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PubMed1997

Data analysis using the Internet: the World Wide Web scanning probe microscopy data analysis system.

The first interactive world-wide web-based image analysis system is presented (http://pharm6.pharm.nottingham.ac.uk/processing/main. html). The system, currently tailored to scanning probe microscopy image data, has been developed to permit the use of software algorithms developed within our laboratory by researchers throughout the world. The implementation and functionality of the scanning probe microscopy server is described. Feedback from users of the facility has demonstrated its value within the research community, and highlighted key operational issues which are to be addressed. A future role of Internet-based data processing software is also discussed.

Algorithms↗
PubMed2004

Research in primary dental care Part 6: Data analysis.

The analysis of data from a research project seeks to answer the research question which the investigators set at the outset of the study. This article provides information on data analysis for both quantitative and qualitative data, always referring the reader back to their initial research question. Advice is also given on software which can be used for the analysis of data. A guide is provided to the choice of appropriate statistics for studies involving quantitative data.

Data Interpretation, Statistical↗
PubMedDate not supplied

The microcomputer: an alternative for data analysis.

Programs for data analysis available on microcomputers now rival those programs available for mainframes in terms of ease of use, accuracy, accessibility, and cost. Researchers should seriously consider the possibilities that exist for data analysis on both the microcomputer and the mainframe. An informed decision will allow best use of available resources.

Computers↗
PubMed2006

Regularization network-based gene selection for microarray data analysis.

Microarray data contains a large number of genes (usually more than 1000) and a relatively small number of samples (usually fewer than 100). This presents problems to discriminant analysis of microarray data. One way to alleviate the problem is to reduce dimensionality of data by selecting important genes to the discriminant problem. Gene selection can be cast as a feature selection problem in the context of pattern classification. Feature selection approaches are broadly grouped into filter methods and wrapper methods. The wrapper method outperforms the filter method but at the cost of more intensive computation. In the present study, we proposed a wrapper-like gene selection algorithm based on the Regularization Network. Compared with classical wrapper method, the computational costs in our gene selection algorithm is significantly reduced, because the evaluation criterion we proposed does not demand repeated training in the leave-one-out procedure.

Algorithms↗
PubMed2006

The use of qualitative data analysis software (QDAS) to manage and support the analysis of think aloud (TA) data.

This methodological paper describes how qualitative data analysis software (QDAS) is being used to manage and support a three-step protocol analysis (PA) of think aloud (TA) data in a study examining emergency nurses' reasoning during triage. The authors believe that QDAS program QRS NVivo will greatly facilitate the PA and will allow them to identify and describe the information that triage nurses concentrate on during triage, and how they structure this information to make a triage decision. These findings could assist in designing and creating decision support systems to guide nurses' triaging. Additionally, details about how to use QRS NVivo for PA of TA data may assist and guide future informatics research using similar methodology are presented here. This innovative use of QDAS holds great promise for future nursing informatics research.

Decision Support Systems, Clinical↗
PubMed2005

CGHPRO -- a comprehensive data analysis tool for array CGH.

BACKGROUND: Array CGH (Comparative Genomic Hybridisation) is a molecular cytogenetic technique for the genome wide detection of chromosomal imbalances. It is based on the co-hybridisation of differentially labelled test and reference DNA onto arrays of genomic BAC clones, cDNAs or oligonucleotides, and after correction for various intervening variables, loss or gain in the test DNA can be indicated from spots showing aberrant signal intensity ratios. Now that this technique is no longer confined to highly specialized laboratories and is entering the realm of clinical application, there is a need for a user-friendly software package that facilitates estimates of DNA dosage from raw signal intensities obtained by array CGH experiments, and which does not depend on a sophisticated computational environment. RESULTS: We have developed a user-friendly and versatile tool for the normalization, visualization, breakpoint detection and comparative analysis of array-CGH data. CGHPRO is a stand-alone JAVA application that guides the user through the whole process of data analysis. The import option for image analysis data covers several data formats, but users can also customize their own data formats. Several graphical representation tools assist in the selection of the appropriate normalization method. Intensity ratios of each clone can be plotted in a size-dependent manner along the chromosome ideograms. The interactive graphical interface offers the chance to explore the characteristics of each clone, such as the involvement of the clones sequence in segmental duplications. Circular Binary Segmentation and unsupervised Hidden Markov Model algorithms facilitate objective detection of chromosomal breakpoints. The storage of all essential data in a back-end database allows the simultaneously comparative analysis of different cases. The various display options facilitate also the definition of shortest regions of overlap and simplify the identification of odd clones. CONCLUSION: CGHPRO is a comprehensive and easy-to-use data analysis tool for array CGH. Since all of its features are available offline, CGHPRO may be especially suitable in situations where protection of sensitive patient data is an issue. It is distributed under GNU GPL licence and runs on Linux and Windows.

Algorithms↗
PubMed1977

[Graphical methods in data analysis (author's transl)].

Data analysis is concerned with attentive description and communication of the information contents of a body of data. Background information, conceptual insight and especially graphical methods play a key role in data analysis for developing a feeling for the data both by formal procedures to be applied in the light of specified models and even more by informal inference or methods that are suggestive and conctructive. This paper reviews graphical methods useful for description, screening, analysis, cross-examining, selection, reduction, presentation and summary of data: for uncovering distributional peculiarities and understanding the structure underlying experimental and survey data. Moreover scatter plots, probability plots and residual plots provide insight into the possible inappropriateness of certain assumptions of the statistical model. Some techniques are illustrated by examples: four-dimensional data may be reprented as scatter plot on ordinary graph paper by using a combination of 2 different sets of symbols for at most 7 different levels of the third variable (formula: see text) and of the fourth variable (formula: see text). Comments on the use of tables and graphical methods, a small overview of the latter and of the scope of applications endeavour to pave the way such that structures may be better understandable and unanticipated characteristics may be spotted.

Factor Analysis, Statistical↗
PubMed2002

The quantitative evaluation of functional neuroimaging experiments: the NPAIRS data analysis framework.

We introduce a data-analysis framework and performance metrics for evaluating and optimizing the interaction between activation tasks, experimental designs, and the methodological choices and tools for data acquisition, preprocessing, data analysis, and extraction of statistical parametric maps (SPMs). Our NPAIRS (nonparametric prediction, activation, influence, and reproducibility resampling) framework provides an alternative to simulations and ROC curves by using real PET and fMRI data sets to examine the relationship between prediction accuracy and the signal-to-noise ratios (SNRs) associated with reproducible SPMs. Using cross-validation resampling we plot training-test set predictions of the experimental design variables (e.g., brain-state labels) versus reproducibility SNR metrics for the associated SPMs. We demonstrate the utility of this framework across the wide range of performance metrics obtained from [(15)O]water PET studies of 12 age- and sex-matched data sets performing different motor tasks (8 subjects/set). For the 12 data sets we apply NPAIRS with both univariate and multivariate data-analysis approaches to: (1) demonstrate that this framework may be used to obtain reproducible SPMs from any data-analysis approach on a common Z-score scale (rSPM[Z]); (2) demonstrate that the histogram of a rSPM[Z] image may be modeled as the sum of a data-analysis-dependent noise distribution and a task-dependent, Gaussian signal distribution that scales monotonically with our reproducibility performance metric; (3) explore the relation between prediction and reproducibility performance metrics with an emphasis on bias-variance tradeoffs for flexible, multivariate models; and (4) measure the broad range of reproducibility SNRs and the significant influence of individual subjects. A companion paper describes learning curves for four of these 12 data sets, which describe an alternative mutual-information prediction metric and NPAIRS reproducibility as a function of training-set sizes from 2 to 18 subjects. We propose the NPAIRS framework as a validation tool for testing and optimizing methodological choices and tools in functional neuroimaging.

Adult↗
PubMed1995

The impact of data-analysis methods on cumulative research knowledge: statistical significance testing, confidence intervals, and meta-analysis.

The methods of data analysis used in research have a major effect on the development of cumulative knowledge. Traditional methods based on statistical significance testing have systematically retarded the growth of cumulative research by making it virtually impossible to discern the real meaning of research literatures. Meta-analysis makes it possible to demonstrate graphically the high price the research enterprise has paid for its reliance on significance testing. But in addition to these demonstrations, reform will require that researchers come to understand that the benefits they see as flowing from the use of significance tests are illusory. In research practice and in training of researchers, we must use and teach appropriate data analysis methods: point estimates of effect sizes and confidence intervals within individual studies, and meta-analysis in the integration of multiple studies to produce final conclusions. These reforms are essential to the progress of cumulative research knowledge.

Analysis of Variance↗
PubMed2001

Using real-time recording to enhance the analysis of within-session functional analysis data.

Functional analysis methods have become standard practice for determining the maintaining variables of problem behavior. The analysis of within-session response patterns has been proposed as a useful adjunct to the functional analysis. Many within-session analyses have been conducted on data obtained from interval scoring methods. However, interval methods only provide an estimate of within-session data. The authors briefly describe a real-time recording method and provide a rationale for its use. The authors then provide descriptions of several research studies from their lab in which real-time data were crucial in determining behavioral function from experimental analyses.

Adolescent↗
PubMed1996

Non-compliance in elderly people: evaluation of risk factors by longitudinal data analysis.

Studies on risk factors for drug non-compliance have not taken into account the possibility of correlated outcomes. We therefore conducted a study into risk factors for non-compliance by using analysis techniques that adjust for these correlations (longitudinal data analysis). Data were obtained from interviews and pharmacy records in a cross-sectional survey in Amsterdam. The subjects were 157 elderly people aged 70 years or older. Of these subjects, 37 were residents of a home for the elderly, 40 were community-dwelling elderly who needed to be visited regularly by a district nurse, and 80 were community-dwelling elderly who did not need to be visited by a district nurse. Most drugs (78%) were used according to the directions; the remainder (22%) were not used as intended. Odds ratios (95% confidence intervals) for non-compliance for moderate and poor/wrong knowledge of the purpose of a drug as compared with good/correct knowledge were 2.8 (1.2-6.7) and 4.2 (1.5-12), respectively. Drug regimens of two times daily and more than two times daily were associated with odds ratios for non-compliance of 4.5 (1.6-12) and 4.2 (1.7-11), respectively, compared to a regimen of once daily. Compliance increased if a drug was prescribed by a specialist instead of a general practitioner odds ratio 0.1 (0.04-0.4)]. There was no significant relation between compliance and the number of drugs prescribed to a patient, sex, age, living situation, patient group, or perceived effect. This study, which was based on longitudinal data analysis, demonstrates that in elderly people non-compliance with drug therapy is related to the knowledge of purpose of a drug, the complexity of a drug regimen, and the type of prescriber. The positive association between compliance and the number of drugs prescribed found in former studies was not confirmed.

Aged↗
PubMedDate not supplied

The impact of centering first-level predictors on individual and contextual effects in multilevel data analysis.

BACKGROUND: Multilevel data analysis is a powerful analytical tool. Properly applying the models and correctly interpreting the findings are two interrelated general issues in using multilevel modeling (MLM). There are two specific issues when using MLM: (a) separating the individual-level effects of a predictor variable from its contextual effects and (b) centering first-level predictor variables. This can have major implications for interpreting the results at higher levels, and its impact on second-level interpretation is not always apparent. OBJECTIVES: The major purposes of this article are to show how to separate organizational-level effects from individual-level effects and to show how first-level centering decisions affect the interpretation of second-level coefficients. METHODS: The hierarchical linear models (HLM) are used to analyze a hypothetical data set with 385 patients nested within 10 hospitals, using uncentered, group-mean-centered, and grand-mean-centered versions of the predictor variable. RESULTS: Uncentered and grand-mean-centered models are equivalent, but group-mean-centered models are not equivalent to the other two. For the grand-mean-centered and uncentered models, second-level coefficients provide correct estimates of the individual effect and the contextual effect when the contextual predictor variable is included in the second-level model. The group-mean-centered model leads to a second-level coefficient where individual-level effects are confounded with contextual-level effects. DISCUSSION: There is no single best answer to the question of whether to use group-mean centering or grand-mean centering. The theory and specific questions to be answered should be the researcher's guide to selecting which centering approach to use. Understanding the implications of first-level centering is essential to interpreting second-level coefficients correctly.

Data Interpretation, Statistical↗
PubMed2006

A stable gene selection in microarray data analysis.

BACKGROUND: Microarray data analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most significantly differentially expressed genes under different conditions, and it has been a central research focus. In general, a better gene selection method can improve the performance of classification significantly. One of the difficulties in gene selection is that the numbers of samples under different conditions vary a lot. RESULTS: Two novel gene selection methods are proposed in this paper, which are not affected by the unbalanced sample class sizes and do not assume any explicit statistical model on the gene expression values. They were evaluated on eight publicly available microarray datasets, using leave-one-out cross-validation and 5-fold cross-validation. The performance is measured by the classification accuracies using the top ranked genes based on the training datasets. CONCLUSION: The experimental results showed that the proposed gene selection methods are efficient, effective, and robust in identifying differentially expressed genes. Adopting the existing SVM-based and KNN-based classifiers, the selected genes by our proposed methods in general give more accurate classification results, typically when the sample class sizes in the training dataset are unbalanced.

Algorithms↗
PubMed1997

Transformation and average as handy tools for pressure measurement data analysis.

INTRODUCTION:: Pressure measurement data underneath[Table: see text] the foot represent usually a large number of data files which is equal to double product of the number of the subjects in study and of the number of the trials for each foot. Besides, 'normal' parameters are necessary to compare the results for the controls and for the subjects in study. Statistical parameters (mean values and standard deviations) are not very convenient when it is required to establish cartographical and geometrical parameters or peak pressures, maximum forces, contact areas, contact time and others for foot typing or as a standard. Sometimes it is reasonable to obtain average pressure data as a base for further data analysis. But taking into account that pressure distribution pattern may differ by its length, width and angle of the inclination of the foot axis to the horizontal it was necessary to solve a problem of transformation of any pressure picture to the given length and width (for vertical location of the foot axis). METHODS:: The transformed coordinates X(*) and Y(*) are defined from the following system of equations: X = Y(*) x width/width(*) x sina + X(*) x length/length(*) x cosa + X(o) Y = Y(*) x width/width(*) x cosa - X(*) x length/length(*) x sina + Y(o) where X(o), Y(o) -- the coordinates of the origin; width, length, a -- parameters of the initial pressure picture; width(*), length(*) -- parameters of the transformed pressure picture. X, Y -- are real values but the values of pressure are defined only in the integer coordinates. One of the main parts of average is a reduction of the time process to any number of frames. If N(max) is a maximum number of frames in one of the data files than all other data files must be transformed with accordance to the following formulas: for j-th frame the value of pressure for sensor with coordinates x, y is: P(*1)(xy) (j) = P(1)(xy) (i) x ki + P(1)(xy) (i+1) x k(i+1), where P(1)(xy) (i) -- pressure for sensor with coordinates x, y of the 1-th initial time process; j = 0, ellipsis, N(max) -1; j-th frame corresponds to i-th frame in the initial time process as i = integer(j) x (N -1)/(N(max) -1), N -- a number of frames in initial time process; coefficients k(i) and k(i+1) are equal to k(i) = j x (N -1)/(N(max) -1) -- I, k(i+1) = 1 - ki. Then the values of pressure are averaged: P(*)(xy) (j) = 1 / L x S P(1)(xy) (j), where L is a number of data files. Corresponding software was developed. RESULTS AND DISCUSSION:: 38 files of pressure measurement data of the teenagers from military school were chosen for transformation and average. Statistical errors of transformation and average were calculated for each data file (for peak pressure, force and area) and for all parameters from two software packages: Novel-win v.95.121 and Novel-ortho v.95.121. The maximum errors are for peak pressures (about 7%-10%) and contact areas (about 6%). CONCLUSION:: Transformation and average programs can be successfully used for data analysis additionally to traditional statistical software: groupmask evaluation program.

Journal Article↗
PubMed2001

Validation of a chromatography data analysis software.

The performance of chromatography data analysis software packages is of cardinal importance when the precision and the accuracy of a chromatographic system are evaluated. Users cannot rely on a procedure generating chromatographic data of known accuracy. Holistic approaches cannot always be entirely trusted. We propose a new method consisting in validating a data analysis package against computer generated chromatograms of exactly known characteristics by feeding these chromatograms into the vendor supplied software and comparing the results supplied by the software and the exact answers. We simulated symmetrical and tailing chromatograms and processed these signals with the Agilent Technologies (formerly Hewlett-Packard) ChemStation software. The noise profile (i.e. the power spectrum of the baseline) was determined for a HPLC UV detector prior to the calculations, and chromatograms of different signal-to-noise ratios were used for the analysis. For every chromatogram, we simulated 25 replicates with identical signal-to-noise ratios but different noise sequences. In this manner, both the random and the systematic errors of the retention data and peak shape characteristics can be evaluated. When analyzing tailing peaks, we simulated the effects of extra-column band broadening and those of column overload. Our calculations show that the general performance of the data analysis system studied is excellent. The contribution of the random error originating from the data analysis procedure is in most cases negligible compared to the repeatability of the chromatographic measurement itself.

Chromatography, High Pressure Liquid↗