Advanced analysis and visualization of gene copy number and expression data.

BACKGROUND: Gene copy number and gene expression values play important roles in cancer initiation and progression. Both can be measured with high-throughput microarrays and some methodologies to integrate and analyze these data exist. However, varying gene sets within different gene expression and copy number microarrays present significant challenges. RESULTS: We report an advanced version of earlier published CGH-Plotter that rapidly can identify amplified and deleted areas using gene copy number data. With CGH-Plotter v2, the copy number values can be filtered based on the genomic location in basepair units. After filtering, the values for the missing genes can be interpolated. Moreover, the effect of non-informative areas in the genome can be systematically removed by smoothing and interpolating. Further, we developed a tool (ECN) to illustrate the CGH-data values annotated based on the gene expression. The ECN-tool is a MATLAB toolbox enabling straightforward illustration of copy numbers annotated based on the gene expression levels. CONCLUSION: CGH-Plotter v2 provides two methods for analyzing copy number data; dynamic programming and genomic location based smoothing. With ECN-tool the data analyzed with CGH-Plotter v2 can easily be illustrated along the chromosomes individually or along the whole genome. ECN-tool plots the copy number data annotated based on the gene expression data, and it is easy to find the genes that are both over-expressed and amplified or under-expressed and deleted in the samples. From the resulting figures it is straightforward to select interesting genes.

Rapid identification of bacterial pathogens using a PCR- and microarray-based assay.

BACKGROUND: During the course of a bacterial infection, the rapid identification of the causative agent(s) is necessary for the determination of effective treatment options. We have developed a method based on a modified broad-range PCR and an oligonucleotide microarray for the simultaneous detection and identification of 12 bacterial pathogens at the species level. The broad-range PCR primer mixture was designed using conserved regions of the bacterial topoisomerase genes gyrB and parE. The primer design allowed the use of a novel DNA amplification method, which produced labeled, single-stranded DNA suitable for microarray hybridization. The probes on the microarray were designed from the alignments of species- or genus-specific variable regions of the gyrB and parE genes flanked by the primers. We included mecA-specific primers and probes in the same assay to indicate the presence of methicillin resistance in the bacterial species. The feasibility of this assay in routine diagnostic testing was evaluated using 146 blood culture positive and 40 blood culture negative samples. RESULTS: Comparison of our results with those of a conventional culture-based method revealed a sensitivity of 96% (initial sensitivity of 82%) and specificity of 98%. Furthermore, only one cross-reaction was observed upon investigating 102 culture isolates from 70 untargeted bacteria. The total assay time was only three hours, including the time required for the DNA extraction, PCR and microarray steps in sequence. CONCLUSION: The assay rapidly provides reliable data, which can guide optimal antimicrobial treatment decisions in a timely manner.

High-resolution copy number and gene expression microarray analyses of head and neck squamous cell carcinoma cell lines of tongue and larynx.

Gene amplifications and deletions are frequent in head and neck squamous cell carcinomas (SCC) but the association of these alterations with gene expression is mostly unknown. Here, we characterized genome-wide copy number and gene expression changes on microarrays for 18 oral tongue SCC (OTSCC) cell lines. We identified a number of altered regions including nine high-level amplifications such as 6q12-q14 (CD109, MYO6), 9p24 (JAK2, CD274, SLC1A1, RLN1), 11p12-p13 (TRAF6, COMMD9, TRIM44, FJX1, CD44, PDHX, APIP), 11q13 (FADD, PPFIA1, CTTN), and 14q24 (ABCD4, HBLD1, LTBP2, ZNF410, COQ6, ACYP1, JDP2) where 9% to 64% of genes showed overexpression. Across the whole genome, 26% of the amplified genes had associated overexpression in OTSCC. Furthermore, our data implicated that OTSCC cell lines harbored similar genomic alterations as laryngeal SCC cell lines We have previously analyzed, suggesting that despite differences in clinicopathological features there are no marked differences in molecular genetic alterations of these two HNSCC sites. To identify genes whose expression was associated with copy number increase in head and neck SCC, a statistical analysis for oral tongue and laryngeal SCC cell line data were performed. We pinpointed 1,192 genes that had a statistically significant association between copy number and gene expression. These results suggest that genomic alterations with associated gene expression changes play an important role in the malignant behavior of head and neck SCC. The identified genes provide a basis for further functional validation and may lead to the identification of novel candidates for targeted therapies. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat.

Optimized LOWESS normalization parameter selection for DNA microarray data.

BACKGROUND: Microarray data normalization is an important step for obtaining data that are reliable and usable for subsequent analysis. One of the most commonly utilized normalization techniques is the locally weighted scatterplot smoothing (LOWESS) algorithm. However, a much overlooked concern with the LOWESS normalization strategy deals with choosing the appropriate parameters. Parameters are usually chosen arbitrarily, which may reduce the efficiency of the normalization and result in non-optimally normalized data. Thus, there is a need to explore LOWESS parameter selection in greater detail. RESULTS AND DISCUSSION: In this work, we discuss how to choose parameters for the LOWESS method. Moreover, we present an optimization approach for obtaining the fraction of data points utilized in the local regression and analyze results for local print-tip normalization. The optimization procedure determines the bandwidth parameter for the local regression by minimizing a cost function that represents the mean-squared difference between the LOWESS estimates and the normalization reference level. We demonstrate the utility of the systematic parameter selection using two publicly available data sets. The first data set consists of three self versus self hybridizations, which allow for a quantitative study of the optimization method. The second data set contains a collection of DNA microarray data from a breast cancer study utilizing four breast cancer cell lines. Our results show that different parameter choices for the bandwidth window yield dramatically different calibration results in both studies. CONCLUSIONS: Results derived from the self versus self experiment indicate that the proposed optimization approach is a plausible solution for estimating the LOWESS parameters, while results from the breast cancer experiment show that the optimization procedure is readily applicable to real-life microarray data normalization. In summary, the systematic approach to obtain critical parameters in the LOWESS technique is likely to produce data that optimally meets assumptions made in the data preprocessing step and thereby makes studies utilizing the LOWESS method unambiguous and easier to repeat.

Evaluation of multiplex polymerase chain reaction and microarray-based assay for rapid herpesvirus diagnostics.

Rapid diagnosis is critical to minimize morbidity and mortality associated with infections of the central nervous system (CNS). In this study, we evaluated the performance of a multiplex polymerase chain reaction (PCR) and microarray-based method, Prove-it™ Herpes, in a routine clinical laboratory setting for the diagnostics of 7 herpesviruses in viral CNS infections. Cerebrospinal fluid samples (n = 495), which had arrived for diagnostics in the 5 participating laboratories, were analyzed for herpesvirus DNA both by the current PCR-based method of the laboratory and by the microarray assay. The sensitivity and specificity for the microarray assay were 93% and 99%, respectively. The microarray assay was considered as a rapid and robust diagnostic platform that was easily implemented into the laboratory workflow. The broad herpesvirus coverage and the small sample volume required by the assay could benefit the diagnostics and thus the treatment of life-threatening infections of the CNS, especially among immunocompromised patients.

Are data from different gene expression microarray platforms comparable?

Many commercial and custom-made microarray formats are routinely used for large-scale gene expression surveys. Here, we sought to determine the level of concordance between microarray platforms by analyzing breast cancer cell lines with in situ synthesized oligonucleotide arrays (Affymetrix HG-U95v2), commercial cDNA microarrays (Agilent Human 1 cDNA), and custom-made cDNA microarrays from a sequence-validated 13K cDNA library. Gene expression data from the commercial platforms showed good correlations across the experiments (r = 0.78-0.86), whereas the correlations between the custom-made and either of the two commercial platforms were lower (r = 0.62-0.76). Discrepant findings were due to clone errors on the custom-made microarrays, old annotations, or unknown causes. Even within platform, there can be several ways to analyze data that may influence the correlation between platforms. Our results indicate that combining data from different microarray platforms is not straightforward. Variability of the data represents a challenge for developing future diagnostic applications of microarrays.

A novel strategy for microarray quality control using Bayesian networks.

MOTIVATION: High-throughput microarray technologies enable measurements of the expression levels of thousands of genes in parallel. However, microarray printing, hybridization and washing may create substantial variability in the quality of the data. As erroneous measurements may have a drastic impact on the results by disturbing the normalization schemes and by introducing expression patterns that lead to incorrect conclusions, it is crucial to discard low quality observations in the early phases of a microarray experiment. A typical microarray experiment consists of tens of thousands of spots on a microarray, making manual extraction of poor quality spots impossible. Thus, there is a need for a reliable and general microarray spot quality control strategy. RESULTS: We suggest a novel strategy for spot quality control by using Bayesian networks, which contain many appealing properties in the spot quality control context. We illustrate how a non-linear least squares based Gaussian fitting procedure can be used in order to extract features for a spot on a microarray. The features we used in this study are: spot intensity, size of the spot, roundness of the spot, alignment error, background intensity, background noise, and bleeding. We conclude that Bayesian networks are a reliable and useful model for microarray spot quality assessment. SUPPLEMENTARY INFORMATION: http://sigwww.cs.tut.fi/TICSP/SpotQuality/.