Analysis Of Multiple Datasets Research Articles

Canonical Correlation Analysis for Data Fusion and Group Inferences

We have presented two CCA-based approaches for data fusion and group analysis of biomedical imaging data and demonstrated their utility on fMRI, sMRI, and EEG data. The results show that CCA and M-CCA are powerful tools that naturally allow the analysis of multiple data sets. The data fusion and group analysis methods presented are completely data driven, and use simple linear mixing models to decompose the data into their latent components. Since CCA and M-CCA are based on second-order statistics they provide a relatively lessstrained solution as compared to methods based on higherorder statistics such as ICA. While this can be advantageous, the flexibility also tends to lead to solutions that are less sparse than those obtained using assumptions of non-Gaussianity-in particular superGaussianity-at times making the results more difficult to interpret. Thus, it is important to note that both approaches provide complementary perspectives, and hence it is beneficial to study the data using different analysis techniques.

ARE SPECIES REAL? THE SHAPE OF THE SPECIES BOUNDARY WITH EXPONENTIAL FAILURE, REINFORCEMENT, AND THE “MISSING SNOWBALL”

Under simple assumptions, the evolution of epistatic "Dobzhansky-Muller" incompatibilities between a pair of species should yield an accelerating decline of log overall reproductive compatibility--a "snowball" effect that might rapidly provide new species with "reality." Possible alternatives include: (1) simple exponential failure, giving a linear rate of log compatibility loss, and (2) "slowdown," likely during reinforcement in which mate choice evolves to prevent deleterious hybridization, yielding a decelerating log compatibility loss. In analyses of multiple datasets, we find little support for the snowball effect, except possibly in Lepidoptera hybrid viability. The snowball predicts a slow initial rate of incompatibility acquisition, with low initial variance; instead, highly variable compatibility is almost universally observed at low genetic distances. Another deviation from predictions is that reproductive isolation usually remains incomplete until long after speciation. These results do not disprove snowball compatibility decay, but can result if large deleterious effects are due to relatively few genetic changes, or if different types of incompatibility evolve at very different rates. On the other hand, data on Bacillus and Saccharomyces, as well as theories of chromosomal evolution, suggest that some kinds of incompatibility accumulate approximately linearly, without Dobzhansky-Muller effects. In microorganisms, linearity can result from direct negative effects of DNA sequence divergence on compatibility. Finally, a decelerating slowdown model is supported for sympatric Leptasterias starfish, and in Drosophila prezygotic isolation in sympatry but not allopatry, providing novel comparative evidence for reinforcement.

Automating approximate Bayesian computation by local linear regression

BackgroundIn several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method.ResultsThe software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown.ConclusionIn practice, the ABCreg simplifies implementing ABC based on local-linear regression.

A hierarchical Bayesian approach to multi‐state mark–recapture: simulations and applications

Summary Mark–recapture models are valuable for assessing diverse demographic and behavioural parameters, yet the precision of traditional estimates is often constrained by sparse empirical data. Bayesian inference explicitly recognizes estimation uncertainty, and hierarchical Bayes has proven particularly useful for dealing with sparseness by combining information across data sets. We developed a general hierarchical Bayesian multi‐state mark–recapture model, tested its performance on simulated data sets and applied it to real ecological data on stopovers by migratory birds. Our hierarchical model performed well in terms of both precision and accuracy of parameters when tested with simulated data of varying quality (sample size, capture and survivorship probabilities). It also provided more precise and accurate parameter estimates than a non‐hierarchical model when data were sparse. A specific version of the model, designed for estimation of daily transience and departure of migratory birds at a mid‐route stopover, was applied to 11 years of autumn migration data from Atlantic Canada. Hierarchical estimates of departure and transience were more precise than those derived from parallel non‐hierarchical and frequentist methods, and indicated that inter‐annual variability in parameters suggested by these other methods was largely due to sampling error. Synthesis and applications. Estimates of demographic parameters, often derived from mark–recapture studies, provide the basis for evaluating the status of species at risk, for developing conservation and management strategies and for evaluating the results of current protocols. The hierarchical Bayesian multi‐state mark–recapture model presented here permits partitioning of complex parameter variation across space or time, and the simultaneous analysis of multiple data sets results in a marked increase in the precision of estimates derived from sparse capture data. Its structural flexibility should make it a valuable tool for conservation ecologists and wildlife managers.

Integrative data analysis: The simultaneous analysis of multiple data sets.

There are both quantitative and methodological techniques that foster the development and maintenance of a cumulative knowledge base within the psychological sciences. Most noteworthy of these techniques is meta-analysis, which allows for the synthesis of summary statistics drawn from multiple studies when the original data are not available. However, when the original data can be obtained from multiple studies, many advantages stem from the statistical analysis of the pooled data. The authors define integrative data analysis (IDA) as the analysis of multiple data sets that have been pooled into one. Although variants of IDA have been incorporated into other scientific disciplines, the use of these techniques is much less evident in psychology. In this article the authors present an overview of IDA as it may be applied within the psychological sciences, discuss the relative advantages and disadvantages of IDA, describe analytic strategies for analyzing pooled individual data, and offer recommendations for the use of IDA in practice.

From individuals to flyways: The future of marking birds for conservation

The broad agenda for bird and biodiversity conservation, together with wider environmental issues such as global environmental change and disease control, will continue to set the context and priorities for research based on marked birds. We outline the ways in which the British Trust for Ornithology (BTO) Ringing Scheme will address these issues and how it will contribute to all six science themes of the BTO Strategy. New technologies for tracking birds will continue to increase in importance, facilitating novel study designs that require fewer birds to be marked and will lead to rapid increases in knowledge complementing more traditional bird ringing. Continental and flyway mechanisms for rapid collation, sharing and analysis of data will become the norm. The power of analysis and modelling techniques will continue to grow, facilitating integrated analyses of multiple data sets. The scientific progress achieved through large‐scale bird ringing over the last 100 years has been critically dependent on thousands of highly skilled and dedicated volunteers, whose efforts will be equally important in the future. Maintaining their enthusiasm and support at a time of unprecedented changes in technology and study designs will be a key challenge for bird‐marking schemes during the first half of the 21st century.

Integrative data analysis: The simultaneous analysis of multiple data sets.

Integrative data analysis: The simultaneous analysis of multiple data sets.

Analyzing multiple data sets by interconnecting RSAT programs via SOAP Web services—an example with ChIP-chip data

This protocol shows how to access the Regulatory Sequence Analysis Tools (RSAT) via a programmatic interface in order to automate the analysis of multiple data sets. We describe the steps for writing a Perl client that connects to the RSAT Web services and implements a workflow to discover putative cis-acting elements in promoters of gene clusters. In the presented example, we apply this workflow to lists of transcription factor target genes resulting from ChIP-chip experiments. For each factor, the protocol predicts the binding motifs by detecting significantly overrepresented hexanucleotides in the target promoters and generates a feature map that displays the positions of putative binding sites along the promoter sequences. This protocol is addressed to bioinformaticians and biologists with programming skills (notions of Perl). Running time is approximately 6 min on the example data set.

Geological modelling from field data and geological knowledge: Part II. Modelling validation using gravity and magnetic data inversion

The analysis of multiple data sets is required to select a realistic 3D geological model among an infinite number of possibilities. An inverse method that aims at describing the 3D geometry of geological objects is presented. The method takes into account the geology and the physical properties of rocks, while respecting the topological properties of an a priori model. The a priori model is built from the geological data set and its geometry is largely dependent upon assumptions about inaccessible geology at depth. This method, referred to as “total litho-inversion” is a generalised 3D inversion that results in quantifying the lithology and the distribution of rock property in a probabilistic way. Its application is demonstrated through (i) a simple synthetic case and (ii) the relative distribution characterization of granites and diorites in an orogenic domain.

Analysis of Multiple Data Sets in Outdoor Recreation Research: Introduction to the Special Issue

Outdoor recreation research has evolved and matured over the past several decades. The modern era of outdoor recreation research can be traced to the studies supporting the Outdoor Recreation Resources Review Commission (ORRRC) in the early 1960s. Since that time, the scientific and professional literature has expanded dramatically and several trends in outdoor recreation research are evident. First, research has evolved from primarily descriptive empirically based studies of visitor characteristics and use patterns to more theoretically based analytical studies of visitor behavior and the underlying meanings of outdoor recreation. Second, the research-based literature in outdoor recreation has been synthesized in a number of conceptual/organizational frameworks (e.g., the Recreation Opportunity Spectrum, carrying capacity frameworks) that are useful for integrating multiple studies and guiding further research and management. Third, the synergistic effects of an accumulating body of research have developed a strong theoretical understanding of important issues in outdoor recreation including crowding, conflict, motivations and benefits, norms, substitutability, specialization, and sense of place. Fourth, issues addressed in outdoor recreation research continue to evolve to meet societal interests and needs including the relationship between outdoor recreation and race, ethnicity, and gender; the appropriate roles of fees in outdoor recreation; and the characteristics and impacts of new forms of outdoor recreation such as mountain biking. Analysis of multiple data sets offers another approach for outdoor recreation research to evolve and mature. Recreation research has been traditionally characterized by cross-sectional surveys of recreation participants and, occasionally, other populations. The resulting data tend to be isolated in space and time and often lack the ability to support theoretical development and broad generalization. As multiple data sets have been generated using widely accepted and reasonably comparable study methods, new integrative analytical approaches have become possible and potentially productive. Examples of these approaches include meta-analysis, comparative analysis, time series analysis, and

A practical approach to using Multiple-Race response data: A bridging method for publicuse microdata

Revised federal policies require that multiple-race responses be allowed in all federal data collection efforts, but many researchers find the multitude of race categories and variables very difficult to use. Important comparability issues also interfere with using multiple-race data in analyses of multiple data sets and/or several points in time. These difficulties have, in effect, discouraged the use of the new data on race. We present a practical method for incorporating multiple-race respondents into analyses that use public-use microdata. Our method is a modification of the regression method developed by the National Center for Health Statistics (NCHS), which uses multiple-race respondents' specific combination of races, as well as other individual-level and contextual characteristics, to predict the respondents 'preferred single race. In this paper we (1) apply the NCHS-generated regression coefficients to public-use microdata with limited geographic information; and (2) provide a downloadable computer program with which researchers can apply this practical and preferable method for including multiple-race respondents in a wide variety of analyses.

SU-FF-T-149: Development and Validation of An MCNPX Monte Carlo IMRT Source Model with Optimized Radiotherapy Interface and Comparative Analysis Software

Purpose:Dosimetric accuracy is essential for successful radiotherapy and paramount to the medical physicist. In an effort to implement the most advanced dose deposition algorithm available, an MCNP‐based Monte CarloIMRTsourcemodel of a Novalis LINAC with mMLC has been developed and benchmarked. Concurrently, accompanying software has also been developed to facilitate and optimize CT‐based Monte Carlo simulations and subsequent comparative analysis against various measurement types and calculations. Method and Materials: A Monte Carlosourcemodel has been developed and thoroughly benchmarked in accordance with guidelines of AAPM Task Group 53 as well as Medical Physics Monograph No. 32. To that end, extensive water phantom/ion chamber measurements as well as axial and coronal film measurements were acquired to adjust and confirm the MCNP sourcemodel. In order to better tune the sourcemodel as well as visualize datasets and provide comparative Gamma analysis against TPS calculations and various measurements, a comparative software suite has been fully developed and refined. Additionally, an input file preparation Graphic User Interface (GUI) has been created in order to provide efficient, optimized, and accurate CT‐Based simulations. These simulations are optimized by greatly reducing the number of extraneous voxels present in the input file lattice while preserving patient representation. Results: The Monte Carlosourcemodel been refined to provide simulation results that agree with measurements on the order of a well‐commissioned Treatment Planning System. The input file preparation software allows for greatly optimized patient‐based simulations that decrease simulation times over 60% while preserving dosimetric resolution. The comparative analysissoftware provides genuinely useful visualization and analysis of multiple datasets. Conclusion: This Monte CarloIMRTsourcemodel, combined with the radiotherapy interface and post‐processing comparative software, provides a powerful series of tools for clinical and dosimetric investigations.

Using Sociotransformative Constructivism to Create Multicultural and Gender-Inclusive Classrooms

Maxima was an intervention project that focused on assisting teachers to establish more inquiry-based, gender-inclusive, and culturally relevant learning environments. The authors grounded the project by using sociotransformative constructivism as a theoretical framework to steer the implementation of three guiding concepts for professional development. These guiding concepts are as follows: being responsive and theoretically explicit, providing ongoing and on-site support, and reflexive approaches to collaboration. The analysis of multiple data sets gathered during Year 1 of the project indicates that most of the teachers responded positively to the proposed guiding concepts and significantly changed their teaching practice.

How settings change people: Applying behavior setting theory to consumer‐run organizations

AbstractSelf‐help initiatives stand as a classic context for organizational studies in community psychology. Behavior setting theory stands as a classic conception of organizations and the environment. This study explores both, applying behavior setting theory to consumer‐run organizations (CROs). Analysis of multiple data sets from all CROs in Kansas provide insight into how organizational size influences member participation, how members benefit from participation, and how behavior setting theory may need to be revised. Results suggest that members do benefit from participation. However, individual involvement in organizational planning and decision making does not appear to be the primary factor leading to member benefit. The predictions of behavior setting theory are found to be true, but results suggest that these predictions provide an incomplete picture of CROs. The incorporation of roles as a unit of analysis within behavior settings is suggested to improve the explanatory power of this theory. © 2007 Wiley Periodicals, Inc.

Analysis of Multiple Data Sets in Outdoor Recreation Research

The modern era of outdoor recreation research can be traced to the studies supporting the Outdoor Recreation Resources Review Commission in the early 1960s. Since that time, outdoor recreation rese...

Analysis of Multiple Data Sets in Outdoor Recreation Research

Analysis of Multiple Data Sets in Outdoor Recreation Research

A CT Database for Research, Development and Education: Concept and Potential

Both in radiology and in surgery, numerous applications are emerging that enable 3D visualization of data from various imaging modalities. In clinical practice, the patient's images are analyzed on work stations in the Radiology Department. For specific preclinical and educational applications, however, data from single patients are insufficient. Instead, similar scans from a number of individuals within a collective must be compiled. The definition of standardized acquisition procedures and archiving formats are prerequisite for subsequent analysis of multiple data sets. Focusing on bone morphology, we describe our concept of a computer database of 3D human bone models obtained from computed tomography (CT) scans. We further discuss and illustrate deployment areas ranging from prosthesis design, over virtual operation simulation up to 3D anatomy atlases. The database of 3D bone models described in this work, created and maintained by the AO Development Institute, may be accessible to research institutes on request.

Hidden Likelihood Support in Genomic Data: Can Forty-Five Wrongs Make a Right?

Combined analysis of multiple phylogenetic data sets can reveal emergent character support that is not evident in separate analyses of individual data sets. Previous parsimony analyses have shown that this hidden support often accounts for a large percentage of the overall phylogenetic signal in cladistic studies. Here, reanalysis of a large comparative genomic data set for yeast (genus Saccharomyces) demonstrates that hidden support can be an important factor in maximum likelihood analyses of multiple data sets as well. Emergent signal in a concatenation of 106 genes was responsible for up to 64% of the likelihood support at a particular node (the difference in log likelihood scores between optimal topologies that included and excluded a supported clade). A grouping of four yeast species (S. cerevisiae, S. paradoxus, S. mikatae, and S. kudriavzevii) was robustly supported by combined analysis of all 106 genes, but separate analyses of individual genes suggested numerous conflicts. Forty-eight genes strictly contradicted S. cerevisiae + S. paradoxus + S. mikatae + S. kudriavzevii in separate analyses, but combined likelihood analyses that included up to 45 of the "wrong" data sets supported this group. Extensive hidden support also emerged in a combined likelihood analysis of 41 genes that each recovered the exact same topology in separate analyses of the individual genes. These results show that isolated analyses of individual data sets can mask congruence and distort interpretations of clade stability, even in strictly model-based phylogenetic methods. Consensus and supertree procedures that ignore hidden phylogenetic signals are, at best, incomplete.

Analysis of Multiple Data Sets Reveals No Association between the Insulin Gene Variable Number Tandem Repeat Element and Polycystic Ovary Syndrome or Related Traits

Variation at the insulin gene VNTR (variable number tandem repeat) minisatellite has been reported to be associated with polycystic ovary syndrome (PCOS), but findings have been inconsistent and all studies have featured small sample sizes. To gain a robust understanding of the role of the INS-VNTR in PCOS susceptibility. Case-control, family-based association and quantitative trait analyses. A UK population comprising 255 parent-offspring trios, 185 additional cases, and 1062 control subjects (cases and controls all British/Irish) as well as 1599 women from a northern Finland population-based birth cohort characterized for PCO symptomatology and testosterone levels. VNTR class was inferred from genotyping of the -23HphI variant. None. INS-VNTR genotype frequencies between subject groups, body mass index, and testosterone levels by genotype. Case-control analyses in both UK and Finnish samples failed to confirm previously reported class III allele associations with PCOS (UK, P = 0.43, Finnish, P = 0.31; Kruskal-Wallis chi2). Transmission analysis in trios showed no excess transmission of either allele (P = 0.62), regardless of parent of origin (maternal: P = 0.73; paternal: P = 0.66). No association between genotype and testosterone levels was seen in any sample (UK PCOS subjects, P = 0.95; Finnish symptomatic cases, P = 0.38; Finnish control women, P = 0.58). Despite the strong biological candidacy and supportive data from previous studies, we conclude that variation at the INS-VNTR has no major role in the development of PCOS.

Assessing the phylogenetic utility of four mitochondrial genes and a nuclear intron in the asian pit viper genus, Trimeresurus: separate, simultaneous, and conditional data combination analyses.

A number of methods have been proposed for addressing how to optimize the analysis of multiple data sets from diverse mitochondrial and nuclear gene partitions in the pursuit of robust organismal phylogenies. The present study used separate, simultaneous, and conditional data combination methods to analyze 3,135 bp of data from four mitochondrial partitions and the seventh intron of the beta fibrinogen gene in the Asian pit viper genus, Trimeresurus sensu stricto. The phylogenetic utility and homogeneity of all partitions were estimated via a combination of homogeneity partition tests, homoplasy indices, and partitioned Bremer support. Despite the detection of significant heterogeneity of phylogenetic signal between the mitochondrial and nuclear partitions, the simultaneous analysis represented the best-supported topology of all the data. The relatively slow rate (approximately one quarter of the rate of mtDNA) and functionally unconstrained molecular evolution of the intron resulted in much lower levels of homoplasy compared with the mitochondrial partitions. This was further shown via partitioned Bremer support, which, when considered throughout hierarchical clade levels, highlighted the phylogenetic strength and limitations of the intron at deeper and shallower phylogenetic levels, respectively. The simultaneous analysis helped to resolve the phylogenetic relationships of taxa that were unresolved throughout all individual gene trees and tentatively supports the existence of morphologically and genetically distinct clades within the genus. Topological appraisals of the mitochondrial gene partitions suggest that the cytochrome b and the NADH subunit 4 gene partitions are better estimators of phylogenetic relationships than are the 12S and 16S ribosomal RNA partitions at the taxonomic levels under consideration.