A quick and effective multivariate statistical strategy for imaging mass spectrometry

A new multivariate statistical strategy for analyzing large datasets that are produced by imaging mass spectrometry（IMS） techniques is reported.The strategy divides the whole datacube of the sample into several subsets and analyses them one by one to obtain the results.Instead of analyzing the whole datacube at one time,the strategy makes the analysis easier and decreases the computation time greatly.In this report,the IMS data are produced by the air flow-assisted ionization IMS（AFAI-IMS）.The strategy can be used in combination with most multivariate statistical analysis methods.In this paper,the strategy was combined with the principal component analysis（PCA） and partial least square analysis（PLS）.It was proven to be effective by analyzing the handwriting sample.By using the strategy,the m/z corresponding to the specific lipids in rat brain tissue were distinguished successfully.Moreover the analysis time grew linearly instead of exponentially as the size of sample increased.The strategy developed in this study has enormous potential for searching for the mjz of potential biomarkers quickly and effectively.     

Fourier transform infrared imaging analysis in discrimination studies of squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) of the oral cavity and oropharynx represents more than 95% of all malignant neoplasms in the oral cavity. Histomorphological evaluation of this cancer type is invasive and remains a time consuming and subjective technique. Therefore, novel approaches for histological recognition are necessary to identify malignancy at an early stage. Fourier transform infrared (FTIR) imaging has become an essential tool for the detection and characterization of the molecular components of biological processes, such as those responsible for the dynamic properties of tumor progression. FTIR imaging is a modern analytical technique enabling molecular imaging of a complex biological sample and is based on the absorption of IR radiation by vibrational transitions in covalent bonds. One major advantage of this technique is the acquisition of local molecular expression profiles, while maintaining the topographic integrity of the tissue and avoiding time-consuming extraction, purification, and separation steps. With this imaging technique, it is possible to obtain unique images of the spatial distribution of proteins, lipids, carbohydrates, cholesterols, nucleic acids, phospholipids, and small molecules with high spatial resolution. Analysis and visualization of FTIR imaging datasets are challenging and the use of chemometric tools is crucial in order to take advantage of the full measurement. Therefore, methodologies for this task based on the novel developed algorithm for multivariate image analysis (MIA) are often necessary. In the present study, FTIR imaging and data analysis methods were combined to optimize the tissue measurement mode after deparaffinization and subsequent data evaluation (univariate analysis and MIAs). We demonstrate that it is possible to collect excellent IR spectra from formalin-fixed paraffin-embedded (FFPE) tissue microarrays (TMAs) of OSCC tissue sections employing an optimised analytical protocol. The correlation of FTIR imaging to the morphological tissue features obtained by histological staining of the sections demonstrated that many histomorphological tissue patterns can be visualized in the colour images. The different algorithms used for MIAs of FTIR imaging data dramatically increased the information content of the IR images from squamous cell tissue sections. These findings indicate that intra-operative and surgical specimens of squamous cell carcinoma tissue can be characterized by FTIR imaging.     

Efficient encoding and rapid decoding for interactive visualization of large three‐dimensional hyperspectral chemical images

Interactive visualization of data from a new generation of chemical imaging systems requires coding that is efficient and accessible. New technologies for secondary ion mass spectrometry (SIMS) generate large three‐dimensional, hyperspectral datasets with high spatial and spectral resolution. Interactive visualization is important for chemical analysis, but the raw dataset size exceeds the memory capacities of typical current computer systems and is a significant obstacle. This paper reports the development of a lossless coding method that is memory efficient, enabling large SIMS datasets to be held in fast memory, and supports quick access for interactive visualization. The approach provides pixel indexing, as required for chemical imaging applications, and is based on the statistical characteristics of the data. The method uses differential time‐of‐flight to effect mass‐spectral run‐length‐encoding and uses a scheme for variable‐length, byte‐unit representations for both mass‐spectral time‐of‐flight and intensity values. Experiments demonstrate high compression rates and fast access. Copyright © 2009 John Wiley & Sons, Ltd.     

Multivariate data analysis for Raman imaging of a model pharmaceutical tablet

Spectroscopic imaging techniques provide spatial and spectral information about a sample simultaneously and are finding ever-increasing application in the pharmaceutical industry. Effective extraction of chemical information from imaging data sets is a crucial step during the application of imaging techniques. Multivariate imaging data analysis methods have been reported but few applications of these methods for pharmaceutical samples have been demonstrated. In this study, a bilayer model tablet consisting of avicel, lactose, sodium benzoate, magnesium stearate and red dye was prepared using custom press tooling, and Raman mapping data were collected from a 400 μm × 400 μm area of the tablet surface. Several representative multivariate methods were selected and used in the analysis of the data. Multivariate data analysis methods investigated include principal component analysis (PCA), cluster analysis, direct classical least squares (DCLS) and multivariate curve resolution (MCR). The relative merits and drawbacks of each technique for this application were evaluated. In addition, some practical issues associated with the use of these methods were addressed including data preprocessing, determination of the optimal number of clusters in cluster analysis and the optimization of window size in second derivative calculation.     

Potential applications of functional data analysis in chemometrics

In spectroscopy the measured spectra are typically plotted as a function of the wavelength (or wavenumber), but analysed with multivariate data analysis techniques (multiple linear regression (MLR), principal components regression (PCR), partial least squares (PLS)) which consider the spectrum as a set of m different variables. From a physical point of view it could be more informative to describe the spectrum as a function rather than as a set of points, hereby taking into account the physical background of the spectrum, being a sum of absorption peaks for the different chemical components, where the absorbance at two wavelengths close to each other is highly correlated. In a first part of this contribution, a motivating example for this functional approach is given. In a second part, the potential of functional data analysis is discussed in the field of chemometrics and compared to the ubiquitous PLS regression technique using two practical data sets. It is shown that for spectral data, the use of B-splines proves to be an appealing basis to accurately describe the data. By applying both functional data analysis and PLS on the data sets the predictive ability of functional data analysis is found to be comparable to that of PLS. Moreover, many chemometric datasets have some specific structure (e.g. replicate measurements, on the same object or objects that are grouped), but the structure is often removed before analysis (e.g. by averaging the replicates). In the second part of this contribution, we suggest a method to adapt traditional analysis of variance (ANOVA) methods to datasets with spectroscopic data. In particular, the possibilities to explore and interpret sources of variation, such as variations in sample and ambient temperature, are examined. Copyright © 2008 John Wiley & Sons, Ltd.     

Accounting for Poisson noise in the multivariate analysis of ToF‐SIMS spectrum images

Recent years have seen the introduction of many surface characterization instruments and other spectral imaging systems that are capable of generating data in truly prodigious quantities. The challenge faced by the analyst, then, is to extract the essential chemical information from this overwhelming volume of spectral data. Multivariate statistical techniques such as principal component analysis (PCA) and other forms of factor analysis promise to be among the most important and powerful tools for accomplishing this task. In order to benefit fully from multivariate methods, the nature of the noise specific to each measurement technique must be taken into account. For spectroscopic techniques that rely upon counting particles (photons, electrons, etc.), the observed noise is typically dominated by ‘counting statistics’ and is Poisson in nature. This implies that the absolute uncertainty in any given data point is not constant, rather, it increases with the number of counts represented by that point. Performing PCA, for instance, directly on the raw data leads to less than satisfactory results in such cases. This paper will present a simple method for weighting the data to account for Poisson noise. Using a simple time‐of‐flight secondary ion mass spectrometry spectrum image as an example, it will be demonstrated that PCA, when applied to the weighted data, leads to results that are more interpretable, provide greater noise rejection and are more robust than standard PCA. The weighting presented here is also shown to be an optimal approach to scaling data as a pretreatment prior to multivariate statistical analysis. Published in 2004 by John Wiley & Sons, Ltd.     

Pre-processing of ultraviolet resonance Raman spectra

The application of UV excitation sources coupled with resonance Raman have the potential to offer information unavailable with the current inventory of commonly used structural techniques including X-ray, NMR and IR analysis. However, for ultraviolet resonance Raman (UVRR) spectroscopy to become a mainstream method for the determination of protein secondary structure content and monitoring protein dynamics, the application of multivariate data analysis methodologies must be made routine. Typically, the application of higher order data analysis methods requires robust pre-processing methods in order to standardize the data arrays. The application of such methods can be problematic in UVRR datasets due to spectral shifts arising from day-to-day fluctuations in the instrument response. Additionally, the non-linear increases in spectral resolution in wavenumbers (increasing spectral data points for the same spectral region) that results from increasing excitation wavelengths can make the alignment of multi-excitation datasets problematic. Last, a uniform and standardized methodology for the subtraction of the water band has also been a systematic issue for multivariate data analysis as the water band overlaps the amide I mode. Here we present a two-pronged preprocessing approach using correlation optimized warping (COW) to alleviate spectra-to-spectra and day-to-day alignment errors coupled with a method whereby the relative intensity of the water band is determined through a least-squares determination of the signal intensity between 1750 and 1900 cm(-1) to make complex multi-excitation datasets more homogeneous and usable with multivariate analysis methods.     

Multivariate analysis of extremely large ToFSIMS imaging datasets by a rapid PCA method

Principal component analysis (PCA) and other multivariate analysis methods have been used increasingly to analyse and understand depth profiles in X‐ray photoelectron spectroscopy (XPS), Auger electron spectroscopy (AES) and secondary ion mass spectrometry (SIMS). These methods have proved equally useful in fundamental studies as in applied work where speed of interpretation is very valuable. Until now these methods have been difficult to apply to very large datasets such as spectra associated with 2D images or 3D depth‐profiles. Existing algorithms for computing PCA matrices have been either too slow or demanded more memory than is available on desktop PCs. This often forces analysts to ‘bin’ spectra on much more coarse a grid than they would like, perhaps even to unity mass bins even though much higher resolution is available, or select only part of an image for PCA analysis, even though PCA of the full data would be preferred. We apply the new ‘random vectors’ method of singular value decomposition proposed by Halko and co‐authors to time‐of‐flight (ToF)SIMS data for the first time. This increases the speed of calculation by a factor of several hundred, making PCA of these datasets practical on desktop PCs for the first time. For large images or 3D depth profiles we have implemented a version of this algorithm which minimises memory needs, so that even datasets too large to store in memory can be processed into PCA results on an ordinary PC with a few gigabytes of memory in a few hours. We present results from ToFSIMS imaging of a citrate crystal and a basalt rock sample, the largest of which is 134GB in file size corresponding to 67 111 mass values at each of 512 × 512 pixels. This was processed into 100 PCA components in six hours on a conventional Windows desktop PC. © 2015 The Authors. Surface and Interface Analysis published by John Wiley & Sons Ltd.     

A wavelet‐PCA method saves high mass resolution information in data treatment of SIMS molecular depth profiles

A detailed depth characterization of multilayered polymeric systems is a very attractive topic. Currently, the use of cluster primary ion beams in time‐of‐flight secondary ion mass spectrometry allows molecular depth profiling of organic and polymeric materials. Because typical raw data may contain thousands of peaks, the amount of information to manage grows rapidly and widely, so that data reduction techniques become indispensable in order to extract the most significant information from the given dataset. Here, we show how the wavelet‐based signal processing technique can be applied to the compression of the giant raw data acquired during time‐of‐flight secondary ion mass spectrometry molecular depth‐profiling experiments. We tested the approach on data acquired by analyzing a model sample consisting of polyelectrolyte‐based multilayers spin‐cast on silicon. Numerous wavelet mother functions and several compression levels were investigated. We propose some estimators of the filtering quality in order to find the highest ‘safe’ approximation value in terms of peaks area modification, signal to noise ratio, and mass resolution retention. The compression procedure allowed to obtain a dataset straightforwardly ‘manageable’ without any peak‐picking procedure or detailed peak integration. Moreover, we show that multivariate analysis, namely, principal component analysis, can be successfully combined to the results of the wavelet‐filtering, providing a simple and reliable method for extracting the relevant information from raw datasets. Copyright © 2016 John Wiley & Sons, Ltd.     

A concise review of mass spectrometry imaging

Mass spectrometric imaging allows the investigation of the spatial distribution of molecules at complex surfaces. The combination of molecular speciation with local analysis renders a chemical microscope that can be used for the direct biomolecular characterization of histological tissue surfaces. MS based imaging advantageously allows label-free detection and mapping of a wide-range of biological compounds whose presence or absence can be the direct result of disease pathology. Successful detection of the analytes of interest at the desired spatial resolution requires careful attention to several steps in the mass spectrometry imaging protocol. This review will describe and discuss a selected number of crucial developments in ionization, instrumentation, and application of this innovative technology. The focus of this review is on the latest developments in imaging MS. Selected biological applications are employed to illustrate some of the novel features discussed. Two commonly used MS imaging techniques, secondary ion mass spectrometric (SIMS) imaging and matrix-assisted laser desorption ionization (MALDI) mass spectrometric imaging, center this review. New instrumental developments are discussed that extend spatial resolution, mass resolving power, mass accuracy, tandem-MS capabilities, and offer new gas-phase separation capabilities for both imaging techniques. It will be shown how the success of MS imaging is crucially dependent on sample preparation protocols as they dictate the nature and mass range of detected biomolecules that can be imaged. Finally, developments in data analysis strategies for large imaging datasets will be briefly discussed.     

Comparative visualization for comprehensive two-dimensional gas chromatography

This paper investigates methods for comparing datasets produced by comprehensive two-dimensional gas chromatography (GC x GC). Chemical comparisons are useful for process monitoring, sample classification or identification, correlative determinations, and other important tasks. GC x GC is a powerful new technology for chemical analysis, but methods for comparative visualization must address challenges posed by GC x GC data: inconsistency and complexity. The approach extends conventional techniques for image comparison by utilizing specific characteristics of GC x GC data and developing new methods for comparative visualization and analysis. The paper describes techniques that register (or align) GC x GC datasets to remove retention-time variations; normalize intensities to remove sample amount variations; compute differences in local regions to remove slight misregistrations and differences in peak shapes; employ color (hue), intensity, and saturation to simultaneously visualize differences and values; and use tools for masking, three-dimensional visualization, and tabular presentation with controls for graphical highlights to significantly improve comparative analysis of GC x GC datasets. Experimental results indicate that the comparative methods preserve chemical information and support qualitative and quantitative analyses.     

Processing MALDI Mass Spectra to Improve Mass Spectral Direct Tissue Analysis

Profiling and imaging biological specimens using MALDI mass spectrometry has significant potential to contribute to our understanding and diagnosis of disease. The technique is efficient and high-throughput providing a wealth of data about the biological state of the sample from a very simple and direct experiment. However, in order for these techniques to be put to use for clinical purposes, the approaches used to process and analyze the data must improve. This study examines some of the existing tools to baseline subtract, normalize, align, and remove spectral noise for MALDI data, comparing the advantages of each. A preferred workflow is presented that can be easily implemented for data in ASCII format. The advantages of using such an approach are discussed for both molecular profiling and imaging mass spectrometry.     

PARAFASCA: ASCA combined with PARAFAC for the analysis of metabolic fingerprinting data

Novel post‐genomics experiments such as metabolomics provide datasets that are highly multivariate and often reflect an underlying experimental design, developed with a specific experimental question in mind. ANOVA‐simultaneous component analysis (ASCA) can be used for the analysis of multivariate data obtained from an experimental design instead of the widely used principal component analysis (PCA). This increases the interpretability of the model in terms of the experimental question. Aside from the levels of individual factors, variation that can be described by the experimental design may also depend on levels of multiple (crossed) factors simultaneously, e.g. the interactions. ASCA describes each contribution with a PCA model, but a contribution depending on crossed factors may be described more parsimoniously by multiway models like parallel factor analysis (PARAFAC). The combination of PARAFAC and ASCA, named PARAFASCA, provides a view on the data that is both parsimonious and focused on the experimental question. The novel method is used to analyze a dataset in which the effect of two doses of hydrazine on the urinary chemical composition of rats is investigated by time‐resolved metabolic fingerprinting with nuclear magnetic resonance (NMR) spectroscopy. This experiment has been conducted to monitor the dose‐specific urine composition changes in time upon hydrazine administration. Comparison of the PCA, the ASCA and the PARAFASCA models shows that ASCA and PARAFASCA describe the data more dedicated to the experimental question than PCA, but that PARAFASCA is more parsimonious than ASCA, and separates the variation underlying different effects better. Copyright © 2008 John Wiley & Sons, Ltd.     

Denoising and multivariate analysis of time‐of‐flight SIMS images

Time‐of‐flight SIMS (ToF‐SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF‐SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF‐SIMS image data. Three established denoising algorithms—down‐binning, boxcar and wavelet filtering—were applied to ToF‐SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component score images and the ability of the principal components to explain the chemistries responsible for the image contrast. All filtering methods were found to improve the performance of PCA for all image datasets studied by improving capture of image features and producing principal component score images of higher quality than the unfiltered ion images. The loadings for filtered and unfiltered PCA models described the regions of chemical contrast by identifying peaks defining the regions of different surface chemistry. Down‐binning the images to increase pixel size and signal was the most effective technique to improve PCA performance. Copyright © 2003 John Wiley & Sons, Ltd.     

Quantification and methodology issues in multivariate analysis of ToF‐SIMS data for mixed organic systems

Multivariate methods, such as principal component analysis (PCA) and multivariate curve resolution (MCR), are often employed to aid the analysis of large complex data sets such as time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) images. There is, however, much confusion over the most appropriate choice of method for any given application and the effects of data preprocessing, which is exacerbated by the confusing terminologies and the use of jargon in this field. In the present study, a simple model system consisting of a ToF‐SIMS image of an immiscible polymer blend is used to evaluate PCA and MCR in the accurate identification, localisation and quantification of the phase‐separated polymer domains, using four data preprocessing methods (no scaling, normalisation, variance scaling and Poisson scaling). This highlights significant issues and challenges in the quantitative multivariate analysis of mixed organic systems, including the discrimination of chemically significant features from experimental noise, the resolution of weak chemical contributions and potential bias introduced by data preprocessing. Multivariate analysis using Poisson scaling, identified as the most suitable data preprocessing method for both PCA and MCR, demonstrates a marked improvement upon traditional (manual) analysis and provides valuable additional information that is difficult to detect using traditional analysis. Using these results, we present recommendations for the optimum use of multivariate analysis by analysts and provide guidance on selecting the most appropriate methods. Confusing terminology is also clarified. © Crown copyright 2008. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.     

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied.     

Three-dimensional mass spectral imaging of HeLa-M cells--sample preparation, data interpretation and visualisation

Time-of-flight secondary ion mass spectrometry (ToFSIMS) is being applied increasingly to the study of biological systems where the chemical specificity of mass spectrometry and the high lateral resolution imaging capabilities can be exploited. Here we report a comparison of two cell sample preparation methods and demonstrate how they influence the outcome of the ToFSIMS analysis for three-dimensional (3D) imaging of biological cells using our novel buncher-ToF instrument (J105 3D Chemical Imager) equipped with a C(60) primary ion beam. Cells were analysed fixed and freeze-dried and non-fixed, frozen-hydrated. It is concluded that maintaining the cells in a non-fixed frozen-hydrated state during the analysis helps reduce chemical redistribution, producing cleaner spectra and improved chemical contrast in both 2D and 3D imaging. Insights into data interpretation are included and we present methods for 3D reconstruction of the data using multivariate analysis techniques.     

Generalization of multivariate optical computations as a method for improving the speed and precision of spectroscopic analyses

Multivariate optical computations (MOCs) offer improved analytical precision and increased speed of analysis via synchronous data collection and numerical computation with scanning spectroscopic systems. The improved precision originates in the redistribution of integration time from spurious channels to informative channels in an optimal manner for increasing the signal‐to‐noise ratio with multivariate analysis under the constraint of constant total analysis time. In this work, MOCs perform the multiplication and addition steps of spectral processing by adjusting the integration parameters of the optical detector or adjusting the scanning profile of the tunable optical filter. Improvement in the precision of analysis is achieved via the implicit optimization of the analytically useful signal‐to‐noise ratio. The speed improvements are realized through simpler data post‐processing, which reduces the computation time required after data collection. Alternatively, the analysis time may be significantly truncated while still seeing an improvement in the precision of analysis, relative to competing methods. Surface plasmon resonance (SPR) spectroscopic sensors and visible reflectance spectroscopic imaging were used as test beds for assessing the performance of MOCs. MOCs were shown to reduce the standard deviation of prediction by 15% compared to digital data collection and analysis with the SPR and up to 45% for the imaging applications. Similarly, a 30% decrease in the total analysis time was realized while still seeing precision improvements. Copyright © 2008 John Wiley & Sons, Ltd.     

Variable alignment of high resolution data by cluster analysis

High resolution time-of-flight secondary ion mass spectrometry (HR TOF-SIMS) is a powerful surface analytical method. For complex samples, this technique may yield intricate spectra that are difficult to interpret visually. Chemometric methods are useful for data analysis. However, these methods require that spectra are represented in a matrix format. Variances in mass measurements caused by calibration or instrumental effects may present difficulties in properly aligning mass spectral peaks into the correct columns of the data matrix. Cluster analysis of resolution elements is proposed as an alternative approach to construct the data matrix. An automated method for optimizing the data alignment is presented and evaluated for standard steel samples.     

Distributed computing strategies for processing of FT-ICR MS imaging datasets for continuous mode data visualization

High-resolution Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry imaging enables the spatial mapping and identification of biomolecules from complex surfaces. The need for long time-domain transients, and thus large raw file sizes, results in a large amount of raw data (“big data”) that must be processed efficiently and rapidly. This can be compounded by large-area imaging and/or high spatial resolution imaging. For FT-ICR, data processing and data reduction must not compromise the high mass resolution afforded by the mass spectrometer. The continuous mode “Mosaic Datacube” approach allows high mass resolution visualization (0.001 Da) of mass spectrometry imaging data, but requires additional processing as compared to feature-based processing. We describe the use of distributed computing for processing of FT-ICR MS imaging datasets with generation of continuous mode Mosaic Datacubes for high mass resolution visualization. An eight-fold improvement in processing time is demonstrated using a Dutch nationally available cloud service.

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