawing conclusions from a single set of experiments, as well as the value of publishing overlapping scientific observations performed simultaneously by multiple labs.     

Cross-species microarray hybridizations: a developing tool for studying species diversity

The use of cross-species hybridization (CSH) to DNA microarrays, in which the target RNA and microarray probe are from different species, has increased in the past few years. CSH is used in comparative, evolutionary and ecological studies of closely related species, and for gene-expression profiling of many species that lack a representative microarray platform. However, unlike species-specific hybridization, CSH is still considered a non-standard use of microarrays. Here, we present the recent developments in the field of CSH for cDNA and oligomer microarray platforms. We discuss issues that influence the quality of CSH results, including platform choice, experiment design and data analysis, and suggest strategies that can lead to improvement of CSH studies to investigate species diversity.     

Focus on RNA isolation: obtaining RNA for microRNA (miRNA) expression profiling analyses of neural tissue

MicroRNAs (miRNAs) are present in all known plant and animal tissues and appear to be somewhat concentrated in the mammalian nervous system. Many different miRNA expression profiling platforms have been described. However, relatively little research has been published to establish the importance of 'upstream' variables in RNA isolation for neural miRNA expression profiling. We tested whether apparent changes in miRNA expression profiles may be associated with tissue processing, RNA isolation techniques, or different cell types in the sample. RNA isolation was performed on a single brain sample using eight different RNA isolation methods, and results were correlated using a conventional miRNA microarray and then cross-referenced to Northern blots. Differing results were seen between samples obtained using different RNA isolation techniques and between microarray and Northern blot results. Another complication of miRNA microarrays is tissue-level heterogeneity of cellular composition. To investigate this phenomenon, miRNA expression profiles were determined and compared between highly-purified primary cerebral cortical cell preparations of rat primary E15-E18 neurons versus rat primary E15-E18 astrocytes. Finally, to assess the importance of dissecting human brain gray matter from subjacent white matter in cerebral cortical studies, miRNA expression profiles were compared between gray matter and immediately contiguous white matter. The results suggest that for microarray studies, cellular composition is important, and dissecting white matter from gray matter improves the specificity of the results. Based on these data, recommendations for miRNA expression profiling in neural tissues, and considerations worthy of further study, are discussed.     

Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements

Background  

Comparison of data produced on different microarray platforms often shows surprising discordance. It is not clear whether this discrepancy is caused by noisy data or by improper probe matching between platforms. We investigated whether the significant level of inconsistency between results produced by alternative gene expression microarray platforms could be reduced by stringent sequence matching of microarray probes. We mapped the short oligo probes of the Affymetrix platform onto cDNA clones of the Stanford microarray platform. Affymetrix probes were reassigned to redefined probe sets if they mapped to the same cDNA clone sequence, regardless of the original manufacturer-defined grouping. The NCI-60 gene expression profiles produced by Affymetrix HuFL platform were recalculated using these redefined probe sets and compared to previously published cDNA measurements of the same panel of RNA samples.     

Improving identification of differentially expressed genes by integrative analysis of Affymetrix and Illumina arrays

Together with the widely used Affymetrix microarrays, the recently introduced Illumina platform has become a cost-effective alternative for genome-wide studies. To efficiently use data from both array platforms, there is a pressing need for methods that allow systematic integration of multiple datasets, especially when the number of samples is small. To address these needs, we introduce a meta-analytic procedure for combining Affymetrix and Illumina data in the context of detecting differentially expressed genes between the platforms. We first investigate the effect of different expression change estimation procedures within the platforms on the agreement of the most differentially expressed genes. Using the best estimation methods, we then show the benefits of the integrative analysis in producing reproducible results across bootstrap samples. In particular, we demonstrate its biological relevance in identifying small but consistent changes during T helper 2 cell differentiation.     

Current issues for DNA microarrays: platform comparison, double linear amplification, and universal RNA reference

DNA microarray technology has been widely used to simultaneously determine the expression levels of thousands of genes. A variety of approaches have been used, both in the implementation of this technology and in the analysis of the large amount of expression data. However, several practical issues still have not been resolved in a satisfactory manner, and among the most critical is the lack of agreement in the results obtained in different array platforms. In this study, we present a comparison of several microarray platforms [Affymetrix oligonucleotide arrays, custom complementary DNA (cDNA) arrays, and custom oligo arrays printed with oligonucleotides from three different sources] as well as analysis of various methods used for microarray target preparation and the reference design. The results indicate that the pairwise correlations of expression levels between platforms are relative low overall but that the log ratios of the highly expressed genes are strongly correlated, especially between Affymetrix and cDNA arrays. The microarray measurements were compared with quantitative real-time-polymerase chain reaction (QRT-PCR) results for 23 genes, and the varying degrees of agreement for each platform were characterized. We have also developed and tested a double amplification method which allows the use of smaller amounts of starting material. The added round of amplification produced reproducible results as compared to the arrays hybridized with single round amplified targets. Finally, the reliability of using a universal RNA reference for two-channel microarrays was tested and the results suggest that comparisons of multiple experimental conditions using the same control can be accurate.