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PubMed2003

Initiation into qualitative data analysis.

Qualitative data analysis may be daunting to nursing graduate students and other novice qualitative researchers. This article describes a qualitative research project required in a graduate nursing research course that was desired to guide students through their first qualitative data analysis. Common pitfalls novice researchers may encounter, such as data shuffling, premature closure, and overly delayed closure, also are discussed. The project reported in this article not only decreased students' anxiety concerning qualitative data analysis but also helped students gain respect for the amount of time, effort, and creativity this process entails.

Comprehension↗
PubMed2000

Independent component analysis for noisy data--MEG data analysis.

Independent component analysis (ICA) is a new, simple and powerful idea for analyzing multi-variant data. One of the successful applications is neurobiological data analysis such as electroencephalography (EEG), magnetic resonance imaging (MRI), and magnetoencephalography (MEG). However, many problems remain. In most cases, neurobiological data contain a lot of sensor noise, and the number of independent components is unknown. In this article, we discuss an approach to separate noise-contaminated data without knowing the number of independent components. A well-known two stage approach to ICA is to pre-process the data by principal component analysis (PCA), and then the necessary rotation matrix is estimated. Since PCA does not work well for noisy data, we implement a factor analysis model for pre-processing. In the new pre-processing, the number of sources and the amount of sensor noise are estimated. After the preprocessing, the rotation matrix is estimated using an ICA method. Through the experiments with MEG data, we show this approach is effective.

Artifacts↗
PubMed2006

The use of configural frequency analysis for explorative data analysis.

Configural frequency analysis (CFA) is a widely used method of explorative data analysis. It tries to detect patterns in the data that occur significantly more or significantly less often than expected by chance. Patterns which occur more often than expected by chance are called CFA types, while those which occur less often than expected by chance are called CFA antitypes. The patterns detected are used to generate knowledge about the mechanisms underlying the data. We investigate the ability of CFA to detect adequate types and antitypes in a number of simulation studies. The basic idea of these studies is to predefine sets of types and antitypes and a mechanism which uses them to create a simulated data set. This simulated data set is then analysed with CFA and the detected types and antitypes are compared to the predefined ones. The predefined types and antitypes together with the method to generate the data are called a data generation model. The results of the simulation studies show that CFA can be used in quite different research contexts to detect structural dependencies in observed data. In addition, we can learn from these simulation studies how much data is necessary to enable CFA to reconstruct the predefined types and antitypes with sufficient accuracy. For one of the data generation models investigated, implicitly underlying knowledge space theory, it was shown that zero-order CFA can be used to reconstruct the predefined types (which can be interpreted in this context as knowledge states) with sufficient accuracy. Theoretical considerations show that first-order CFA cannot be used for this data generation model. Thus, it is wrong to consider first-order CFA, as is done in many publications, as the standard or even only method of CFA.

Humans↗
PubMed1997

[Statistical analysis of pharmacological data: a proposal for the data analysis of repeated measurements].

This paper considers the method of data analysis for a pharmacological experiment with multiple dose groups, in which a response variable is observed repeatedly at several time periods. The in-appropriateness of conventionally used statistical methods such as the t-test, Dunnett's multiple comparison procedure and analysis of variance method is pointed out. We recommended that the dose-response relationship should be estimated as a response function of time incorporating the dose effect as parameters. We also propose to adopt a strategy that the researcher tests some hypotheses on the parameters in the response function appropriate to the objective of the experiment after estimating parameters of the response function based on the observed data. A numerical example applying the strategy to real data is presented.

Data Interpretation, Statistical↗
PubMedDate not supplied

[Content analysis: a qualitative data analysis tool in health care].

The content analysis method utilizes various techniques for qualitative data analysis. However, for a novice researcher or one who is unfamiliar with this technique, understanding and applying this method has proved to be difficult. Therefore, this study was undertaken with the purpose of clarifying important concepts and aspects of this method as well as discussing its core points. The content analysis method was systematized during the first half of the twentieth century and, at first, it was an important tool for analyzing articles and advertisements published by the media in the United States. Presently, it is widely used in scientific research in the field of health care.

Nursing↗
PubMed2006

The microarray data analysis process: from raw data to biological significance.

Despite advances in microarray technology that have led to increased reproducibility and substantial reductions in the cost of microarrays, the successful use of this technology is still elusive for many researchers, and microarray data analysis in particular presents a substantial bottleneck for many biomedical researchers. There are many reasons for this, including the expense of and a lack of adequate training in the use of analysis software. An additional reason is that microarray data analysis has largely been treated in the past as a set of separate steps, with the majority of emphasis being placed on statistical analysis and visualization of the data. For many biomedical researchers determining the biological significance of the data has been the greatest challenge and in the last several years more emphasis has been placed on this aspect of the analysis process. Despite this broadening of the scope of analysis there are still several aspects of the process that continue to be neglected, including additional related and interdependent aspects, such as experimental design, data accessibility, and platform selection. Though not traditionally thought of as integral to the data analysis process, these factors have profound effects on the analysis process. This article will discuss the importance of these additional aspects, as well as statistical analysis and determination of biological significance of microarray data. A summary of currently available software options will also be presented with a focus on the aspects discussed.

Animals↗
PubMed2006

Genome-wide prediction of transcriptional regulatory elements of human promoters using gene expression and promoter analysis data.

BACKGROUND: A complete understanding of the regulatory mechanisms of gene expression is the next important issue of genomics. Many bioinformaticians have developed methods and algorithms for predicting transcriptional regulatory mechanisms from sequence, gene expression, and binding data. However, most of these studies involved the use of yeast which has much simpler regulatory networks than human and has many genome wide binding data and gene expression data under diverse conditions. Studies of genome wide transcriptional networks of human genomes currently lag behind those of yeast. RESULTS: We report herein a new method that combines gene expression data analysis with promoter analysis to infer transcriptional regulatory elements of human genes. The Z scores from the application of gene set analysis with gene sets of transcription factor binding sites (TFBSs) were successfully used to represent the activity of TFBSs in a given microarray data set. A significant correlation between the Z scores of gene sets of TFBSs and individual genes across multiple conditions permitted successful identification of many known human transcriptional regulatory elements of genes as well as the prediction of numerous putative TFBSs of many genes which will constitute a good starting point for further experiments. Using Z scores of gene sets of TFBSs produced better predictions than the use of mRNA levels of a transcription factor itself, suggesting that the Z scores of gene sets of TFBSs better represent diverse mechanisms for changing the activity of transcription factors in the cell. In addition, cis-regulatory modules, combinations of co-acting TFBSs, were readily identified by our analysis. CONCLUSION: By a strategic combination of gene set level analysis of gene expression data sets and promoter analysis, we were able to identify and predict many transcriptional regulatory elements of human genes. We conclude that this approach will aid in decoding some of the important transcriptional regulatory elements of human genes.

Algorithms↗
PubMed2005

LF-NMR and multivariate data analysis: compression of data to classify hydrogel contact lenses.

Hydrogel contact lenses swollen in viscoelastic artificial tears solution have been studied, measuring transversal relaxation times of water molecules using LF-NMR techniques. Data were processed by classical multiexponential fitting, by principal component analysis and by SLICING, a multi-way analysis method. The reason for using multivariate data analysis was not to obtain a better fitting, but rather more effective data description. The single-sample relaxation curves were projected in a space spanned by the loading curves, and in this space it was simpler to compare data. In particular, it contributed to the description of the variability of motion characteristics of the water molecule 'families' contained in the studied samples. Applying multivariate techniques, we were able to group lenses with different Equilibrium Water Content (EWC) and with the same water content but different compositions. Accordingly, we were able to point out that, if the lenses are swollen first in physiological solution and then in viscoelastic artificial tears solution, hydration characteristics remained unchanged in all the studied samples, except for 38% EWC lenses.

Algorithms↗
PubMed1986

A new insight into chemical mutagenesis by multivariate data analysis.

Computerized data analysis was applied to a genotoxicity data base, consisting of 42 chemicals assayed in 20 short-term mutagenicity tests. Factor and cluster analysis were used to elicit underlying patterns and to classify the chemicals in groups homogeneous for the kind of genetic damage induced. This analysis put in light a clear differentiation between effects in in vivo and in vitro systems, while the heuristic value of the traditional categories (such as point-mutation and chromosomal damage, or prokariotic and eukariotic systems) was not confirmed.

Humans↗
PubMed2004

Recovering genetic regulatory networks from micro-array data and location analysis data.

Learning large network (with hundreds of variables) is gaining interest of many researchers with the emergence of high-throughput biological data sources such as micro-array data. In this paper, we investigated the two popular large scale network structure learning algorithms, sparse candidate hill climbing (SCHC) and Grow-Shrinkage(GS) algorithm. The experiments show that in fact both of them have serious effectiveness problems when the number of variables(genes) is large compared to the number of instances(experimental conditions), which is a common case in micro-array data. We further propose a new large scale structure learning algorithm based on Lasso regression. Theoretical analysis in [10] suggested that the L1-norm in lasso regression could make our algorithm especially suitable in the cases that the number of variables and instances is unbalance. Our algorithm achieves much better results than SCHC and GS on the synthetic data. We also show the effectiveness of our algorithm by learning genetic regulatory network modules from a real micro-array data (with more than 6000 genes), combined with the genome-wide location analysis data. The learned results are consistent well with biological knowledge.

Algorithms↗
PubMed1991

Multivariate data analysis of NMR data.

Multivariate methods based on principal components (PCA and PLS) have been used to reduce NMR spectral information, to predict NMR parameters of complicated structures, and to relate shift data sets to dependent descriptors of biological significance. Noise reduction and elimination of instrumental artifacts are easily performed on 2D NMR data. Configurational classification of triterpenes and shift predictions in disubstituted benzenes can be obtained using PCA and PLS analysis. Finally, the shift predictions of tripeptides from descriptors of amino acids open the possibility of automatic analysis of multidimensional data of complex structures.

Amino Acid Sequence↗
PubMed2006

Down-regulation of the claudin-18 gene, identified through serial analysis of gene expression data analysis, in gastric cancer with an intestinal phenotype.

Gastric cancer (GC) is one of the most common malignancies worldwide. Genes whose expression is down-regulated in GC may be tumour suppressor genes. In the present study, genes with decreased expression in GC were screened for by serial analysis of gene expression (SAGE) data analysis and reverse transcription (RT)-polymerase chain reaction (PCR), and CLDN18 (encoding claudin-18) was identified. Quantitative RT-PCR revealed that expression of CLDN18 was down-regulated in 13 (56.5%) of 23 GCs. Immunostaining showed that normal gastric mucosa and Paneth cells of the duodenum expressed claudin-18 on cell membranes. Expression of claudin-18 was reduced in several intestinal metaplasias of the stomach. Of 20 samples of gastric adenoma, 18 (90.0%) showed decreased claudin-18 expression. Down-regulation of claudin-18 was observed in 84 of 146 GCs (57.5%) and correlated with poor survival in 65 advanced GCs (p = 0.0346). In addition, expression of the gastric and intestinal phenotypes of GC was examined by immunostaining for MUC5AC, MUC6, MUC2, and CD10. Of 38 GCs showing only the intestinal phenotype, down-regulation of claudin-18 was observed in 28 (73.7%), whereas in the remaining 108 GC cases, down-regulation of claudin-18 was observed in 56 (51.9%) (p = 0.0224). These results indicate that claudin-18 is a good marker of poor survival in GC. Down-regulation of claudin-18 may be involved in GCs with an intestinal phenotype, and may be an early event in gastric carcinogenesis.

Adenocarcinoma↗
PubMed1997

Toward the development of structured criteria for interpretation of functional analysis data.

Using functional analysis results to prescribe treatments is the preferred method for developing behavioral interventions. Little is known, however, about the reliability and validity of visual inspection for the interpretation of functional analysis data. The purpose of this investigation was to develop a set of structured criteria for visual inspection of multielement functional analyses that, when applied correctly, would increase interrater agreement and agreement with interpretations reached by expert consensus. In Study 1, 3 predoctoral interns interpreted functional analysis graphs, and interrater agreement was low (M = .46). In Study 2, 64 functional analysis graphs were interpreted by a panel of experts, and then a set of structured criteria were developed that yielded interpretive results similar to those of the panel (exact agreement = .94). In Study 3, the 3 predoctoral interns from Study 1 were trained to use the structured criteria, and the mean interrater agreement coefficient increased to .81. The results suggest that (a) the interpretation of functional analysis data may be less reliable than is generally assumed, (b) decision-making rules used by experts in the interpretation of functional analysis data can be operationalized, and (c) individuals can be trained to apply these rules accurately to increase interrater agreement. Potential uses of the criteria are discussed.

Humans↗
PubMed1997

Summary oral reflective analysis: a method for interview data analysis in feminist qualitative research.

This article explores an innovative approach to qualitative data analysis called Summary Oral Reflective Analysis (SORA). The method preserves the richness and contextuality of in-depth interview data within a broader feminist philosophical perspective. This multidisciplinary approach was developed in two individual research programs within a cooperative, collaborative arrangement. It represents a creative response to perceived deficiencies in the pragmatics of qualitative data analysis where the maintenance of data contextuality is critical.

Data Interpretation, Statistical↗
PubMed2004

Microarray Data Analysis Toolbox (MDAT): for normalization, adjustment and analysis of gene expression data.

SUMMARY: We introduce a novel Matlab toolbox for microarray data analysis. This toolbox uses normalization based upon a normally distributed background and differential gene expression based on five statistical measures. The objects in this toolbox are open source and can be implemented to suit your application. AVAILABILITY: MDAT v1.0 is a Matlab toolbox and requires Matlab to run. MDAT is freely available at http://microarray.omrf.org/publications/2004/knowlton/MDAT.zip.

Algorithms↗
PubMed2005

SpA: web-accessible spectratype analysis: data management, statistical analysis and visualization.

SUMMARY: SpA is a web-accessible system for the management, visualization and statistical analysis of T-cell receptor spectratype data. Users upload data from their spectratype analyzers to SpA, which saves the raw data and user-defined supplementary covariates to a secure database. The statistical engine performs several data analyses and statistical summaries. The visualization engine displays spectratype histograms in a Java applet and in an image file suitable for download. All of these results are also saved to the database and remain accessible to the user. Additional statistical tools specific to the analysis of multiple spectratypes are also available through the SpA interface. AVAILABILITY: The service is freely accessible via the web at http://www.duke.edu/~kepler/spa.html. Additional technical support and specialized statistical analysis and consultation are available by arrangement with the authors and, depending on the service requested, may be subject to fee.

Animals↗