Classification of malignant gliomas by infrared spectroscopy and linear discriminant analysis

Biopolymers ◽  
2006 ◽  
Vol 82 (4) ◽  
pp. 301-305 ◽  
Author(s):  
Christoph Krafft ◽  
Katja Thümmler ◽  
Stephan B. Sobottka ◽  
Gabriele Schackert ◽  
Reiner Salzer
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Malignant Gliomas ◽  
Linear Discriminant

scholarly journals Classification of blue pen ink using infrared spectroscopy and linear discriminant analysis

2013 ◽  
Vol 109 ◽  
pp. 122-127 ◽  
Author(s):  
Carolina Santos Silva ◽  
Flávia de Souza Lins Borba ◽  
Maria Fernanda Pimentel ◽  
Marcio José Coelho Pontes ◽  
Ricardo Saldanha Honorato ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Linear Discriminant

scholarly journals Classification of cereal bars using near infrared spectroscopy and linear discriminant analysis

2013 ◽  
Vol 51 (2) ◽  
pp. 924-928 ◽  
Author(s):  
Anna Luiza Bizerra Brito ◽  
Lívia Rodrigues Brito ◽  
Fernanda Araújo Honorato ◽  
Márcio José Coelho Pontes ◽  
Liliana Fátima Bezerra Lira Pontes
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Linear Discriminant ◽  
Cereal Bars

scholarly journals A comprehensive simulation study on classification of RNA-Seq data

2017 ◽  
Author(s):  
Gokmen Zararsiz ◽  
Dinçer Göksülük ◽  
Selçuk Korkmaz ◽  
Vahap Eldem ◽  
Gözde Ertürk Zararsız ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Sample Size ◽  
Linear Discriminant Analysis ◽  
Differential Expression ◽  
Simulation Study ◽  
Rna Seq ◽  
Linear Discriminant ◽  
Number Of Genes ◽  
Expression Rate

RNA sequencing (RNA-Seq) is a powerful technique for thegene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies.Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of geneexpression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data hierarchically closer to microarrays and apply microarray-based classifiers.In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such asoverdispersion, sample size, number of genes, number of classes, differential-expression rate, andthe transformation method on model performances.A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate, and number of genes and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM clas sifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www.bioconductor.org/packages/release/bioc/html/MLSeq.html .

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