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At least 19 recordsLinked to original sources
PubMed1996

Exploratory data analysis: data visualization or torture?

Exploratory Data Analysis offers a set of graphical and statistical tools to find the full meaning from data sets. The user visualizes, analyzes, and transforms data distributions with these tools. Graphs reveal relationships between variables; the residuals left after fitting data show the adequacy of the model. Without this careful examination and understanding of the data, rote data analysis using standard statistical tests can give misleading results. Exploratory Data Analysis has its own set of pitfalls and must be used with confirmatory statistics and studies. Increasing power and resolution in personal computers enables modern statistical software to make these methods widely accessible. By easily moving between data and their graphic representation, analysis can be comprehensive without being tedious. Exploratory Data Analysis can add an exciting and useful tool to the epidemiologist's repertoire. This article illustrates several tools from an evolving list.

Computer Graphics↗
PubMed2000

Transformation in the PC-aided biochemical data analysis.

Data transformations enable expression of original data in a new scale, more suitable for data analysis. In computer-aided interactive analysis of biochemical and clinical data an exploratory data analysis often finds that the sample distribution is systematically skewed or does not accept a sample homogeneity. Under such circumstances the original data should be transformed. The power transformation and the Box-Cox transformation improve sample symmetry and also stabilize variance. Both the Hines-Hines selection graph and the plot of logarithm of a maximum likelihood function allow selection of an optimum transformation parameter. The proposed procedure of data transformation in univariate data analysis is illustrated on a determination of 17-hydroxypregnenolone in umbilical blood of a population of newborns. Lower levels of free 5-ene steroids in umbilical blood and elevated levels of 5-ene steroid sulfates indicate a congenital sex-specific placental sulfatase insufficiency. After examination of statistical assumptions by diagnostic plots of an exploratory data analysis the best estimate of a mean value of 17-hydroxypregnenolone is derived.

17-alpha-Hydroxypregnenolone↗
PubMed1999

Application of multivariate data analysis methods to comparative molecular field analysis (CoMFA) data: proton affinities and pKa prediction for nucleic acids components.

Multivariate data analysis methods (Principal Component Analysis (PCA) and Partial Least Squares (PLS)) are applied to the analysis of the CoMFA (Comparative Molecular Field Analysis) data for several nucleic acids components. The data set includes nitrogenated bases, nucleosides, linear nucleotides, 3', 5'-cyclic nucleotides and oligonucleotides. PCA is applied to study the structure of the CoMFA data and to detect possible outliers in the data set. PLS is applied to correlate the CoMFA data with either calculated AM1 proton affinities or with experimental pKa values. The possibility of making a prediction of pKa values directly from 3D structures of the monomers for polynucleotides is also shown. The influence of the superposition criteria and of conformational changes along the glycosidic bond on the pKa prediction are studied as well.

Glycosylation↗
PubMed1993

Laboratory information system for nuclear magnetic resonance relaxometry in biomedical materials (NMR LIS). II. Data analysis.

Data analysis modules for Nuclear Magnetic Resonance Relaxometry LIS are described. Since the experimental NMR results from biomedical samples often exhibit complex structure that arises from complexity of tissue, the analysis methods focus on search, investigation, and evaluation of complex multiexponential relaxation features. Two algorithms have been implemented: an enhanced spin grouping method that is based on a multiexponential curve stripping algorithm, and an eigenvalue decomposition method, implemented as modified DISCRETE program.

Algorithms↗
PubMed1994

Risk factors associated with having a pressure ulcer: a secondary data analysis.

Data obtained through five hospital-wide pressure ulcer audits were pooled for exploratory analysis. The purpose of this secondary data analysis was (1) to delineate the association of various risk factors and the presence of pressure ulcers and (2) to generate specific, testable hypotheses. Risk factors studied included fecal incontinence, urinary incontinence, malnutrition, impaired mobility, decreased mental status, diabetes mellitus, peripheral vascular disease, spinal cord injury, multiple sclerosis, and metastatic carcinoma. Stepwise logistic regression resulted in a best-fitting model that included fecal incontinence, impaired mobility, malnutrition, decreased mental status, and an interaction effect between fecal incontinence and impaired mobility. This model suggested that the odds of having a pressure ulcer were 22 times greater for hospitalized adult patients with fecal incontinence compared to hospitalized adult patients without fecal incontinence.

Adolescent↗
PubMed2004

[Necessity and usefulness of bioinformatic methods for microarray data analysis].

Data emerging from DNA microarray experiments are usually difficult to interpret. While the level of expression of several thousand genes can be measured in a single experiment, only a few dozen experiments are normally carried out, leading to data sets of very high dimensionality and low cardinality. The computational analysis of gene expression data makes significant usage of machine learning and statistical methods. Nevertheless, caution should be used in the blind adoption of these methods, as this usually leads to an over-interpretation of the expression profiles. The following presentation provides an overview of up-to-date principles of biostatistical analysis. A potential application for the analysis of high-dimensional expression profiles of prostate cancer is given.

Chromosome Aberrations↗
PubMed1992

Tentative reference values for gold, silver and platinum: literature data analysis.

Data available on biologic fluid content of therapeutic metals are uneven for silver, gold and platinum respectively. Tentative reference values may be proposed on the basis of the most representative studies for silver and gold. For silver it is suggested that the variability in normal subjects could range up to 10 micrograms/l in whole blood and up to 1 microgram/l in urine. For gold 0.5 microgram/l can be considered the upper limit for both whole blood and urine. For platinum there is no indication that concentration in either blood or urine could reach detectable amounts in normal subjects.

Body Fluids↗
PubMed1984

The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods.

Population pharmacokinetics describe the typical relationships between physiology (both normal and disease altered) and pharmacokinetics, the interindividual variability in these relationships, and their residual intraindividual variability. Knowledge of population kinetics can help one to choose initial drug dosage, to modify dosage appropriately in response to observed drug levels, to make rational decisions regarding certain aspects of drug regulation, and to investigate and elucidate certain research questions in pharmacokinetics. Experimental data from which population kinetics might be estimated often come from only those few individuals both willing and available to be studied. Clinical data from patients undergoing care might be more representative. These data, however, are marked by varying quality, accuracy, and precision, as well as there being few data per patient. Population pharmacokinetic parameters have traditionally been estimated either by fitting all individuals' data together as though there were no individual kinetic differences [the naive pooled data (NPD) approach], or by fitting each individual's data separately and then combining the individual parameter estimates [the two-stage (TS) approach]. These methods have certain theoretical problems which can only be aggravated when the deficiencies of data typical of clinical data are present. In this paper, the standard approaches are discussed and illustrated (using nondeficient data) in order to introduce subsequent papers in which alternative data analysis methods for population parameter estimation are defined, discussed, and compared.

Humans↗
PubMed1995

Classmode: a new data format for real-time multiparameter data analysis and data compression.

A new data acquisition and analysis format (classmode) was developed that allows real-time data classification in a flow cytometer. In our cytometer, detected events were classified in real time by their presence or absence in a set of look-up tables (LUT). A modification of the cytometer hardware allows the exclusive transfer of the LUT data to the acquisition/storage computer. Using a combination of 8 LUTs, the analyzed events can be classified into 256 subpopulations. Real-time data classification results in an increased data transfer rate and a significant compression of the data.

Cells, Cultured↗
PubMed2006

HBM functional imaging analysis contest data analysis in wavelet space.

An analysis of the Functional Imaging Analysis Contest (FIAC) data is presented using spatial wavelet processing. This technique allows the image to be filtered adaptively according to the data itself, rather than relying on a predetermined filter. This adaptive filtering leads to better estimation of the parameters and contrasts in terms of mean squared error. It will be shown that by introducing a slight bias into the estimation, a large reduction in the variance can be achieved, leading to better overall mean squared error estimates. As no single filter needs to be preselected, results containing many scales of information can be found. In the FIAC data, it is shown that both small-scale and large-scale (smoother, more dispersed) effects occur. The combination of small- and large-scale effects detected in the FIAC data would be easy to miss using conventional single filter analysis.

Algorithms↗
PubMed2006

Changes in cod muscle proteins during frozen storage revealed by proteome analysis and multivariate data analysis.

Multivariate data analysis has been combined with proteomics to enhance the recovery of information from 2-DE of cod muscle proteins during different storage conditions. Proteins were extracted according to 11 different storage conditions and samples were resolved by 2-DE. Data generated by 2-DE was subjected to principal component analysis (PCA) and discriminant partial least squares regression (DPLSR). Applying PCA to 2-DE data revealed the samples to form groups according to frozen storage time, whereas differences due to different storage temperatures or chilled storage in modified atmosphere packing did not lead to distinct changes in protein pattern. Applying DPLSR to the 2-DE data enabled the selection of protein spots critical for differentiation between 3 and 6 months frozen storage with 12 months frozen storage. Some of these protein spots have been identified by MS/MS, revealing myosin light chain 1, 2 and 3, triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, aldolase A and two alpha-actin fragments, and a nuclease diphosphate kinase B fragment to change in concentration, during frozen storage. Application of proteomics, multivariate data analysis and MS/MS to analyse protein changes in cod muscle proteins during storage has revealed new knowledge on the issue and enables a better understanding of biochemical processes occurring.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2026

A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses.

Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving the two-dimensional spatial context of mRNA molecules within tissue sections, enabling studies of tissue architecture and microenvironment-associated biology. However, ST analysis remains challenging because data import, quality control, integration, deconvolution, spatial statistics, and visualization often require multiple software environments and reproducible parameter choices. This protocol presents a practical computational workflow for public ST datasets in R, beginning with data acquisition and software setup and proceeding through Seurat-based data loading, quality control, normalization, multi-sample integration, clustering, and spatially variable gene analysis. The workflow then applies complementary deconvolution strategies, including reference-guided SPOTlight analysis and unsupervised STdeconvolve topic modeling, followed by Giotto-based spatial cell-cell communication analysis and interactive region-of-interest (ROI) selection using a custom Python Dash application. By emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints, the protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation.

Spatial Transcriptomics↗
PubMed2007

Multivariate data analysis of proteome data.

We present the background for multivariate data analysis on proteomics data with a hands-on section on how to transfer data between different software packages. The techniques can also be used for other biological and biochemical problems in which structures have to be found in a large amount of data. Digitalization of the 2D gels, analysis using image processing software, transfer of data, multivariate data analysis, interpretation of the results, and finally we return to biology.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2005

Qualitative data analysis using data displays.

The amount of data generated in qualitative research can be difficult to manage. In this paper Tracey Williamson and Andrew Long discuss how the use of data displays can improve data management and also how the process can help to make the routes from raw data to research findings in qualitative research more transparent. Data displays can take several forms but share the benefit of helping to condense large amounts of data into more manageable forms. They can also help to convey information in a visually stimulating format where presentation time or column space may be limited.

Data Display↗
PubMed1977

The molecular function of hemoglobin as reflected in ligand binding data: analysis of data on erythrocytes.

Hemoglobin oxygen binding data on erythrocytes at diffrent pH, PCO2 and bisphosphoglycerate concentrations have been analyzed in terms of an extended version of the Herzfeld-Stanley model of 1972. The binding of oxygen to subunits when the tetramer is in the quaternary oxy conformation was found to be insensitive to moderate changes in pH and pCO2 (0.71 +/- 0.05 mm Hg-1). Utilizing this circumstance it has been possible to obtain, for the first time, unique estimates of energy parameters related to hemoglobin cooperativity and effector action. At 37 degrees C, pH 7.2 and pCO2 22 mm Hg the following parameter values were obtained: The allosteric constant: (1.5 + 0.4)-10(4); the oxygen binding constant of the deoxy state: (5.4 +/- 0.3).10(-3) mm Hg-1; the 2,3-bisphosphoglycerate binding constants: (3.3 +/- 1.3).10(3)1. mol-1(deoxy), (1.3 +/- 0.5).10(2)1. mol-1 (oxy). Quarternary transition most likely takes place after binding of the second O2 molecule. Following the concepts of Perutz the results suggest that (1) protons and carbon dioxide act as constraint effectors and/or as quaternary effectors; (2) the difference in total conformational energy between the two quaternary ligand-free states is almost exclusively confined to molecular constraints and very little to the difference in quaternary conformational energy. The consistency of the results indicate that the model may be regarded as a useful tool for the description of the functional interrelations in the hemoglobin oxygenation process as reflected in oxygen binding data.

Binding Sites↗