Application of Advanced Statistical Methods in the Plant Biotechnology and Phytochemistry

2015 ◽  
Vol 712 ◽  
pp. 101-106
Author(s):  
Ewa Skrzypczak-Pietraszek ◽  
Jacek Pietraszek
Keyword(s):  
Principal Component Analysis ◽  
Statistical Methods ◽  
Plant Biotechnology ◽  
Principal Component ◽  
Final Analysis ◽  
Component Analysis ◽  
Measured Data ◽  
Problem Definition ◽  
Wallis Anova ◽  
Non Parametric

The large dimensionality and unknown distributions are often met in a plant biotechnology and phytochemistry investigations. In this paper two methods are presented: principal component analysis allowing to reduce dimensionality and non-parametric Kruskal-Wallis ANOVA allowing to separate factors’ influence even if the distribution is unknown. The paper contains: problem definition, presentation of the measured data and the final analysis. The paper should be potentially useful to other industrial or research approaches.

Multivariate Techniques of Use in Biological Research

1969 ◽  
Vol 5 (1) ◽  
pp. 67-77 ◽  
Author(s):  
S. C. Pearce
Keyword(s):  
Principal Component Analysis ◽  
Statistical Methods ◽  
Matrix Algebra ◽  
Principal Component ◽  
Component Analysis ◽  
Canonical Analysis ◽  
Biological Research ◽  
Multivariate Techniques ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical

SUMMARYMultivariate statistical methods are used increasingly in biological research to investigate the responses of organisms considered as a whole, whereas established statistical methods are usually concerned with measured characteristics considered one at a time. Multivariate techniques are mostly explained in terms of matrix algebra, which is a way of dealing with groups of numbers rather than individual ones. A brief description is given of some elementary results of matrix algebra and a method is presented whereby hypotheses can be generated about interrelations within an organism. Two techniques, principal component analysis and canonical analysis, are described in greater detail. It is emphasized that hypotheses need to be tested even though they have been generated by objective statistical means.

scholarly journals Principal Component Analysis and Factor Analysis: differences and similarities in Nutritional Epidemiology application

2019 ◽  
Vol 22 ◽  
Author(s):  
Roberta de Oliveira Santos ◽  
Bartira Mendes Gorgulho ◽  
Michelle Alessandra de Castro ◽  
Regina Mara Fisberg ◽  
Dirce Maria Marchioni ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Statistical Methods ◽  
Principal Component ◽  
Component Analysis ◽  
Food Group ◽  
Correlated Data ◽  
Nutritional Epidemiology ◽  
Study Results ◽  
Nutritional Studies

ABSTRACT: Introduction: Statistical methods such as Principal Component Analysis (PCA) and Factor Analysis (FA) are increasingly popular in Nutritional Epidemiology studies. However, misunderstandings regarding the choice and application of these methods have been observed. Objectives: This study aims to compare and present the main differences and similarities between FA and PCA, focusing on their applicability to nutritional studies. Methods: PCA and FA were applied on a matrix of 34 variables expressing the mean food intake of 1,102 individuals from a population-based study. Results: Two factors were extracted and, together, they explained 57.66% of the common variance of food group variables, while five components were extracted, explaining 26.25% of the total variance of food group variables. Among the main differences of these two methods are: normality assumption, matrices of variance-covariance/correlation and its explained variance, factorial scores, and associated error. The similarities are: both analyses are used for data reduction, the sample size usually needs to be big, correlated data, and they are based on matrices of variance-covariance. Conclusion: PCA and FA should not be treated as equal statistical methods, given that the theoretical rationale and assumptions for using these methods as well as the interpretation of results are different.

Principal component analysis based on non-parametric maximum entropy

2010 ◽  
Vol 73 (10-12) ◽  
pp. 1840-1852 ◽  
Author(s):  
Ran He ◽  
Baogang Hu ◽  
XiaoTong Yuan ◽  
Wei-Shi Zheng
Keyword(s):  
Principal Component Analysis ◽  
Maximum Entropy ◽  
Principal Component ◽  
Component Analysis ◽  
Non Parametric

scholarly journals Capacitive facial activity measurement

ACTA IMEKO ◽  
2014 ◽  
Vol 2 (2) ◽  
pp. 78 ◽  
Author(s):  
Ville Rantanen ◽  
Pekka Kumpulainen ◽  
Hanna Venesvirta ◽  
Jarmo Verho ◽  
Oleg Spakov ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Principal Component ◽  
Component Analysis ◽  
Measured Data ◽  
Activity Measurement ◽  
Facial Movements ◽  
Wide Range ◽  
The Face ◽  
Facial Activity

A wide range of applications can benefit from the measurement of facial activity. The current study presents a method that can be used to detect and classify the movements of different parts of the face and the expressions the movements form. The method is based on capacitive measurement of facial movements. It uses principal component analysis on the measured data to identify active areas of the face in offline analysis, and hierarchical clustering as a basis for classifying the movements offline and in real-time. Experiments involving a set of voluntary facial movements were carried out with 10 participants. The results show that the principal component analysis of the measured data could be applied with almost perfect performance to offline mapping of the vertical location of the facial activity of movements such as raising and lowering eyebrows, opening mouth, raising mouth corners, and lowering mouth corners. The presented classification method also performed very well in classifying the same movements both with the offline and the real-time implementations.

scholarly journals ALGORITMA PRINCIPAL COMPONEN ANALYSIS DALAM PEMROSESAN SINYAL ELECTROKARDIOGRAM

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Husaini Husaini ◽  
Huzaeni Huzaeni ◽  
Fahmi Fahmi
Keyword(s):  
Principal Component Analysis ◽  
Parametric Method ◽  
Principal Component ◽  
Component Analysis ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Redundant Data ◽  
The Cost ◽  
Pca Algorithm ◽  
Non Parametric

Abstrak — Principal Component Analysis (PCA) merupakan salah satu teknik yang ada dalam statistic dan merupakan metode non parametric untuk mengekstraksi informasi-informasi yang bersesuaian dari sekumpulan data yang masih diragukan dan memerlukan proses untuk menghilangkan gangguan-gangguan yang ada. Data yang dimaksud salah satunya adalah sinyal ektrokardiogram (EKG). Sinyal EKG merupakan sinyal yang diperoleh dari rekaman aktifitas elektrik dari jantung. Rekaman sinyal EKG tidak saja digunakan untuk tujuan diagnosa, tapi juga disimpan sebagai referensi dalam mengklasifikasi EKG arrhythmia. Untuk mendapatkan hasil yang lebih baik maka data-data sinyal EKG akan direduksi dimensinya dengan tujuan untuk menghilangkab data-data yang tidak sesuai, tidak relevan dan data redundant sehingga dapat menghemat biaya komputasinya dan mencegah data-data yang over-fitting. Tulisan ini memaparkan tentang ide dasar dari PCA dalam mereduksi dimensi data-data dari sinyal  EKG. Hasil yang ditampilkan adalah berupa proses-proses dalam algoritma PCA dan akurasi klasifikasi sinyal  dengan metode KNN dan Naive Bayes.Kata kunci : principal component analysisi (PCA), sinyal EKG, reduksi dimensi Abstract — The Principal Component Analysis (PCA) is one of the existing techniques in statistics and a non parametric method for extracting the information from a collection of data that still in doubt and requires a process to remove any disturbances. The data in question one of them is the signal ektrokardiogram (ECG). ECG signals are signals obtained from recording electrical activity from the heart. ECG signal recording is not only used for diagnostic purposes, but is also stored as a reference in classifying ECG arrhythmias. To get better results then the ECG signal data will be reduced the dimension. The aim to removed data that are not appropriate, irrelevant and redundant data so as to save the cost of computing and prevent data over-fitting. This paper describes the basic idea of PCA in reducing the dimensions of data from ECG signals. The results shown are the processes in PCA algorithm and signal classification accuracy by KNN and Naive Bayes methods.Keywords— Principal Component Analysis, ECG Signal, reduction dimentionality

Composition of Cellular Subsets by Flow Cytometry Identifies Differences Between MDS Subtypes and Aplastic Anemia but No Differences Are Identified Between Cases with and without Monosomy 7.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 3802-3802
Author(s):  
Ester Mejstrikova ◽  
Vendula Pelkova ◽  
Michaela Reiterová ◽  
Martina Sukova ◽  
Zuzana Zemanova ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Flow Cytometry ◽  
B Cells ◽  
B Cell ◽  
Aplastic Anemia ◽  
Principal Component ◽  
Component Analysis ◽  
Monosomy 7 ◽  
Parametric Statistics ◽  
Non Parametric

Abstract Abstract 3802 Poster Board III-738 Introduction Monosomy 7 or del(7q) are frequent cytogenetic abnormalities in children with myelodysplastic syndrome (MDS) and associates with poor prognosis. MDS globally affects all cellular subsets in bone marrow and in peripheral blood. We asked whether flow cytometry (FC) can separate individual subtypes of MDS from each other and from aplastic anemia (SAA) and whether in individual subtypes of childhood MDS can separate patients with and without monosomy 7. Patients/analyzed parameters In total we analyzed 94 children with centrally analyzed immunophenotype in the reference lab who were diagnosed and treated for MDS or SAA between 1998 and 2009. In total we analyzed 14 patients with refractory cytopenia, 37 patients with advanced forms of MDS (JMML 10, RAEB 25, CMML 2) and 43 patients with SAA. Monosomy 7/del(7q) was present in 17 patients (RC 6, JMML 3, RAEB 8). Analyzed parameters were as follows: B cells, CD10+CD19+, CD19+45dim/neg, CD19+34+, CD19/CD34 ratio, CD34+, CD117 cells, CD34+38dim/neg, CD3+, CD3+4+, CD3+8+, CD3+HLADR+. Statistics We analyzed all parameters using non parametric tests (Mann-Whitney, Kruskal Wallis) and principal component analysis (PCA). Results Principal component analysis of all analyzed patients together clearly separates advanced forms of MDS from RC and SAA, the most contributing factor being the number of CD34 and CD117+ cells. In non parametric statistics following factors significantly differ among MDS subtypes and SAA (Kruskal-Wallis): CD19, CD117, CD34, CD3, CD3+4+, CD8+ and CD3+HLADR+. RC and SAA patients are separated mainly by the number of B cells and the CD34:CD19 ratio. In addition, the following parameters differ between RC and SAA (Mann-Whitney): CD34, CD117 and CD3+HLADR+. Unlike the CD34:CD19 ratio, the number of CD19+34+ precursors does not differ between RC and SAA patients. Patients with monosomy 7 do not differ from the remaining patients when all MDS patients are analyzed together or separately in the respective subgroups (RC, non RC, JMML) by PCA or by non parametric statistics. Conclusion PCA separates advanced MDS forms from RC and SAA. Advanced forms of MDS are characterized by increased percentage of CD34+ and CD117+ cells compared to RC and SAA patients. The global reduction of B cell progenitor compartment is pronounced especially in non-JMML cases of MDS, whereas SAA patients typically present with isolated reduction of cells at early stages (CD19+34+) of B cell development. Patients with monosomy 7 cluster within the respective disease category, they do not form own cluster in PCA. Supported by MSMT VZ MSM0021620813, MZO 00064203 VZ FNM, MZO VFN2005, IGA NR/9531-3, NPV 2B06064. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Measuring local competitiveness: comparing and integrating two methods PCA and AHP

2021 ◽  
Author(s):  
Katarzyna A. Kurek ◽  
Wim Heijman ◽  
Johan van Ophem ◽  
Stanisław Gędek ◽  
Jacek Strojny
Keyword(s):  
Social Sciences ◽  
Principal Component Analysis ◽  
Analytical Hierarchy Process ◽  
Mixed Method ◽  
Principal Component ◽  
Component Analysis ◽  
Mixed Modelling ◽  
Hierarchy Process ◽  
Comparable Model ◽  
Non Parametric

AbstractThis article discusses two methods to measure the concept of local competitiveness: Principal Component Analysis (PCA) and Analytical Hierarchy Process (AHP). The goal of this analysis is to determine whether these two methods used in social sciences research lead to comparable model results. By non-parametric tests we show that there is a significant correlation between the PCA and AHP local competitiveness indexes. Thereafter, a developed mixed method examination of whether the methods can be used interchangeably is presented and illustrated with detailed examples of two mixed approaches. The mixed method confirms the correlation between the PCA and AHP models. However, the mixed modelling results indicate the utility of the PCA in the situation of a multicriteria local competitiveness data examination.

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