The Normal Deviate Test of Phylogenetic Value of a Data Matrix

The principal component analysis has been applied to a data matrix formed by 7 usual substituent constants for 38 substituents. Three factors are able to explain 99.4% cumulative proportion of total variance. Several rotations have been carried out for the first two factors in order to obtain their physical meaning. The first factor is related to the resonance effect, whereas the second one expresses the inductive effect, and both together describe 97.5% cumulative proportion of total variance. Their mutual orthogonality does not directly follow from the rotations carried out. With the help of these factors the substituents are divided into four main classes, and some of them assume a special position.

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Additive-multiplicative description of extrathermodynamic relations: Application to solvent effect

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19900634 ◽

1990 ◽

Vol 55 (3) ◽

pp. 634-643 ◽

Cited By ~ 2

Author(s):

Oldřich Pytela

Keyword(s):

Solvent Effect ◽

Additive Model ◽

Multiplicative Model ◽

Data Matrix ◽

Statistical Parameters ◽

Model Application ◽

Mutual Correlation ◽

Linear Free Energy ◽

Cross Terms ◽

Energy Relationships

The paper is focused on evaluation of significance of the additive-multiplicative model of extrathermodynamic relations (linear free energy relationships) as compared with the additive model. Application of the method of conjugated deviations to a data matrix describing manifestations of solvent effects in 367 processes in solutions (6 334 data) has shown that introduction of cross-terms into the additive model is statistically significant for a model with two and particularly three parameters. At the same time the calculation has provided a new set of statistical parameters for description of solvent effect with application of the additive-multiplicative model. Compared with an analogous set designated for the additive model, the new parameters show a lower mutual correlation, retaining the same nature of the properties described, i.e. polarity-acidity (PAC parameter), polarity-basicity (PBC), and polarity-polarizability (PPC).

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Resolution of a protein sequence ambiguity by X-ray crystallographic and mass spectrometric methods

Journal of Applied Crystallography ◽

10.1107/s0021889891011986 ◽

1992 ◽

Vol 25 (2) ◽

pp. 205-210 ◽

Cited By ~ 8

Author(s):

L. J. Keefe ◽

E. E. Lattman ◽

C. Wolkow ◽

A. Woods ◽

M. Chevrier ◽

...

Keyword(s):

Mass Spectrometry ◽

Amino Acid ◽

Electron Density ◽

Mass Spectrometric ◽

Amino Acid Sequences ◽

Data Matrix ◽

Protein Crystal ◽

Insertion Mutant ◽

Laser Desorption Mass Spectrometry ◽

X Ray

Ambiguities in amino acid sequences are a potential problem in X-ray crystallographic studies of proteins. Amino acid side chains often cannot be reliably identified from the electron density. Many protein crystal structures that are now being solved are simple variants of a known wild-type structure. Thus, cloning artifacts or other untoward events can readily lead to cases in which the proposed sequence is not correct. An example is presented showing that mass spectrometry provides an excellent tool for analyzing suspected errors. The X-ray crystal structure of an insertion mutant of Staphylococcal nuclease has been solved to 1.67 Å resolution and refined to a crystallographic R value of 0.170 [Keefe & Lattman (1992). In preparation]. A single residue has been inserted in the C-terminal α helix. The inserted amino acid was believed to be an alanine residue, but the final electron density maps strongly indicated that a glycine had been inserted instead. To confirm the observations from the X-ray data, matrix-assisted laser desorption mass spectrometry was employed to verify the glycine insertion. This mass spectrometric technique has sufficient mass accuracy to detect the methyl group that distinguishes glycine from alanine and can be extended to the more common situation in which crystallographic measurements suggest a problem with the sequence, but cannot pinpoint its location or nature.

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ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

Bioinformatics ◽

10.1093/bioinformatics/btab179 ◽

2021 ◽

Author(s):

Irzam Sarfraz ◽

Muhammad Asif ◽

Joshua D Campbell

Keyword(s):

Single Cell ◽

R Package ◽

Poor Quality ◽

Data Matrix ◽

Supplementary Information ◽

Data Provenance ◽

Rna Seq ◽

Efficient Management ◽

The Matrix ◽

The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

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An Empirical Bayes approach for the identification of long-range chromosomal interaction from Hi-C data

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2020-0026 ◽

2021 ◽

Vol 20 (1) ◽

pp. 1-15

Author(s):

Qi Zhang ◽

Zheng Xu ◽

Yutong Lai

Keyword(s):

Long Range ◽

Latent Variables ◽

Empirical Bayes ◽

Genome Structure ◽

Peak Detection ◽

Data Matrix ◽

Dispersion Modeling ◽

Empirical Bayes Approach ◽

Over Dispersion ◽

Bayes Approach

Abstract Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e., peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the “true” interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a Smoothed Expectation Maximization algorithm, and the empirical null based on the zero assumption. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can identify better peaks in terms of accuracy, biological interpretability, and the consistency across biological replicates. The source code is available on Github (https://github.com/QiZhangStat/EBHiC).

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Health Sciences Students’ Perceptions of the Role of the Supervisor in Clinical Placements

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18094427 ◽

2021 ◽

Vol 18 (9) ◽

pp. 4427

Author(s):

Álvaro Borrallo-Riego ◽

Eleonora Magni ◽

Juan Antonio Jiménez-Álvarez ◽

Vicente Fernández-Rodríguez ◽

María Dolores Guerra-Martín

Keyword(s):

Professional Training ◽

Learning Experience ◽

Health Sciences ◽

Data Matrix ◽

Quantitative Methodology ◽

Clinical Placements ◽

Validated Questionnaire ◽

Significant Difference ◽

Health Sciences Students

The supervision of clinical placements is essential to achieving a positive learning experience in the clinical setting and which supports the professional training of those being supervised. The aim of this study was to explore health sciences students’ perceptions of the role of the supervisor in the supervision of clinical placements. A quantitative methodology was used, administering a previously validated questionnaire, by means of an expert panel and a pre-test, to 134 students from the Faculty of Nursing, Physiotherapy and Podiatry at the University of Seville (Spain). The analysis of variables was carried out by means of a data matrix. The results revealed a statistically significant difference in the perception of placement supervision depending on the degree, with Nursing producing the highest degree of affirmation in the variables studied and the greatest satisfaction with placement supervision; in contrast, Physiotherapy produced the greatest dissatisfaction and the lowest degree of affirmation. The study and analysis of these perceptions facilitates the collection of relevant information in order to formulate actions that help to improve the supervision experience during placements. They also allow a greater understanding of what factors most influence the experience of supervision during clinical placements.

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Nuclear Magnetic Resonance Metabolomics with Double Pulsed-Field-Gradient Echo and Automatized Solvent Suppression Spectroscopy for Multivariate Data Matrix Applied in Novel Wine and Juice Discriminant Analysis

Molecules ◽

10.3390/molecules26144146 ◽

2021 ◽

Vol 26 (14) ◽

pp. 4146

Author(s):

José Enrique Herbert-Pucheta ◽

José Daniel Lozada-Ramírez ◽

Ana E. Ortega-Regules ◽

Luis Ricardo Hernández ◽

Cecilia Anaya de Parrodi

Keyword(s):

Statistical Analysis ◽

Discriminant Analysis ◽

Field Gradient ◽

Multivariate Statistical Analysis ◽

Data Matrix ◽

Cabernet Sauvignon ◽

Gradient Echo ◽

Multivariate Statistical ◽

Orthogonal Projections ◽

Pulsed Field

The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad 1H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches.

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Kernel weighted least square approach for imputing missing values of metabolomics data

Scientific Reports ◽

10.1038/s41598-021-90654-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nishith Kumar ◽

Md. Aminul Hoque ◽

Masahiro Sugimoto

Keyword(s):

Missing Data ◽

Large Scale ◽

Missing Values ◽

Kernel Weight ◽

Least Square ◽

Data Matrix ◽

Data Imputation ◽

Metabolomics Data ◽

Missing Value ◽

Missing Data Imputation

AbstractMass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA.

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