A powerful method for detecting differentially expressed genes from GeneChip arrays that does not require replicates

BACKGROUND: Studies of differential expression that use Affymetrix GeneChip arrays are often carried out with a limited number of replicates. Reasons for this include financial considerations and limits on the available amount of RNA for sample preparation. In addition, failed hybridizations are not uncommon leading to a further reduction in the number of replicates available for analysis. Most existing methods for studying differential expression rely on the availability of replicates and the demand for alternative methods that require few or no replicates is high. RESULTS: We describe a statistical procedure for performing differential expression analysis without replicates. The procedure relies on a Bayesian integrated approach (BGX) to the analysis of Affymetrix GeneChips. The BGX method estimates a posterior distribution of expression for each gene and condition, from a simultaneous consideration of the available probe intensities representing the gene in a condition. Importantly, posterior distributions of expression are obtained regardless of the number of replicates available. We exploit these posterior distributions to create ranked gene lists that take into account the estimated expression difference as well as its associated uncertainty. We estimate the proportion of non-differentially expressed genes empirically, allowing an informed choice of cut-off for the ranked gene list, adapting an approach proposed by Efron. We assess the performance of the method, and compare it to those of other methods, on publicly available spike-in data sets, as well as in a proper biological setting. CONCLUSION: The method presented is a powerful tool for extracting information on differential expression from GeneChip expression studies with limited or no replicates

Poor glycaemic control is associated with reduced exercise performance and oxygen economy during cardio-pulmonary exercise testing in people with type 1 diabetes

BackgroundTo explore the impact of glycaemic control (HbA1c) on functional capacity during cardio-pulmonary exercise testing in people with type 1 diabetes.MethodsSixty-four individuals with type 1 diabetes (age: 34 ± 8 years; 13 females, HbA1c: 7.8 ± 1% (62 ± 13 mmol/mol), duration of diabetes: 17 ± 9 years) performed a cardio-pulmonary cycle ergometer exercise test until volitional exhaustion. Stepwise linear regression was used to explore relationships between HbA1c and cardio-respiratory data with p ≤ 0.05. Furthermore, participants were divided into quartiles based on HbA1c levels and cardio-respiratory data were analysed by one-way ANOVA. Multiple regression analysis was performed to explore the relationships between changes in time to exhaustion and cardio-respiratory data. Data were adjusted for confounder.ResultsHbA1c was related to time to exhaustion and oxygen consumption at the power output elicited at the sub-maximal threshold of the heart rate turn point (r = 0.47, R2 = 0.22, p = 0.03). Significant differences were found at time to exhaustion between QI vs. QIV and at oxygen consumption at the power output elicited at the heart rate turn point between QI vs. QII and QI vs. QIV (p < 0.05). Changes in oxygen uptake, power output and in oxygen consumption at the power output elicited at the heart rate turn point and at maximum power output explained 55% of the variance in time to exhaustion (r = 0.74, R2 = 0.55, p < 0.01).ConclusionsPoor glycaemic control is related to less economical use of oxygen at sub-maximal work rates and an earlier time to exhaustion during cardio-pulmonary exercise testing. However, exercise training could have the same potential to counteract the influence of poor glycaemic control on functional capacity

A comprehensive re-analysis of the Golden Spike data: Towards a benchmark for differential expression methods

<p>Abstract</p> <p>Background</p> <p>The Golden Spike data set has been used to validate a number of methods for summarizing Affymetrix data sets, sometimes with seemingly contradictory results. Much less use has been made of this data set to evaluate differential expression methods. It has been suggested that this data set should not be used for method comparison due to a number of inherent flaws.</p> <p>Results</p> <p>We have used this data set in a comparison of methods which is far more extensive than any previous study. We outline six stages in the analysis pipeline where decisions need to be made, and show how the results of these decisions can lead to the apparently contradictory results previously found. We also show that, while flawed, this data set is still a useful tool for method comparison, particularly for identifying combinations of summarization and differential expression methods that are unlikely to perform well on real data sets. We describe a new benchmark, AffyDEComp, that can be used for such a comparison.</p> <p>Conclusion</p> <p>We conclude with recommendations for preferred Affymetrix analysis tools, and for the development of future spike-in data sets.</p

A full Bayesian hierarchical mixture model for the variance of gene differential expression

<p>Abstract</p> <p>Background</p> <p>In many laboratory-based high throughput microarray experiments, there are very few replicates of gene expression levels. Thus, estimates of gene variances are inaccurate. Visual inspection of graphical summaries of these data usually reveals that heteroscedasticity is present, and the standard approach to address this is to take a log₂transformation. In such circumstances, it is then common to assume that gene variability is constant when an analysis of these data is undertaken. However, this is perhaps too stringent an assumption. More careful inspection reveals that the simple log₂transformation does not remove the problem of heteroscedasticity. An alternative strategy is to assume independent gene-specific variances; although again this is problematic as variance estimates based on few replications are highly unstable. More meaningful and reliable comparisons of gene expression might be achieved, for different conditions or different tissue samples, where the test statistics are based on accurate estimates of gene variability; a crucial step in the identification of differentially expressed genes.</p> <p>Results</p> <p>We propose a Bayesian mixture model, which classifies genes according to similarity in their variance. The result is that genes in the same latent class share the similar variance, estimated from a larger number of replicates than purely those per gene, i.e. the total of all replicates of all genes in the same latent class. An example dataset, consisting of 9216 genes with four replicates per condition, resulted in four latent classes based on their similarity of the variance.</p> <p>Conclusion</p> <p>The mixture variance model provides a realistic and flexible estimate for the variance of gene expression data under limited replicates. We believe that in using the latent class variances, estimated from a larger number of genes in each derived latent group, the <it>p</it>-values obtained are more robust than either using a constant gene or gene-specific variance estimate.</p

LeMMINGs - II. The e-MERLIN legacy survey of nearby galaxies. The deepest radio view of the Palomar sample on parsec scale

We present the second data release of high-resolution (≤0.2 arcsec) 1.5-GHz radio images of 177 nearby galaxies from the Palomar sample, observed with the e-MERLIN array, as part of the Legacy e-MERLIN Multi-band Imaging of Nearby Galaxies Sample (LeMMINGs) survey. Together with the 103 targets of the first LeMMINGs data release, this represents a complete sample of 280 local active (LINER and Seyfert) and inactive galaxies (H II galaxies and absorption line galaxies, ALG). This large program is the deepest radio survey of the local Universe, ≳1017.6 W Hz−1, regardless of the host and nuclear type: we detect radio emission ≳0.25 mJy beam−1 for 125/280 galaxies (44.6 per cent) with sizes of typically ≲100 pc. Of those 125, 106 targets show a core which coincides within 1.2 arcsec with the optical nucleus. Although we observed mostly cores, around one third of the detected galaxies features jetted morphologies. The detected radio core luminosities of the sample range between ∼1034 and 1040 erg s−1. LINERs and Seyferts are the most luminous sources, whereas H II galaxies are the least. LINERs show FR I-like core-brightened radio structures while Seyferts reveal the highest fraction of symmetric morphologies. The majority of H II galaxies have single radio core or complex extended structures, which probably conceal a nuclear starburst and/or a weak active nucleus (seven of them show clear jets). ALGs, which are typically found in evolved ellipticals, although the least numerous, exhibit on average the most luminous radio structures, similar to LINERs

Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity

<p>Abstract</p> <p>Background</p> <p>To identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify DEGs with a higher level of sensitivity and specificity. However, in addition to these recommendations, researchers also want to know which combinations enhance reproducibility.</p> <p>Results</p> <p>We compared eight conventional methods for ranking genes: weighted average difference (WAD), average difference (AD), fold change (FC), rank products (RP), moderated <it>t </it>statistic (modT), significance analysis of microarrays (samT), shrinkage <it>t </it>statistic (shrinkT), and intensity-based moderated <it>t </it>statistic (ibmT) with six preprocessing algorithms (PLIER, VSN, FARMS, multi-mgMOS (mmgMOS), MBEI, and GCRMA). A total of 36 real experimental datasets was evaluated on the basis of the area under the receiver operating characteristic curve (AUC) as a measure for both sensitivity and specificity. We found that the RP method performed well for VSN-, FARMS-, MBEI-, and GCRMA-preprocessed data, and the WAD method performed well for mmgMOS-preprocessed data. Our analysis of the MicroArray Quality Control (MAQC) project's datasets showed that the FC-based gene ranking methods (WAD, AD, FC, and RP) had a higher level of reproducibility: The percentages of overlapping genes (POGs) across different sites for the FC-based methods were higher overall than those for the <it>t</it>-statistic-based methods (modT, samT, shrinkT, and ibmT). In particular, POG values for WAD were the highest overall among the FC-based methods irrespective of the choice of preprocessing algorithm.</p> <p>Conclusion</p> <p>Our results demonstrate that to increase sensitivity, specificity, and reproducibility in microarray analyses, we need to select suitable combinations of preprocessing algorithms and gene ranking methods. We recommend the use of FC-based methods, in particular RP or WAD.</p

Serological Profiling of a Candida albicans Protein Microarray Reveals Permanent Host-Pathogen Interplay and Stage-Specific Responses during Candidemia

Candida albicans in the immunocompetent host is a benign member of the human microbiota. Though, when host physiology is disrupted, this commensal-host interaction can degenerate and lead to an opportunistic infection. Relatively little is known regarding the dynamics of C. albicans colonization and pathogenesis. We developed a C. albicans cell surface protein microarray to profile the immunoglobulin G response during commensal colonization and candidemia. The antibody response from the sera of patients with candidemia and our negative control groups indicate that the immunocompetent host exists in permanent host-pathogen interplay with commensal C. albicans. This report also identifies cell surface antigens that are specific to different phases (i.e. acute, early and mid convalescence) of candidemia. We identified a set of thirteen cell surface antigens capable of distinguishing acute candidemia from healthy individuals and uninfected hospital patients with commensal colonization. Interestingly, a large proportion of these cell surface antigens are involved in either oxidative stress or drug resistance. In addition, we identified 33 antigenic proteins that are enriched in convalescent sera of the candidemia patients. Intriguingly, we found within this subset an increase in antigens associated with heme-associated iron acquisition. These findings have important implications for the mechanisms of C. albicans colonization as well as the development of systemic infection

The multifunctional roles of vegetated strips around and within agricultural fields : A systematic map protocol.

Background: Agriculture and agricultural intensification can have significant negative impacts on the environment, including nutrient and pesticide leaching, spreading of pathogens, soil erosion and reduction of ecosystem services provided by terrestrial and aquatic biodiversity. The establishment and management of vegetated strips adjacent to farmed fields (including various field margins, buffer strips and hedgerows) are key mitigation measures for these negative environmental impacts and environmental managers and other stakeholders must often make decisions about how best to design and implement vegetated strips for a variety of different outcomes. However, it may be difficult to obtain relevant, accurate and summarised information on the effects of implementation and management of vegetated strips, even though a vast body of evidence exists on multipurpose vegetated strip interventions within and around fields. To improve the situation, we describe a method for assembling a database of relevant research relating to vegetated strips undertaken in boreo-temperate farming systems (arable, pasture, horticulture, orchards and viticulture). Methods: We will search 13 bibliographic databases, 1 search engine and 37 websites for stakeholder organisations using a predefined and tested search string that focuses on a comprehensive list of vegetated strip synonyms. Non-English language searches in Danish, Finnish, German, Spanish, and Swedish will also be undertaken using a web-based search engine. We will screen search results at title, abstract and full text levels, recording the number of studies deemed non-relevant (with reasons at full text). A systematic map database that displays the meta-data (i.e. descriptive summary information about settings and methods) of relevant studies will be produced following full text assessment. The systematic map database will be displayed as a web-based geographical information system (GIS). The nature and extent of the evidence base will be discussed

Genome-Wide Expression Analysis Identifies a Modulator of Ionizing Radiation-Induced p53-Independent Apoptosis in Drosophila melanogaster

Tumor suppressor p53 plays a key role in DNA damage responses in metazoa, yet more than half of human tumors show p53 deficiencies. Therefore, understanding how therapeutic genotoxins such as ionizing radiation (IR) can elicit DNA damage responses in a p53-independent manner is of clinical importance. Drosophila has been a good model to study the effects of IR because DNA damage responses as well as underlying genes are conserved in this model, and because streamlined gene families make loss-of-function analyses feasible. Indeed, Drosophila is the only genetically tractable model for IR-induced, p53-independent apoptosis and for tissue regeneration and homeostasis after radiation damage. While these phenomenon occur only in the larvae, all genome-wide gene expression analyses after irradiation to date have been in embryos. We report here the first analysis of IR-induced, genome-wide gene expression changes in wild type and p53 mutant Drosophila larvae. Key data from microarrays were confirmed by quantitative RT-PCR. The results solidify the central role of p53 in IR-induced transcriptome changes, but also show that nearly all changes are made of both p53-dependent and p53-independent components. p53 is found to be necessary not just for the induction of but also for the repression of transcript levels for many genes in response to IR. Furthermore, Functional analysis of one of the top-changing genes, EF1a-100E, implicates it in repression of IR-induced p53-independent apoptosis. These and other results support the emerging notion that there is not a single dominant mechanism but that both positive and negative inputs collaborate to induce p53-independent apoptosis in response to IR in Drosophila larvae