Quantitative Analysis of a Weak Correlation between Complicated Data on the Basis of Principal Component Analysis

Abstract

The mining of weak correlation information between two data matrices with high complexity is a very challenging task. A new method named principal component analysis-based multiconfidence ellipse analysis (PCA/MCEA) was proposed in this study, which first applied a confidence ellipse to describe the difference and correlation of such information among different categories of objects/samples on the basis of PCA operation of a single targeted data. This helps to find the number of objects contained in the overlapping and nonoverlapping areas of ellipses obtained from PCA runs. Then, a quantitative evaluation index of correlation between data matrices was defined by comparing the PCA results of more than one data matrix. The similarity and difference between data matrices was further quantified through comprehensively analyzing the outcomes. Complicated data of tobacco agriculture were used as an example to illustrate the strategy of the proposed method, which includes rich features of climate, altitude, and chemical compositions of tobacco leaves. The number of objects of these data reached 171,516 with 14, 4, and 5 descriptors of climate, altitude, and chemicals, respectively. On the basis of the new method, the complex but weak relationship between these independent and dependent variables were interestingly studied. Three widely used but conventional methods were applied for comparison in this work. The results showed the power of the new method to discover the weak correlation between complicated data.

Insulin-like growth factor 2 and autophagy gene expression alteration arise as potential biomarkers in Parkinson’s disease

“…4 A, blue squares), showing a decrease in the IGF2 levels and the expression of genes from the initial step of the autophagy pathway, excepting LC3B and RUBICON . Next, we evaluated the most predominantly variables (IGF2 and autophagy gene expression) associated with PD patients using a PCA, a statistical method that allows the association of the profile of gene expression for each patient 71 – 73 . For instance, we can find an association of patients' gender with a specific gene expression profile using this method.…”

“…A heat map and a dendrogram analysis were performed using the GraphPad Prism 9 and Statistics 10.0 software to evaluate the potential clustering of patients and controls based on the expression levels of IGF2 and autophagy genes evaluated. To potentially categorize a predominant behavior for each patient based on the evaluated variables, IGF2 and autophagy genes expression, we performed a principal component analysis (PCA), a statistical method used to predict responses based on a group of multiple variables' information 71 – 73 using the Statistics 10.0 software 74 . mRNA expression levels in macrophage cell culture were analyzed using ordinary one-way ANOVA (parametric data).…”

The state of water is associated with the viability and acidification capacity of Lactobacilli in frozen sourdough

“…According to the requirements of total eigenvalues (≥ 1) and cumulative variance (≥ 85%), only component 1 was extracted, that total eigenvalues and cumulative variance were 6.503 and 92.895%, respectively (Table 4). Loading values reflect the influence degree of indexes (Pang et al 2021). Table 5 showed that the two biggest loading values were TTA (0.982) and WLR (-0.982), which indicated TTA and WLR were two key factors to evaluate the quality of frozen sourdough.…”

Insulin-Like Growth Factor 2 (IGF2) and Autophagy Gene Expression Alteration in Parkinson’s Disease: Potential Predictor Markers.

“…4A, bottom), showing a decrease in the expression of genes from the initial step of the autophagy pathway, excepting LC3 and RUBICON. Then, we performed a principal component analysis (PCA) (Pang et al, 2021;Seki et al, 2019;Wang et al, 2021) by using the Statistics 10.0 software (Hair et al, 2013). This analysis revealed a clear separation pro le for a group of patients (PC1).…”