SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

Isoniazid Preventive Therapy Added to ART to Prevent TB: An Individual Participant Data Meta-Analysis

Background: Isoniazid preventive therapy prevents active tuberculosis in people with HIV, but previous studies have found no evidence of benefit in people with HIV who had a negative tuberculin skin test, and a non-significant effect on mortality. We aimed to estimate the effect of isoniazid preventive therapy given with antiretroviral therapy (ART) for the prevention of tuberculosis and death among people with HIV across population subgroups. Methods: We searched PubMed, Embase, the Cochrane database, and conference abstracts from database inception to Jan 15, 2019, to identify potentially eligible randomised trials. Eligible studies were trials that enrolled HIV-positive adults (age ≥15 years) taking ART who were randomly assigned to either daily isoniazid preventive therapy plus ART or ART alone and followed up longitudinally for outcomes of incident tuberculosis and mortality. We approached all authors of included trials and requested individual participant data: coprimary outcomes were relative risk of incident tuberculosis and all-cause mortality. We did a single-stage meta-analysis of individual participant data using stratified Cox-proportional hazards models. We did prespecified subgroup analyses by sex, CD4 cell count, and evidence of immune sensitisation to tuberculosis (indicated by tuberculin skin test or interferon-γ release assays [IGRAs]). We also assessed the relative risk of liver injury in an additional prespecified analysis. This study is registered with PROSPERO, CRD42019121400. Findings: Of 838 records, we included three trials with data for 2611 participants and 8584·8 person-years of follow-up for the outcome of incident tuberculosis, and a subset of 2362 participants with 8631·6 person-years of follow-up for the coprimary outcome of all-cause mortality. Risk for tuberculosis was lower in participants given isoniazid preventive therapy and ART than participants given ART alone (hazard ratio [HR] 0·68, 95% CI 0·49–0·95, p=0·02). Risk of all-cause mortality was lower in participants given isoniazid preventive therapy and ART than participants given ART alone, but this difference was non-significant (HR 0·69, 95% CI 0·43–1·10, p=0·12). Participants with baseline CD4 counts of less than 500 cells per μL had increased risk of tuberculosis, but there was no significant difference in the benefit of isoniazid preventive therapy with ART by sex, baseline CD4 count, or results of tuberculin skin test or IGRAs. 65 (2·5%) of 2611 participants had raised alanine aminotransferase, but data were insufficient to calculate an HR. Interpretation: Isoniazid preventive therapy with ART prevents tuberculosis across demographic and HIV-specific and tuberculosis-specific subgroups, which supports efforts to further increase use of isoniazid preventive therapy with ART broadly among people living with HIV. Funding: National Institutes of Health and National Institute of Allergy and Infectious Diseases

Optimality Driven Nearest Centroid Classification from Genomic Data

Nearest-centroid classifiers have recently been successfully employed in high-dimensional applications, such as in genomics. A necessary step when building a classifier for high-dimensional data is feature selection. Feature selection is frequently carried out by computing univariate scores for each feature individually, without consideration for how a subset of features performs as a whole. We introduce a new feature selection approach for high-dimensional nearest centroid classifiers that instead is based on the theoretically optimal choice of a given number of features, which we determine directly here. This allows us to develop a new greedy algorithm to estimate this optimal nearest-centroid classifier with a given number of features. In addition, whereas the centroids are usually formed from maximum likelihood estimates, we investigate the applicability of high-dimensional shrinkage estimates of centroids. We apply the proposed method to clinical classification based on gene-expression microarrays, demonstrating that the proposed method can outperform existing nearest centroid classifiers

Zinc intake, status and indices of cognitive function in adults and children: a systematic review and meta-analysis

In developing countries, deficiencies of micronutrients are thought to have a major impact on child development; however, a consensus on the specific relationship between dietary zinc intake and cognitive function remains elusive. The aim of this systematic review was to examine the relationship between zinc intake, status and indices of cognitive function in children and adults. A systematic literature search was conducted using EMBASE, MEDLINE and Cochrane Library databases from inception to March 2014. Included studies were those that supplied zinc as supplements or measured dietary zinc intake. A meta-analysis of the extracted data was performed where sufficient data were available. Of all of the potentially relevant papers, 18 studies met the inclusion criteria, 12 of which were randomised controlled trials (RCTs; 11 in children and 1 in adults) and 6 were observational studies (2 in children and 4 in adults). Nine of the 18 studies reported a positive association between zinc intake or status with one or more measure of cognitive function. Meta-analysis of data from the adult’s studies was not possible because of limited number of studies. A meta-analysis of data from the six RCTs conducted in children revealed that there was no significant overall effect of zinc intake on any indices of cognitive function: intelligence, standard mean difference of <0.001 (95% confidence interval (CI) –0.12, 0.13) P=0.95; executive function, standard mean difference of 0.08 (95% CI, –0.06, 022) P=0.26; and motor skills standard mean difference of 0.11 (95% CI –0.17, 0.39) P=0.43. Heterogeneity in the study designs was a major limitation, hence only a small number (n=6) of studies could be included in the meta-analyses. Meta-analysis failed to show a significant effect of zinc supplementation on cognitive functioning in children though, taken as a whole, there were some small indicators of improvement on aspects of executive function and motor development following supplementation but high-quality RCTs are necessary to investigate this further

Phenotype Prediction Using Regularized Regression on Genetic Data in the DREAM5 Systems Genetics B Challenge

A major goal of large-scale genomics projects is to enable the use of data from high-throughput experimental methods to predict complex phenotypes such as disease susceptibility. The DREAM5 Systems Genetics B Challenge solicited algorithms to predict soybean plant resistance to the pathogen Phytophthora sojae from training sets including phenotype, genotype, and gene expression data. The challenge test set was divided into three subcategories, one requiring prediction based on only genotype data, another on only gene expression data, and the third on both genotype and gene expression data. Here we present our approach, primarily using regularized regression, which received the best-performer award for subchallenge B2 (gene expression only). We found that despite the availability of 941 genotype markers and 28,395 gene expression features, optimal models determined by cross-validation experiments typically used fewer than ten predictors, underscoring the importance of strong regularization in noisy datasets with far more features than samples. We also present substantial analysis of the training and test setup of the challenge, identifying high variance in performance on the gold standard test sets.National Science Foundation (U.S.). Graduate Research Fellowship ProgramNational Defense Science and Engineering Graduate Fellowshi

On the Polynomial Measurement Error Model

This paper discusses point estimation of the coefficients of polynomial measurement error (errors-in-variables) models. This includes functional and structural models. The connection between these models and total least squares (TLS) is also examined. A compendium of existing as well as new results is presented

A Schur complement approach to preconditioning sparse linear least-squares problems with some dense rows

The effectiveness of sparse matrix techniques for directly solving large-scale linear least-squares problems is severely limited if the system matrix A has one or more nearly dense rows. In this paper, we partition the rows of A into sparse rows and dense rows (A s and A d ) and apply the Schur complement approach. A potential difficulty is that the reduced normal matrix AsTA s is often rank-deficient, even if A is of full rank. To overcome this, we propose explicitly removing null columns of A s and then employing a regularization parameter and using the resulting Cholesky factors as a preconditioner for an iterative solver applied to the symmetric indefinite reduced augmented system. We consider complete factorizations as well as incomplete Cholesky factorizations of the shifted reduced normal matrix. Numerical experiments are performed on a range of large least-squares problems arising from practical applications. These demonstrate the effectiveness of the proposed approach when combined with either a sparse parallel direct solver or a robust incomplete Cholesky factorization algorithm

Hippocampus specific iron deficiency alters competition and cooperation between developing memory systems

Iron deficiency (ID) is the most common gestational micronutrient deficiency in the world, targets the fetal hippocampus and striatum and results in long-term behavioral abnormalities. These structures primarily mediate spatial and procedural memory, respectively, in the rodent but have interconnections that result in competition or cooperation during cognitive tasks. We determined whether ID-induced impairment of one alters the function of the other by genetically inducing a 40% reduction of hippocampus iron content in late fetal life in mice and measuring dorsal striatal gene expression and metabolism and the behavioral balance between the two memory systems in adulthood. Slc11a2hipp/hipp mice had similar striatum iron content, but 18% lower glucose and 44% lower lactate levels, a 30% higher phosphocreatine:creatine ratio, and reduced iron transporter gene expression compared to wild type (WT) littermates, implying reduced striatal metabolic function. Slc11a2hipp/hipp mice had longer mean escape times on a cued task paradigm implying impaired procedural memory. Nevertheless, when hippocampal and striatal memory systems were placed in competition using a Morris Water Maze task that alternates spatial navigation and visual cued responses during training, and forces a choice between hippocampal and striatal strategies during probe trials, Slc11a2hipp/hipp mice used the hippocampus-dependent response less often (25%) and the visual cued response more often (75%) compared to WT littermates that used both strategies approximately equally. Hippocampal ID not only reduces spatial recognition memory performance but also affects systems that support procedural memory, suggesting an altered balance between memory systems

Identifying Tightly Regulated and Variably Expressed Networks by Differential Rank Conservation (DIRAC)

A powerful way to separate signal from noise in biology is to convert the molecular data from individual genes or proteins into an analysis of comparative biological network behaviors. One of the limitations of previous network analyses is that they do not take into account the combinatorial nature of gene interactions within the network. We report here a new technique, Differential Rank Conservation (DIRAC), which permits one to assess these combinatorial interactions to quantify various biological pathways or networks in a comparative sense, and to determine how they change in different individuals experiencing the same disease process. This approach is based on the relative expression values of participating genes—i.e., the ordering of expression within network profiles. DIRAC provides quantitative measures of how network rankings differ either among networks for a selected phenotype or among phenotypes for a selected network. We examined disease phenotypes including cancer subtypes and neurological disorders and identified networks that are tightly regulated, as defined by high conservation of transcript ordering. Interestingly, we observed a strong trend to looser network regulation in more malignant phenotypes and later stages of disease. At a sample level, DIRAC can detect a change in ranking between phenotypes for any selected network. Variably expressed networks represent statistically robust differences between disease states and serve as signatures for accurate molecular classification, validating the information about expression patterns captured by DIRAC. Importantly, DIRAC can be applied not only to transcriptomic data, but to any ordinal data type