Finding associations in a heterogeneous setting: statistical test for aberration enrichment.
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ABSTRACT:
SUBMITTER: Mezlini AM
PROVIDER: S-EPMC8066476 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundComplexity and noise in expression quantitative trait loci (eQTL) studies make it difficult to distinguish potential regulatory relationships among the many interactions. The predominant method of identifying eQTLs finds associations that are significant at a genome-wide level. The vast number of statistical tests carried out on these data make false negatives very likely. Corrections for multiple testing error render genome-wide eQTL techniques unable to detect modest regulatory effects. We propose an alternative method to identify eQTLs that builds on traditional approaches. In contrast to genome-wide techniques, our method determines the significance of an association between an expression trait and a locus with respect to the set of all associations to the expression trait. The use of this specific information facilitates identification of expression traits that have an expression profile that is characterized by a single exceptional association to a locus. Our approach identifies expression traits that have exceptional associations regardless of the genome-wide significance of those associations. This property facilitates the identification of possible false negatives for genome-wide significance. Further, our approach has the property of prioritizing expression traits that are affected by few strong associations. Expression traits identified by this method may warrant additional study because their expression level may be affected by targeting genes near a single locus.ResultsWe demonstrate our method by identifying eQTL hotspots in Plasmodium falciparum (malaria) and Saccharomyces cerevisiae (yeast). We demonstrate the prioritization of traits with few strong genetic effects through Gene Ontology (GO) analysis of Yeast. Our results are strongly consistent with results gathered using genome-wide methods and identify additional hotspots and eQTLs.ConclusionsNew eQTLs and hotspots found with this method may represent regions of the genome or biological processes that are controlled through few relatively strong genetic interactions. These points of interest warrant experimental investigation.
| S-EPMC2974753 | biostudies-literature
Project description:We present a new method, link-test, to select prostate cancer biomarkers from SELDI mass spectrometry and microarray data sets. Biomarkers selected by link-test are supported by data sets from both mRNA and protein levels, and therefore results in improved robustness. Link-test determines the level of significance of the association between a microarray marker and a specific mass spectrum marker by constructing background mass spectra distributions estimated by all human protein sequences in the SWISS-PROT database. The data set consist of both microarray and mass spectrometry data from prostate cancer patients and healthy controls. A list of statistically justified prostate cancer biomarkers is reported by link-test. Cross-validation results show high prediction accuracy using the identified biomarker panel. We also employ a text-mining approach with OMIM database to validate the cancer biomarkers. The study with link-test represents one of the first cross-platform studies of cancer biomarkers.
| S-EPMC1941704 | biostudies-literature
Project description:MOTIVATION:Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the 'rising activity, multilevel mixed effects, indicator emphasis' (RAMMIE) method and the improved quasi-Poisson regression-based method known as 'Farrington Flexible' both currently used at Public Health England, and the 'Early Aberration Reporting System' (EARS) method used at the US Centre for Disease Control and Prevention. We model the wide range of data structures encountered within the daily syndromic surveillance systems used by PHE. We undertake extensive simulations to identify which algorithms work best across different types of syndromes and different outbreak sizes. We evaluate RAMMIE for the first time since its introduction. Performance metrics were computed and compared in the presence of a range of simulated outbreak types that were added to baseline data. RESULTS:We conclude that amongst the algorithm variants that have a high specificity (i.e. >90%), Farrington Flexible has the highest sensitivity and specificity, whereas RAMMIE has the highest probability of outbreak detection and is the most timely, typically detecting outbreaks 2-3 days earlier. AVAILABILITY AND IMPLEMENTATION:R codes developed for this project are available through https://github.com/FelipeJColon/AlgorithmComparison. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC6736430 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2011-08-10 | GSE26736 | GEO
Project description:BackgroundDetecting impaired naming capacity contributes to the detection of mild (MildND) and major (MajorND) neurocognitive disorder due to Alzheimer's disease (AD). The Test for Finding Word retrieval deficits (WoFi) is a new, 50-item, auditory stimuli-based instrument.ObjectiveThe study aimed to adapt WoFi to the Greek language, to develop a short version of WoFi (WoFi-brief), to compare the item frequency and the utility of both instruments with the naming subtest of the widely used Addenbrooke's cognitive examination III (ACEIIINaming) in detecting MildND and MajorND due to AD.MethodsThis cross-sectional, validation study included 99 individuals without neurocognitive disorder, as well as 114 and 49 patients with MildND and MajorND due to AD, respectively. The analyses included categorical principal components analysis using Cramer's V, assessment of the frequency of test items based on corpora of television subtitles, comparison analyses, Kernel Fisher discriminant analysis models, proportional odds logistic regression (POLR) models and stratified repeated random subsampling used to recursive partitioning to training and validation set (70/30 ratio).ResultsWoFi and WoFi-brief, which consists of 16 items, have comparable item frequency and utility and outperform ACEIIINaming. According to the results of the discriminant analysis, the misclassification error was 30.9%, 33.6% and 42.4% for WoFi, WoFi-brief and ACEIIINaming, respectively. In the validation regression model including WoFi the mean misclassification error was 33%, while in those including WoFi-brief and ACEIIINaming it was 31% and 34%, respectively.ConclusionsWoFi and WoFi-brief are more effective in detecting MildND and MajorND due to AD than ACEIIINaming.
| S-EPMC10313575 | biostudies-literature
Project description:Background Many microarray experiments search for genes with differential expression between a common “reference” group and multiple test groups, like in the case of time-course designs or of various treatments versus a control condition. In such cases, currently employed statistical approaches based on t-test or close derivatives have limited efficacy, mostly because estimation of noise is done on only two groups at time. Alternative approaches based on ANOVA correctly capture noise from all the groups, but then do not confront single test groups with the reference. We therefore conceived a statistical test for pairwise comparisons between the reference group and each test group that uses within-group variance calculated from all the groups. Results We implemented an R-Bioconductor package named Mulcom, with a statistical test derived from the Dunnett’s test, designed to compare multiple experimental groups against a common reference. In addition to the basic Dunnett’s t value, the package includes an optional minimal fold-change threshold, m. Thanks to automated, permutation-based estimation of False Discovery Rate (FDR), the package also permits fast optimization of the test, to obtain the maximum number of significant genes at a given FDR value. When applied on a time-course experiment profiled in parallel on two microarray platforms, and compared with currently used tests, Mulcom displayed higher concordance of significant genes in the two array platforms, and higher enrichment in functional annotation to categories related to the biology of the experiment. Conclusions The Mulcom package provides a fast and powerful tool for the identification of differentially expressed genes when several experimental conditions are compared with a common reference. We found that Mulcom leads to lists of differentially expressed genes that are particularly consistent across microarray platforms and enriched in significant classes of genes. In our opinion, the main reasons for these good performances are three: (i) within-group variability is estimated from all experimental groups even if only two of them are compared each time; (ii) the optional fold-change threshold m avoids false positives due to aberrantly low within-group variability; (iii) automated test optimization allows maximizing sensitivity without compromising specificity.
2011-08-10 | GSE26732 | GEO
Project description:Background Many microarray experiments search for genes with differential expression between a common “reference” group and multiple test groups, like in the case of time-course designs or of various treatments versus a control condition. In such cases, currently employed statistical approaches based on t-test or close derivatives have limited efficacy, mostly because estimation of noise is done on only two groups at time. Alternative approaches based on ANOVA correctly capture noise from all the groups, but then do not confront single test groups with the reference. We therefore conceived a statistical test for pairwise comparisons between the reference group and each test group that uses within-group variance calculated from all the groups. Results We implemented an R-Bioconductor package named Mulcom, with a statistical test derived from the Dunnett’s test, designed to compare multiple experimental groups against a common reference. In addition to the basic Dunnett’s t value, the package includes an optional minimal fold-change threshold, m. Thanks to automated, permutation-based estimation of False Discovery Rate (FDR), the package also permits fast optimization of the test, to obtain the maximum number of significant genes at a given FDR value. When applied on a time-course experiment profiled in parallel on two microarray platforms, and compared with currently used tests, Mulcom displayed higher concordance of significant genes in the two array platforms, and higher enrichment in functional annotation to categories related to the biology of the experiment. Conclusions The Mulcom package provides a fast and powerful tool for the identification of differentially expressed genes when several experimental conditions are compared with a common reference. We found that Mulcom leads to lists of differentially expressed genes that are particularly consistent across microarray platforms and enriched in significant classes of genes. In our opinion, the main reasons for these good performances are three: (i) within-group variability is estimated from all experimental groups even if only two of them are compared each time; (ii) the optional fold-change threshold m avoids false positives due to aberrantly low within-group variability; (iii) automated test optimization allows maximizing sensitivity without compromising specificity.
2011-08-10 | GSE26735 | GEO
Project description:We devise a method to detect and estimate forces in a heterogeneous environment based on experimentally recorded stochastic trajectories. In particular, we focus on systems modeled by the heterogeneous overdamped Langevin equation. Here, the observed drift includes a "spurious" force term when the diffusivity varies in space. We show how Bayesian inference can be leveraged to reliably infer forces by taking into account such spurious forces of unknown amplitude as well as experimental sources of error. The method is based on marginalizing the force posterior over all possible spurious force contributions. The approach is combined with a Bayes factor statistical test for the presence of forces. The performance of our method is investigated analytically, numerically and tested on experimental data sets. The main results are obtained in a closed form allowing for direct exploration of their properties and fast computation. The method is incorporated into TRamWAy, an open-source software platform for automated analysis of biomolecule trajectories.
| S-EPMC7052274 | biostudies-literature
Project description:A persistent challenge lies in the interpretation of consensus and discord from functional genomics experimentation. Harmonizing and analyzing this data will enable investigators to discover relations of many genes to many diseases, and from many phenotypes and experimental paradigms to many diseases through their genomic substrates. The GeneWeaver.org system provides a platform for cross-species integration and interrogation of heterogeneous curated and experimentally derived functional genomics data. GeneWeaver enables researchers to store, share, analyze, and compare results of their own genome-wide functional genomics experiments in an environment containing rich companion data obtained from major curated repositories, including the Mouse Genome Database and other model organism databases, along with derived data from highly specialized resources, publications, and user submissions. The data, largely consisting of gene sets and putative biological networks, are mapped onto one another through gene identifiers and homology across species. A versatile suite of interactive tools enables investigators to perform a variety of set analysis operations to find consilience among these often noisy experimental results. Fast algorithms enable real-time analysis of large queries. Specific applications include prioritizing candidate genes for quantitative trait loci, identifying biologically valid mouse models and phenotypic assays for human disease, finding the common biological substrates of related diseases, classifying experiments and the biological concepts they represent from empirical data, and applying patterns of genomic evidence to implicate novel genes in disease. These results illustrate an alternative to strict emphasis on replicability, whereby researchers classify experimental results to identify the conditions that lead to their similarity.
| S-EPMC4602068 | biostudies-literature
Project description:BackgroundMany biological processes, such as cancer metastasis, organismal development, and acquisition of resistance to cytotoxic therapy, rely on the emergence of rare sub-clones from a larger population. Understanding how the genetic and epigenetic features of diverse clones affect clonal fitness provides insight into molecular mechanisms underlying selective processes. While large-scale barcoding with NGS readout has facilitated cellular fitness assessment at the population level, this approach does not support characterization of clones prior to selection. Single-cell genomics methods provide high biological resolution, but are challenging to scale across large populations to probe rare clones and are destructive, limiting further functional analysis of important clones.ResultsHere, we develop CloneSifter, a methodology for tracking and enriching rare clones throughout their response to selection. CloneSifter utilizes a CRISPR sgRNA-barcode library that facilitates the isolation of viable cells from specific clones within the barcoded population using a sequence-specific retrieval reporter. We demonstrate that CloneSifter can measure clonal fitness of cancer cell models in vitro and retrieve targeted clones at abundance as low as 1 in 1883 in a heterogeneous cell population.ConclusionsCloneSifter provides a means to track and access specific and rare clones of interest across dynamic changes in population structure to comprehensively explore the basis of these changes.
| S-EPMC7687773 | biostudies-literature