scholarly journals Effect Size Estimation and Misclassification Rate Based Variable Selection in Linear Discriminant Analysis

2021 ◽  
Vol 11 (3) ◽  
pp. 537-558
Author(s):  
Bernd Klaus
Keyword(s):  
Discriminant Analysis ◽  
Variable Selection ◽  
Linear Discriminant Analysis ◽  
Effect Size ◽  
Misclassification Rate ◽  
Size Estimation ◽  
Linear Discriminant ◽  
Effect Size Estimation

scholarly journals Altered Airway Microbiota Composition in Patients With Pulmonary Hypertension

Hypertension ◽  
2020 ◽  
Vol 76 (5) ◽  
pp. 1589-1599 ◽  
Author(s):  
Chenting Zhang ◽  
Tingting Zhang ◽  
Wenju Lu ◽  
Xin Duan ◽  
Xiaoyun Luo ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Discriminant Analysis ◽  
Respiratory Tract ◽  
Linear Discriminant Analysis ◽  
Effect Size ◽  
Bacterial Invasion ◽  
Microbiota Composition ◽  
Upper Respiratory Tract ◽  
Pharyngeal Swab ◽  
Linear Discriminant

Alteration in microbiota composition of respiratory tract has been reported in the progression of many chronic lung diseases, yet, the correlation and causal link between respiratory tract microbiota and the disease development of pulmonary hypertension (PH) remain largely unknown. This study aims to define and compare the respiratory microbiota composition in pharyngeal swab samples between patients with PH and reference subjects. A total of 118 patients with PH and 79 reference subjects were recruited, and the pharyngeal swab samples were collected to sequence the 16S ribosomal RNA (16S rRNA) V3-V4 region of respiratory microbiome. The relative abundances in patients with PH were profoundly different from reference subjects. The Ace and Sobs indexes indicated that the microbiota richness of pharynx value is significantly higher; while the community diversity value is markedly lower in patients with PH, comparing to those of the reference subjects. The microbiota on pharynx showed a different profile between the 2 groups by principal component analysis. The linear discriminant analysis effect size also revealed a significantly higher proportion of Streptococcus , Lautropia , and Ralstonia in patients with PH than reference subjects. The linear discriminant analysis effect size output, which represents the microbial gene functions, suggest genes related to bacterial invasion of epithelial cells, bacterial toxins were enhanced, while genes related to energy metabolism, protein digestion and absorption, and cell division pathways were attenuated in patients with PH versus reference subjects. In summary, our study reports the first systematic definition and divergent profile of the upper respiratory tract microbiota between patients with PH and reference subjects.

Application of sparse linear discriminant analysis for metabolomics data

2014 ◽  
Vol 6 (22) ◽  
pp. 9037-9044 ◽  
Author(s):  
Meilan Ouyang ◽  
Zhimin Zhang ◽  
Chen Chen ◽  
Xinbo Liu ◽  
Yizeng Liang
Keyword(s):  
Discriminant Analysis ◽  
Variable Selection ◽  
Linear Discriminant Analysis ◽  
New Method ◽  
Metabolomics Data ◽  
Linear Discriminant

A new method performs classification and variable selection simultaneously to analyze complicated metabolomics datasets.

scholarly journals Comparison of Predicted Probabilities of Proportional Hazards Regression and Linear Discriminant Analysis Methods Using a Colorectal Cancer Molecular Biomarker Database

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Sreelatha Meleth ◽  
Chakrapani Chatla ◽  
Venkat R. Katkoori ◽  
Billie Anderson ◽  
James M. Hardin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Discriminant Analysis ◽  
Variable Selection ◽  
Linear Discriminant Analysis ◽  
Error Rate ◽  
Proportional Hazards ◽  
Test Set ◽  
Linear Discriminant ◽  
Proportional Hazards Regression

Background Although a majority of studies in cancer biomarker discovery claim to use proportional hazards regression (PHREG) to the study the ability of a biomarker to predict survival, few studies use the predicted probabilities obtained from the model to test the quality of the model. In this paper, we compared the quality of predictions by a PHREG model to that of a linear discriminant analysis (LDA) in both training and test set settings. Methods The PHREG and LDA models were built on a 491 colorectal cancer (CRC) patient dataset comprised of demographic and clinicopathologic variables, and phenotypic expression of p53 and Bcl-2. Two variable selection methods, stepwise discriminant analysis and the backward selection, were used to identify the final models. The endpoint of prediction in these models was five-year post-surgery survival. We also used linear regression model to examine the effect of bin size in the training set on the accuracy of prediction in the test set. Results The two variable selection techniques resulted in different models when stage was included in the list of variables available for selection. However, the proportion of survivors and non-survivors correctly identified was identical in both of these models. When stage was excluded from the variable list, the error rate for the LDA model was 42% as compared to an error rate of 34% for the PHREG model. Conclusions This study suggests that a PHREG model can perform as well or better than a traditional classifier such as LDA to classify patients into prognostic classes. Also, this study suggests that in the absence of the tumor stage as a variable, Bcl-2 expression is a strong prognostic molecular marker of CRC.

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