Indirect two-sided relative ranking: a robust similarity measure for gene expression data

<p>Abstract</p> <p>Background</p> <p>There is a large amount of gene expression data that exists in the public domain. This data has been generated under a variety of experimental conditions. Unfortunately, these experimental variations have generally prevented researchers from accurately comparing and combining this wealth of data, which still hides many novel insights.</p> <p>Results</p> <p>In this paper we present a new method, which we refer to as indirect two-sided relative ranking, for comparing gene expression profiles that is robust to variations in experimental conditions. This method extends the current best approach, which is based on comparing the correlations of the up and down regulated genes, by introducing a comparison based on the correlations in rankings across the entire database. Because our method is robust to experimental variations, it allows a greater variety of gene expression data to be combined, which, as we show, leads to richer scientific discoveries.</p> <p>Conclusions</p> <p>We demonstrate the benefit of our proposed indirect method on several datasets. We first evaluate the ability of the indirect method to retrieve compounds with similar therapeutic effects across known experimental barriers, namely vehicle and batch effects, on two independent datasets (one private and one public). We show that our indirect method is able to significantly improve upon the previous state-of-the-art method with a substantial improvement in recall at rank 10 of 97.03% and 49.44%, on each dataset, respectively. Next, we demonstrate that our indirect method results in improved accuracy for classification in several additional datasets. These datasets demonstrate the use of our indirect method for classifying cancer subtypes, predicting drug sensitivity/resistance, and classifying (related) cell types. Even in the absence of a known (i.e., labeled) experimental barrier, the improvement of the indirect method in each of these datasets is statistically significant.</p

Stepwise classification of cancer samples using clinical and molecular data

<p>Abstract</p> <p>Background</p> <p>Combining clinical and molecular data types may potentially improve prediction accuracy of a classifier. However, currently there is a shortage of effective and efficient statistical and bioinformatic tools for true integrative data analysis. Existing integrative classifiers have two main disadvantages: First, coarse combination may lead to subtle contributions of one data type to be overshadowed by more obvious contributions of the other. Second, the need to measure both data types for all patients may be both unpractical and (cost) inefficient.</p> <p>Results</p> <p>We introduce a novel classification method, a stepwise classifier, which takes advantage of the distinct classification power of clinical data and high-dimensional molecular data. We apply classification algorithms to two data types independently, starting with the traditional clinical risk factors. We only turn to relatively expensive molecular data when the uncertainty of prediction result from clinical data exceeds a predefined limit. Experimental results show that our approach is adaptive: the proportion of samples that needs to be re-classified using molecular data depends on how much we expect the predictive accuracy to increase when re-classifying those samples.</p> <p>Conclusions</p> <p>Our method renders a more cost-efficient classifier that is at least as good, and sometimes better, than one based on clinical or molecular data alone. Hence our approach is not just a classifier that minimizes a particular loss function. Instead, it aims to be cost-efficient by avoiding molecular tests for a potentially large subgroup of individuals; moreover, for these individuals a test result would be quickly available, which may lead to reduced waiting times (for diagnosis) and hence lower the patients distress. Stepwise classification is implemented in R-package <it>stepwiseCM </it>and available at the Bioconductor website.</p

Renal histomorphology in dogs with pyometra and control dogs, and long term clinical outcome with respect to signs of kidney disease

<p>Abstract</p> <p>Background</p> <p>Age-related changes in renal histomorphology are described, while the presence of glomerulonephritis in dogs with pyometra is controversial in current literature.</p> <p>Methods</p> <p>Dogs with pyometra were examined retrospectively for evidence of secondary renal damage and persisting renal disease through two retrospective studies. In Study 1, light microscopic lesions of renal tissue were graded and compared in nineteen dogs with pyometra and thirteen age-matched control bitches. In Study 2, forty-one owners of dogs with pyometra were interviewed approximately 8 years after surgery for evidence ofclinical signs of renal failure in order to document causes of death/euthanasia.</p> <p>Results</p> <p>Interstitial inflammation and tubular atrophy were more pronounced in dogs with pyometra than in the control animals. Glomerular lesions classified as glomerular sclerosis were present in both groups. No unequivocal light microscopic features of glomerulonephritis were observed in bitches in any of the groups.</p> <p>Two bitches severely proteinuric at the time of surgery had developed end stage renal disease within 3 years. In five of the bitches polyuria persisted after surgery. Most bitches did not show signs of kidney disease at the time of death/euthanasia.</p> <p>Conclusion</p> <p>Tubulointerstitial inflammation was observed, but glomerular damage beyond age-related changes could not be demonstrated by light microscopy in the dogs with pyometra. However, severe proteinuria after surgery may predispose to development of renal failure.</p

A randomised controlled trial evaluating family mediated exercise (FAME) therapy following stroke

<p>Abstract</p> <p>Background</p> <p>Stroke is a leading cause of disability among adults worldwide. Evidence suggests that increased duration of exercise therapy following stroke has a positive impact on functional outcome following stroke. The main objective of this randomised controlled trial is to evaluate the impact of additional family assisted exercise therapy in people with acute stroke.</p> <p>Methods/Design</p> <p>A prospective multi-centre single blind randomised controlled trial will be conducted. Forty patients with acute stroke will be randomised into either an experimental or control group. The experimental group will receive routine therapy and additional lower limb exercise therapy in the form of family assisted exercises. The control group will receive routine therapy with no additional formal input from their family members. Participants will be assessed at baseline, post intervention and followed up at three months using a series of standardised outcome measures. A secondary aim of the project is to evaluate the impact of the family mediated exercise programme on the person with stroke and the individual(s) assisting in the delivery of exercises using a qualitative methodology. The study has gained ethical approval from the Research Ethics Committees of each of the clinical sites involved in the study.</p> <p>Discussion</p> <p>This study will evaluate a structured programme of exercises that can be delivered to people with stroke by their 'family members/friends'. Given that the progressive increase in the population of older people is likely to lead to an increased prevalence of stroke in the future, it is important to reduce the burden of this illness on the individual, the family and society. Family mediated exercises can maximise the carry over outside formal physiotherapy sessions, giving patients the opportunity for informal practice.</p> <p>Trial Registration</p> <p>The protocol for this study is registered with the US NIH Clinical trials registry (NCT00666744)</p

Chromosomal Rearrangements Formed by rrn Recombination Do Not Improve Replichore Balance in Host-Specific Salmonella enterica Serovars

operons. One hypothesis explaining these rearrangements suggests that replichore imbalance introduced from horizontal transfer of pathogenicity islands and prophages drives chromosomal rearrangements in an attempt to improve balance.This hypothesis was directly tested by comparing the naturally-occurring chromosomal arrangement types to the theoretically possible arrangement types, and estimating their replichore balance using a calculator. In addition to previously characterized strains belonging to host-specific serovars, the arrangement types of 22 serovar Gallinarum strains was also determined. Only 48 out of 1,440 possible arrangement types were identified in 212 host-specific strains. While the replichores of most naturally-occurring arrangement types were well-balanced, most theoretical arrangement types had imbalanced replichores. Furthermore, the most common types of rearrangements did not change replichore balance.The results did not support the hypothesis that replichore imbalance causes these rearrangements, and suggest that the rearrangements could be explained by aspects of a host-specific lifestyle

Identification of ChIP-seq mapped targets of HP1β due to bombesin/GRP receptor activation

Epithelial cells lining the adult colon do not normally express gastrin-releasing peptide (GRP) or its receptor (GRPR). In contrast, GRP/GRPR can be aberrantly expressed in human colorectal cancer (CRC) including Caco-2 cells. We have previously shown that GRPR activation results in the up-regulation of HP1β, an epigenetic modifier of gene transcription. The aim of this study was to identify the genes whose expression is altered by HP1β subsequent to GRPR activation. We determined HP1β binding positions throughout the genome using chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq). After exposure to GRP, we identified 9,625 genomic positions occupied by HP1β. We performed gene microarray analysis on Caco-2 cells in the absence and presence of a GRPR specific antagonist as well as siRNA to HP1β. The expression of 97 genes was altered subsequent to GRPR antagonism, while the expression of 473 genes was altered by HP1β siRNA exposure. When these data were evaluated in concert with our ChIP-seq findings, 9 genes showed evidence of possible altered expression as a function of GRPR signaling via HP1β. Of these, genomic PCR of immunoprecipitated chromatin demonstrated that GRPR signaling affected the expression of IL1RAPL2, FAM13A, GBE1, PLK3, and SLCO1B3. These findings provide the first evidence by which GRPR aberrantly expressed in CRC might affect tumor progression

Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction

&lt;b&gt;Background&lt;/b&gt; The widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm that is based on relative expression ordering of gene pairs. However, its general robustness does not extend to some difficult datasets, such as those involving cancer outcome prediction, which may be due to the relatively simple voting scheme used by the classifier. We believe that the performance can be enhanced by separating its effective feature selection component and combining it with a powerful classifier such as the support vector machine (SVM). More generally the top scoring pairs generated by the k-TSP ranking algorithm can be used as a dimensionally reduced subspace for other machine learning classifiers.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Results&lt;/b&gt; We developed an approach integrating the k-TSP ranking algorithm (TSP) with other machine learning methods, allowing combination of the computationally efficient, multivariate feature ranking of k-TSP with multivariate classifiers such as SVM. We evaluated this hybrid scheme (k-TSP+SVM) in a range of simulated datasets with known data structures. As compared with other feature selection methods, such as a univariate method similar to Fisher's discriminant criterion (Fisher), or a recursive feature elimination embedded in SVM (RFE), TSP is increasingly more effective than the other two methods as the informative genes become progressively more correlated, which is demonstrated both in terms of the classification performance and the ability to recover true informative genes. We also applied this hybrid scheme to four cancer prognosis datasets, in which k-TSP+SVM outperforms k-TSP classifier in all datasets, and achieves either comparable or superior performance to that using SVM alone. In concurrence with what is observed in simulation, TSP appears to be a better feature selector than Fisher and RFE in some of the cancer datasets.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusions&lt;/b&gt; The k-TSP ranking algorithm can be used as a computationally efficient, multivariate filter method for feature selection in machine learning. SVM in combination with k-TSP ranking algorithm outperforms k-TSP and SVM alone in simulated datasets and in some cancer prognosis datasets. Simulation studies suggest that as a feature selector, it is better tuned to certain data characteristics, i.e. correlations among informative genes, which is potentially interesting as an alternative feature ranking method in pathway analysis

Medulloblastoma outcome is adversely associated with overexpression of EEF1D, RPL30, and RPS20 on the long arm of chromosome 8

BACKGROUND: Medulloblastoma is the most common malignant brain tumor of childhood. Improvements in clinical outcome require a better understanding of the genetic alterations to identify clinically significant biological factors and to stratify patients accordingly. In the present study, we applied cytogenetic characterization to guide the identification of biologically significant genes from gene expression microarray profiles of medulloblastoma. METHODS: We analyzed 71 primary medulloblastomas for chromosomal copy number aberrations (CNAs) using comparative genomic hybridization (CGH). Among 64 tumors that we previously analyzed by gene expression microarrays, 27 were included in our CGH series. We analyzed clinical outcome with respect to CNAs and microarray results. We filtered microarray data using specific CNAs to detect differentially expressed candidate genes associated with survival. RESULTS: The most frequent lesions detected in our series involved chromosome 17; loss of 16q, 10q, or 8p; and gain of 7q or 2p. Recurrent amplifications at 2p23-p24, 2q14, 7q34, and 12p13 were also observed. Gain of 8q is associated with worse overall survival (p = 0.0141), which is not entirely attributable to MYC amplification or overexpression. By applying CGH results to gene expression analysis of medulloblastoma, we identified three 8q-mapped genes that are associated with overall survival in the larger group of 64 patients (p < 0.05): eukaryotic translation elongation factor 1D (EEF1D), ribosomal protein L30 (RPL30), and ribosomal protein S20 (RPS20). CONCLUSION: The complementary use of CGH and expression profiles can facilitate the identification of clinically significant candidate genes involved in medulloblastoma growth. We demonstrate that gain of 8q and expression levels of three 8q-mapped candidate genes (EEF1D, RPL30, RPS20) are associated with adverse outcome in medulloblastoma

Prognostic factors related to sequelae in childhood bacterial meningitis: Data from a Greek meningitis registry

<p>Abstract</p> <p>Background</p> <p>Bacterial meningitis (BM) is a life-threatening disease, often related with serious complications and sequelae. Infants and children who survive bacterial meningitis often suffer neurological and other sequelae.</p> <p>Methods</p> <p>A total of 2,477 patients aged 1 month to 14 years old hospitalized in a Children's Hospital in Greece diagnosed with acute bacterial meningitis were collected through a Meningitis Registry, from 1974 to 2005. Clinical, laboratory and other parameters (sex, age, pathogen, duration of symptoms before and after admission) were evaluated through univariate and multivariate analysis with regard to sequelae. Analysis of acute complications were also studied but not included in the final model.</p> <p>Results</p> <p>The rate of acute complications (arthritis and/or subdural effusion) was estimated at 6.8% (152 out of 2,251 patients, 95%CI 5.8-7.9) while the rate of sequelae (severe hearing loss, ventriculitis, hydrocephalus or seizure disorder) among survivors was estimated at 3.3% (73 out of 2,207 patients, 95%CI 2.6-4.2). Risk factors on admission associated with sequelae included seizures, absence of hemorrhagic rash, low CSF glucose, high CSF protein and the etiology of meningitis. A combination of significant prognostic factors including presence of seizures, low CSF glucose, high CSF protein, positive blood culture and absence of petechiae on admission presented an absolute risk of sequelae of 41.7% (95%CI 15.2-72.3).</p> <p>Conclusions</p> <p>A combination of prognostic factors of sequelae in childhood BM may be of value in selecting patients for more intensive therapy and in identifying possible candidates for new treatment strategies.</p