Assessing the precision of a detergent-assisted cartridge precipitation workflow for non-targeted quantitative proteomics.

Detergent-based workflows incorporating sodium dodecyl sulfate (SDS) necessitate additional steps for detergent removal ahead of mass spectrometry (MS). These steps may lead to variable protein recovery, inconsistent enzyme digestion efficiency, and unreliable MS signals. To validate a detergent-based workflow for quantitative proteomics, we herein evaluate the precision of a bottom-up sample preparation strategy incorporating cartridge-based protein precipitation with organic solvent to deplete SDS.

Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods.

To address the growing concern of honey adulteration in Canada and globally, a quantitative NMR method was developed to analyze 424 honey samples collected across Canada as part of two surveys in 2018 and 2019 led by the Canadian Food Inspection Agency. Based on a robust and reproducible methodology, NMR data were recorded in triplicate on a 700 MHz NMR spectrometer equipped with a cryoprobe, and the data analysis led to the identification and quantification of 33 compounds characteristic of the chemical composition of honey. The high proportion of Canadian honey in the library provided a unique opportunity to apply multivariate statistical methods including PCA, PLS-DA, and SIMCA in order to differentiate Canadian samples from the rest of the world.

Coupling of multivariate curve resolution-alternating least squares and mechanistic hard models to investigate antibody purification from human plasma using ion exchange chromatography.

A two steps proposal for the purification of immunoglobulin G from human blood plasma is investigated. The first step is precipitation using cold ethanol based on the Cohn method with some modification and the second step is a chromatographic separation by DEAE-Sepharose FF resin as a weak anion exchanger. The presence of interferent in the region3 of chromatographic fractions, which is co-eluted with IgG, restricts the application of the mechanistic chromatography model.

Beyond principal components: a critical comparison of factor analysis methods for subspace modelling in chemistry.

Multivariate data analysis tools have become an integral part of modern analytical chemistry, and principal component analysis (PCA) is perhaps foremost among these. PCA is central in approaching many problems in data exploration, classification, calibration, modelling, and curve resolution. However, PCA is only one form of a broader group of factor analysis (FA) methods that are rarely employed by chemists.

Sparse Projection Pursuit Analysis: An Alternative for Exploring Multivariate Chemical Data.

Sparse projection pursuit analysis (SPPA), a new approach for the unsupervised exploration of high-dimensional chemical data, is proposed as an alternative to traditional exploratory methods such as principal components analysis (PCA) and hierarchical cluster analysis (HCA). Where traditional methods use variance and distance metrics for data compression and visualization, the proposed method incorporates the fourth statistical moment (kurtosis) to access interesting subspaces that can clarify relationships within complex data sets. The quasi-power algorithm used for projection pursuit is coupled with a genetic algorithm for variable selection to efficiently generate sparse projection vectors that improve the chemical interpretability of the results while at the same time mitigating the problem of overmodeling.

Chemical Barcoding: A Nuclear-Magnetic-Resonance-Based Approach To Ensure the Quality and Safety of Natural Ingredients.

One of the greatest challenges facing the functional food and natural health product (NHP) industries is sourcing high-quality, functional, natural ingredients for their finished products. Unfortunately, the lack of ingredient standards, modernized analytical methodologies, and industry oversight creates the potential for low quality and, in some cases, deliberate adulteration of ingredients. By exploring a diverse library of NHPs provided by the independent certification organization ISURA, we demonstrated that nuclear magnetic resonance (NMR) spectroscopy provides an innovative solution to authenticate botanicals and warrant the quality and safety of processed foods and manufactured functional ingredients.

Improved modeling of multivariate measurement errors based on the Wishart distribution.

The error covariance matrix (ECM) is an important tool for characterizing the errors from multivariate measurements, representing both the variance and covariance in the errors across multiple channels. Such information is useful in understanding and minimizing sources of experimental error and in the selection of optimal data analysis procedures. Experimental ECMs, normally obtained through replication, are inherently noisy, inconvenient to obtain, and offer limited interpretability.

Generalized error-dependent prediction uncertainty in multivariate calibration.

Most of the current expressions used to calculate figures of merit in multivariate calibration have been derived assuming independent and identically distributed (iid) measurement errors. However, it is well known that this condition is not always valid for real data sets, where the existence of many external factors can lead to correlated and/or heteroscedastic noise structures. In this report, the influence of the deviations from the classical iid paradigm is analyzed in the context of error propagation theory.

Procrustes rotation as a diagnostic tool for projection pursuit analysis.

Projection pursuit (PP) is an effective exploratory data analysis tool because it optimizes the projection of high dimensional data using distributional characteristics rather than variance or distance metrics. The recent development of fast and simple PP algorithms based on minimization of kurtosis for clustering data has made this powerful tool more accessible, but under conditions where the sample-to-variable ratio is small, PP fails due to opportunistic overfitting of random correlations to limiting distributional targets. Therefore, some kind of variable compression or data regularization is required in these cases.

Characterization of heteroscedastic measurement noise in the absence of replicates.

A method is described for the characterization of measurement errors with non-uniform variance (heteroscedastic noise) in contiguous signal vectors (e.g., spectra, chromatograms) that does not require the use of replicated measurements.

Chromatographic behaviour of peptides following dimethylation with H2/D2-formaldehyde: implications for comparative proteomics.

The differential separation of deuterated and non-deuterated forms of isotopically substituted compounds in chromatography is a well-known but not well-understood phenomenon. This separation is relevant in comparative proteomics, where stable isotopes are used for differential labelling and the effect of isotope resolution on quantitation has been used to disqualify some deuterium labelling methods in favour of heavier isotopes. In this work, a detailed evaluation of the extent of isotopic separation and its impact on quantitation was performed for peptides labelled through dimethylation with H(2)/D(2) formaldehyde.

Preliminary exploration of time course DNA microarray data with correlation maps.

In the analysis of data from high-throughput experiments, information regarding the underlying data structure provides the researcher with confidence in the appropriateness of various analysis methods. One extremely simple but powerful data visualization method is the correlation heat map, whereby correlations between experiments/conditions are calculated and represented using color. In this work, the use of correlation maps to shed light on transcription patterns from DNA microarray time course data prior to gene-level analysis is described.

Potential bias in GO::TermFinder.

The increased need for multiple statistical comparisons under conditions of non-independence in bioinformatics applications, such as DNA microarray data analysis, has led to the development of alternatives to the conventional Bonferroni correction for adjusting P-values. The use of the false discovery rate (FDR), in particular, has grown considerably. However, the calculation of the FDR frequently depends on drawing random samples from a population, and inappropriate sampling will result in a bias in the calculated FDR.

Characterization of the measurement error structure in 1D 1H NMR data for metabolomics studies.

NMR-based metabolomics is characterized by high throughput measurements of the signal intensities of complex mixtures of metabolites in biological samples by assaying, typically, bio-fluids or tissue homogenates. The ultimate goal is to obtain relevant biological information regarding the dissimilarity in patho-physiological conditions that the samples experience. For a long time now, this information has been obtained through the analysis of measured NMR signals via multivariate statistics.

Bootstrap method for the estimation of measurement uncertainty in spotted dual-color DNA microarrays.

DNA microarrays permit the measurement of gene expression across the entire genome of an organism, but the quality of the thousands of measurements is highly variable. For spotted dual-color microarrays the situation is complicated by the use of ratio measurements. Studies have shown that measurement errors can be described by multiplicative and additive terms, with the latter dominating for low-intensity measurements.

Methods for estimating and mitigating errors in spotted, dual-color DNA microarrays.

The conceptual simplicity of DNA microarray technology often belies the complex nature of the measurement errors inherent in the methodology. As the technology has developed, the importance of understanding the sources of uncertainty in the measurements and developing ways to control their influence on the conclusions drawn has become apparent. In this review, strategies for modeling measurement errors and minimizing their effect on the outcome of experiments using a variety of techniques are discussed in the context of spotted, dual-color microarrays.

Multivariate curve resolution of time course microarray data.

Background: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data.

An automated, pressure-driven sampling device for harvesting from liquid cultures for genomic and biochemical analyses.

Here we describe an automated, pressure-driven, sampling device for harvesting 10 to 30 ml samples, in replicate, with intervals as short as 10 s. Correlation between biological replicate time courses measured by microarrays was extremely high. The sampler enables sampling at intervals within the range of many important biological processes.

DNA microarrays: is there a role for analytical chemistry?

DNA microarrays, or "DNA chips", represent a relatively new technology that is having a profound impact on biology and medicine, yet analytical research into this area is somewhat sparse. This article presents an overview of DNA microarrays and their application to gene expression analysis from the perspective of analytical chemistry, treating aspects of array platforms, measurement, image analysis, experimental design, normalization, and data analysis. Typical approaches are described and unresolved issues are discussed, with a view to identifying some of the contributions that might be made by analytical chemists.

Genomic analysis of stationary-phase and exit in Saccharomyces cerevisiae: gene expression and identification of novel essential genes.

Most cells on earth exist in a quiescent state. In yeast, quiescence is induced by carbon starvation, and exit occurs when a carbon source becomes available. To understand how cells survive in, and exit from this state, mRNA abundance was examined using oligonucleotide-based microarrays and quantitative reverse transcription-polymerase chain reaction.

Maximum likelihood principal components regression on wavelet-compressed data.

Maximum likelihood principal component regression (MLPCR) is an errors-in-variables method used to accommodate measurement error information when building multivariate calibration models. A hindrance of MLPCR has been the substantial demand on computational resources sometimes made by the algorithm, especially for certain types of error structures. Operations on these large matrices are memory intensive and time consuming, especially when techniques such as cross-validation are used.