Principal component analysis

Bro, Rasmus; Smilde, Age K.

doi:10.1039/c3ay41907j

Cited by 2,349 publications

(1,386 citation statements)

References 64 publications

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“…Many applications of chemometrics to EEM data make use of traditional multivariate analysis tools, such as PCA and PLS regression [87]. PCA can extract latent variables from a set of measurements, regardless of their origin [64]. While methods based on PCA are very powerful for assessing sample variance and fingerprinting of samples, there is another family of methods based on component resolution that are able to extract even more information from EEM data.…”

Section: Exploratory Analysis: Principal Component Analysismentioning

confidence: 99%

“…chemometrics). Proper use of chemometric data analysis on MDF data requires careful consideration of sample photo-physics and an in-depth understanding of the methods [64,65] and their use [66][67][68][69][70]. The initial and final steps in the implementation of EEM/SFS-based analytical methods generally requires the use of chemometrics, first to assess the reproducibility and robustness of the measurement itself, and second to produce an output in terms of a qualitative result or quantitative value.…”

Section: Chemometric Data Analysis For Calibration and Validationmentioning

confidence: 99%

See 1 more Smart Citation

Calibration, standardization, and quantitative analysis of multidimensional fluorescence (MDF) measurements on complex mixtures (IUPAC Technical Report)

Ryder

Stedmon

Harrit

et al. 2017

Pure and Applied Chemistry

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This IUPAC Technical Report describes and compares the currently applied methods for the calibration and standardization of multi-dimensional fluorescence (MDF) spectroscopy data as well as recommendations on the correct use of chemometric methods for MDF data analysis. The paper starts with a brief description of the measurement principles for the most important MDF techniques and a short introduction to the most important applications. Recommendations are provided for instrument calibration, sample preparation and handling, and data collection, as well as the proper use of chemometric data analysis methods.

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Section: Exploratory Analysis: Principal Component Analysismentioning

confidence: 99%

Section: Chemometric Data Analysis For Calibration and Validationmentioning

confidence: 99%

Calibration, standardization, and quantitative analysis of multidimensional fluorescence (MDF) measurements on complex mixtures (IUPAC Technical Report)

Ryder

Stedmon

Harrit

et al. 2017

Pure and Applied Chemistry

Self Cite

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“…Before determining the seed pathway, the PCA method was employed to compute the activity score for every pathway in the PIN [18]. Specifically, all data were assembled to a matrix X with j samples (j = 1, 2, …, J) and k pathways (k = 1, 2, …, K).…”

Section: Selecting Seed Pathwaymentioning

confidence: 99%

Dysregulated pathways for off-pump coronary artery bypass grafting

Xiang

Shu

et al. 2017

Open Life Sciences

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Abstract:Background: The objective of this paper was to identify dysregulated myocardial pathways with offpump coronary artery bypass grafting (OPCABG) based on pathway interaction network (PIN). Methodology: To achieve this goal, firstly, gene expression profiles, protein-protein interactions (PPIs) and pathway data were collected. Secondly, we constructed a PIN by integrating these data and Pearson correlation coefficient (PCC) algorithm. Next, for every pathway in the PIN, its activity was counted dependent on the principal component analysis (PCA) method to select the seed pathway. Ultimately, a minimum pathway set (MPS) was extracted from the PIN on the basis of the seed pathway and the area under the receiver operating characteristics curve (AUROC) index, and pathways in the MPS were denoted as dysregulated pathways. Results: The PIN had 1,189 nodes and 22,756 interactions, of which mitochondrial translation termination was the seed pathway. Starting with mitochondrial translation termination, a MPS (AUROC = 0.983) with 7 nodes and 26 edges was obtained. The 7 pathways were regarded as dysregulated myocardial pathways with OPCABG. Conclusion: The findings might provide potential biomarkers to diagnose early, serve as the evidence to perform the OPCABG and predict inflammatory response and myocardial reperfusion injury after OPCABG in the future.

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“…Principal component analysis [1] (PCA) is a powerful tool for surface analysis data and has many applications. It can provide an overview of exactly the type of complex data that modern surface analysis instruments produce.…”

Section: Introductionmentioning

confidence: 99%

Rapid multivariate analysis of 3D ToF‐SIMS data: graphical processor units (GPUs) and low‐discrepancy subsampling for large‐scale principal component analysis

Cumpson

Fletcher

Sano

et al. 2016

Surface & Interface Analysis

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Principal component analysis (PCA) and other multivariate analysis methods have been used increasingly to analyse and understand depth-profiles in XPS, AES and SIMS. For large images or three-dimensional (3D) imaging depth-profiles, PCA has been difficult to apply until now simply because of the size of the matrices of data involved. In a recent paper, we described two algorithms, random vector 1 (RV1) and random vector 2 (RV2), that improve the speed of PCA and allow datasets of unlimited size, respectively. In this paper, we now apply the RV2 algorithm to perform PCA on full 3D time-of-flight SIMS data for the first time without subsampling. The dataset we process in this way is a 128 × 128 pixel depth-profile of 120 layers, each voxel having a 70 439 value mass spectrum associated with it. This forms over a terabyte of data when uncompressed and took 27 h to process using the RV2 algorithm using a conventional windows desktop personal computer (PC). While full PCA (e.g. using RV2) is to be preferred for final reports or publications, a much more rapid method is needed during analysis sessions to inform decisions on the next analytical step. We have therefore implemented the RV1 algorithm on a PC having a graphical processor unit (GPU) card containing 2880 individual processor cores. This increases the speed of calculation by a factor of around 4.1 compared with what is possible using a fast commercially available desktop PC having central processing units alone, and full PCA is performed in less than 7 s. The size of the dataset that can be processed in this way is limited by the size of the memory on the GPU card. This is typically sufficient for two-dimensional images but not 3D depth-profiles without sampling. We have therefore examined efficient sampling schemes that allow a good approximate solution to the PCA problem for large 3D datasets. We find that low-discrepancy series such as Sobol series sampling gives more rapid convergence than random sampling, and we recommend such methods for routine use. Using the GPU and low-discrepancy series together, we anticipate that any time-of-flight SIMS dataset, of whatever size, can be efficiently and accurately processed into PCA components in a maximum of around 10 s using a commercial PC with a widely available GPU card, although the longer RV2 approach is still to be preferred for the presentation of final results, such as in published papers.

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Principal component analysis

Abstract: Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas.

Cited by 2,349 publications

References 64 publications

Calibration, standardization, and quantitative analysis of multidimensional fluorescence (MDF) measurements on complex mixtures (IUPAC Technical Report)

Calibration, standardization, and quantitative analysis of multidimensional fluorescence (MDF) measurements on complex mixtures (IUPAC Technical Report)

Dysregulated pathways for off-pump coronary artery bypass grafting

Rapid multivariate analysis of 3D ToF‐SIMS data: graphical processor units (GPUs) and low‐discrepancy subsampling for large‐scale principal component analysis

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