RNA-Seq vs Dual- and Single-Channel Microarray Data: Sensitivity Analysis for Differential Expression and Clustering

With the fast development of high-throughput sequencing technologies, a new generation of genome-wide gene expression measurements is under way. This is based on mRNA sequencing (RNA-seq), which complements the already mature technology of microarrays, and is expected to overcome some of the latter’s disadvantages. These RNA-seq data pose new challenges, however, as strengths and weaknesses have yet to be fully identified. Ideally, Next (or Second) Generation Sequencing measures can be integrated for more comprehensive gene expression investigation to facilitate analysis of whole regulatory networks. At present, however, the nature of these data is not very well understood. In this paper we study three alternative gene expression time series datasets for the Drosophila melanogaster embryo development, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays. The aim is to study the state of the art for the three technologies, with a view of assessing overlapping features, data compatibility and integration potential, in the context of time series measurements. This involves using established tools for each of the three different technologies, and technical and biological replicates (for RNA-seq and microarrays, respectively), due to the limited availability of biological RNA-seq replicates for time series data. The approach consists of a sensitivity analysis for differential expression and clustering. In general, the RNA-seq dataset displayed highest sensitivity to differential expression. The single-channel data performed similarly for the differentially expressed genes common to gene sets considered. Cluster analysis was used to identify different features of the gene space for the three datasets, with higher similarities found for the RNA-seq and single-channel microarray dataset.     

A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays.     

Evaluation of gene expression measurements from commercial microarray platforms

Multiple commercial microarrays for measuring genome-wide gene expression levels are currently available, including oligonucleotide and cDNA, single- and two-channel formats. This study reports on the results of gene expression measurements generated from identical RNA preparations that were obtained using three commercially available microarray platforms. RNA was collected from PANC-1 cells grown in serum-rich medium and at 24 h following the removal of serum. Three biological replicates were prepared for each condition, and three experimental replicates were produced for the first biological replicate. RNA was labeled and hybridized to microarrays from three major suppliers according to manufacturers’ protocols, and gene expression measurements were obtained using each platform’s standard software. For each platform, gene targets from a subset of 2009 common genes were compared. Correlations in gene expression levels and comparisons for significant gene expression changes in this subset were calculated, and showed considerable divergence across the different platforms, suggesting the need for establishing industrial manufacturing standards, and further independent and thorough validation of the technology.     

Efficient two-sample designs for microarray experiments with biological replications

In the last years, biostatistical research has begun to apply linear models and design theory to develop efficient experimental designs and analysis tools for gene expression microarray data. With two-colour microarrays, direct comparisons of RNA-targets are possible and lead to incomplete block designs. In this setting, efficient designs for simple and factorial microarray experiments have mainly been proposed for technical replicates. But for biological replicates, which are crucial to obtain inference that can be generalised to a biological population, this question has only been discussed recently and is not fully solved yet. In this paper, we propose efficient designs for independent two-sample experiments using two-colour microarrays enabling biologists to measure their biological random samples in an efficient manner to draw generalisable conclusions. We give advice for experimental situations with differing group sizes and show the impact of different designs on the variance and degrees of freedom of the test statistics. The designs proposed in this paper can be evaluated using SAS PROC MIXED or S+/R lme.     

Biological detection of low radiation doses by combining results of two microarray analysis methods

The accurate determination of the biological effects of low doses of pollutants is a major public health challenge. DNA microarrays are a powerful tool for investigating small intracellular changes. However, the inherent low reliability of this technique, the small number of replicates and the lack of suitable statistical methods for the analysis of such a large number of attributes (genes) impair accurate data interpretation. To overcome this problem, we combined results of two independent analysis methods (ANOVA and RELIEF). We applied this analysis protocol to compare gene expression patterns in Saccharomyces cerevisiae growing in the absence and continuous presence of varying low doses of radiation. Global distribution analysis highlights the importance of mitochondrial membrane functions in the response. We demonstrate that microarrays detect cellular changes induced by irradiation at doses that are 1000-fold lower than the minimal dose associated with mutagenic effects.     

Optimal allocation of replicates for measurement evaluation studies

Optimal experimental design is important for the efficient use of modern highthroughput technologies such as microarrays and proteomics. Multiple factors including the reliability of measurement system, which itself must be estimated from prior experimental work, could influence design decisions. In this study, we describe how the optimal number of replicate measures （technical replicates） for each biological sample （biological replicate） can be determined. Different allocations of biological and technical replicates were evaluated by minimizing the variance of the ratio of technical variance （measurement error） to the total variance （sum of sampling error and measurement error）. We demonstrate that if the number of biological replicates and the number of technical replicates per biological sample are variable, while the total number of available measures is fixed, then the optimal allocation of replicates for measurement evaluation experiments requires two technical replicates for each biological replicate. Therefore, it is recommended to use two technical replicates for each biological replicate if the goal is to evaluate the reproducibility of measurements.     

M<Superscript>3</Superscript>G: Maximum Margin Microarray Gridding

Background  

Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.     

In silico analysis of DND1 and its co-expressed genes in human cancers

Dead-End (DND1) is an RNA-binding protein involved in translational regulation. Defects in DND1 gene causes germ cell tumors and sterility in rodents. Experimental studies with human somatic cancer cells indicate that DND1 has anti-proliferative and pro-apoptotic function in some while oncogenic function in other cells. We examined The Cancer Genome Atlas data for gene alterations and gene expression changes in DND1 in a variety of human cancers. We found that DND1 is amplified, deleted or mutated in multiple human cancers. In different cancers, DND1 alteration correlates with increased diagnosis age of patients, shift in tumor spectrum or change of tumor sites and in some cases is significantly associated with worse survival for cancer patients. For 15 cancers, we retrieved expression data of thousands of genes that co-expressed with DND1. We found that these cancers contain different percentage of genes that are positively or negatively co-expressed with DND1. Ingenuity Pathway Analysis was performed to explore the biological implications of these genes. More than 10 canonical pathways were identified and each cancer type exhibits unique pathway profiles. Comparison analysis across all 15 cancer types showed that some cancers exhibit strikingly similar profiles of DND1-correlated signaling pathway activation or suppression. Our data reinforce the notion that the biological role of DND1 is cell-type specific and suggest that DND1 may play opposing role by exerting anti-proliferative effects in some cancer cells while being pro-proliferative in others. Our study provides valuable insights to direct experimental investigations of DND1 function in somatic cancers.     

Transformation and implication of the K-fgf proto-oncogene in NIH-3T3 cells,and induction of metastatic potential

A plasmid containing the K-fgf proto-oncogene linked to the dihydrofolate reductase gene has been constructed, and used in transfection experiments to investigate the effects of K-fgf expression on the tumorigenic and metastatic properties of NIH-3T3 fibroblasts. Analysis of cells transfected with K-fgf revealed that expression of the K-fgf proto-oncogene can, in a single step, induce both tumorigenic and metastatic characteristics, as determined in soft agar cloning experiments, and in tumorigenicity and experimental lung metastasis assays with BALB/c nu/nu mice. Selection for resistance to increasing concentrations of methotrexate lead to the isolation of a series of cell lines containing amplifications of both the dihydrofolate reductase gene and the linked K-fgf gene, which synthesized elevated levels of growth factor message and protein. The most highly resistant and gene amplified cell lines exhibited lower than expected levels of K-fgf mRNA, and also appeared to have down-regulated cell surface growth factor receptors. Further support for the concept that altered K-fgf expression can induce fully malignant and metastatic cells was obtained in experimental metastasis assays, where K-fgf transfected and gene amplified cell lines were highly aggressive.     

Genetic heterogeneity of stably transfected cell lines revealed by expression profiling with oligonucleotide microarrays

Large-scale gene expression measurements with oligonucleotide microarrays have contributed tremendously to biological research. However, to distinguish between relevant expression changes and falsely identified positives, the source and magnitude of errors must be understood. Here, we report a source of biological variability in microarray experiments with stably transfected cell lines. Mouse embryonic fibroblast (MEF/3T3) and rat schwannoma (RT4) cell lines were generated to provide regulatable schwannomin expression. The expression levels of 29 samples from five different mouse embryonic fibroblast clonal cell lines and 18 samples from 3 RT4 cell lines were monitored with oligonucleotide microarrays. Using hierarchical clustering, we determined that the changes in gene expression induced by schwannomin overexpression were subtle when compared with those detected as a consequence of clonal selection during generation of the cell lines. The hierarchical clustering implies that significant alterations of gene expression were introduced during the transfection and selection processes. A total of 28 genes were identified by Kruskal-Wallis rank test that showed significant variation between clonal lines. Most of them were related to cytoskeletal function and signaling pathways. Based on these analyses, we recommend that replications of experiments with several selected cell lines are necessary to assess biological effects of induced gene expression.     

新基因BRD7对鼻咽癌蛋白质表达谱影响的初步研究

BRD7基因是与鼻咽癌相关的候选抑瘤基因.为了进一步研究该基因的作用机制,将pcDNA3.1(+)/BRD7的表达质粒经脂质体导入HNE1细胞,RNA斑点杂交筛选出阳性克隆,通过双向电泳分离过表达BRD7的HNE1细胞内蛋白质,筛选出差异表达的蛋白质点,并进行质谱分析鉴定.鉴定出10种表达上调的蛋白质点,包括精氨(基)琥珀酸裂解酶,TSA(thio-specific antioxdant), Proteaseome activator28 beta subunit(PA28),金属蛋白酶抑制因子-2前体等,这些蛋白质涉及细胞生长,细胞代谢等很多相关事件.这些结果说明BRD7基因可能通过多种途径影响鼻咽癌的发生发展.     

Data extraction from composite oligonucleotide microarrays

Microarray or DNA chip technology is revolutionizing biology by empowering researchers in the collection of broad-scope gene information. It is well known that microarray-based measurements exhibit a substantial amount of variability due to a number of possible sources, ranging from hybridization conditions to image capture and analysis. In order to make reliable inferences and carry out quantitative analysis with microarray data, it is generally advisable to have more than one measurement of each gene. The availability of both between-array and within-array replicate measurements is essential for this purpose. Although statistical considerations call for increasing the number of replicates of both types, the latter is particularly challenging in practice due to a number of limiting factors, especially for in-house spotting facilities. We propose a novel approach to design so-called composite microarrays, which allow more replicates to be obtained without increasing the number of printed spots.