Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Classification of RNA-Sequencing Data Via Poisson and Negative Binomial Linear Discriminant Analyses: A Methodological Study
RNA-Dizileme Verilerinin Poisson ve Negatif Binom Doğrusal Ayırma Analizleri ile Sınıflandırılması: Metodolojik Bir Çalışma
Dinçer GÖKSÜLÜKa, Ahmet Ergün KARAAĞAOĞLUb
aDepartment of Biostatistics, Erciyes University Faculty of Medicine, Kayseri, Türkiye
bDepartment of Biostatistics, Lokman Hekim University Faculty of Medicine, Ankara, Türkiye
Turkiye Klinikleri J Biostat. 2023;15(3):150-60
doi: 10.5336/biostatic.2023-98597
Article Language: EN
Full Text
ABSTRACT
 Objective: Microarray and RNA sequencing (RNASeq) technologies are frequently employed in genetic data analysis for detecting disease-associated genes, identifying cancer subtypes, and enabling molecular diagnosis. While numerous methods have been proposed for classification problems using microarray data, there is a paucity of developed methods for classifying RNA-Seq data. This study aims to compare the performance of novel methods developed for RNA-Seq data on 3 distinct real-life datasets. Material and Methods: Cervical cancer, Alzheimer's disease, and kidney cancer RNA-Seq data were utilized in this study. The data were divided into training and test sets in a %70 and %30 ratio, respectively. Various preprocessing steps, such as normalization, power transformation, and variance filtering, were applied to the data. The Poisson Linear Discriminant Analysis (PLDA) and Negative Binomial Linear Discriminant Analysis (NBLDA) models were used for classification purposes, and the predictive performances of these models were compared. Results: Among the three datasets, the Alzheimer's data exhibited the lowest level of dispersion, while the cervical cancer data had the highest overdispersion. The NBLDA model demonstrated superior classification performance compared to the PLDA model. In cases of mild-to-moderate overdispersion, the predictive performance of the PLDA model improved when power transformation was applied, resulting in performance similar to that of the NBLDA model. Conclusion: PLDA and NBLDA models are two novel and promising techniques used in classifying RNA-Seq data. The performance of these models is influenced by the degree of overdispersion. In cases of high overdispersion, it is recommended to utilize the NBLDA model.

Keywords: Genomics; RNA-Sequencing; PLDA; NBLDA; classification
ÖZET
 Amaç: Mikrodizi ve RNA dizileme teknolojileri, genetik çalışmalarda hastalıkla ilişkili genlerin tespiti, kanser alt tiplerinin belirlenmesi, moleküler teşhis gibi amaçlar için sıklıkla kullanılan yöntemlerdir. Mikrodizi verilerinde sınıflama problemleri için literatürde birçok yöntem önerilmiştir. Bununla birlikte RNA dizileme verilerinde sınıflama problemleri için sınırlı sayıda yöntem bulunmaktadır. Bu çalışma, RNA dizileme verileri için geliştirilen yeni yöntemlerin performansını 3 farklı gerçek veri seti üzerinde karşılaştırmayı amaçlamaktadır. Gereç ve Yöntemler: Bu çalışmada, serviks kanseri, Alzheimer hastalığı ve böbrek kanseri RNA dizileme verileri kullanılmıştır. Veriler, sırasıyla %70 ve %30 oranında eğitim ve test kümelerine ayrılmıştır. Normalizasyon, güç dönüşümü ve varyans filtreleme gibi çeşitli ön işlemlerden sonra veriler, Poisson Doğrusal Ayırma Analizi (PDAA) ve Negatif Binom Doğrusal Ayırma Analizi (NBDAA) modelleri kullanılarak modellenmiş ve modellerin tahmin performansları karşılaştırılmıştır. Bulgular: Üç veri seti arasında Alzheimer verisi en düşük, serviks kanseri verisi ise en yüksek aşırı dağılıma sahipti. NBDAA modeli, PDAA modeline göre daha iyi sınıflandırma performansı göstermiştir. Hafif-orta derecede aşırı dağılım gözlendiği durumlarda, PDAA modelinin tahmin performansı güç dönüşümü uygulandığında iyileşmiş ve NBDAA ile benzer performans elde edilmiştir. Sonuç: PDAA ve NBDAA modelleri, RNA dizileme verilerinin sınıflandırılmasında kullanılan yeni ve umut verici tekniklerdir. Bu modellerin performansı, veri setindeki aşırı yaygınlığın derecesinden etkilenmektedir. Veride yüksek aşırı yaygınlık olması durumunda NBDAA modelinin kullanılması önerilmektedir.

Anahtar Kelimeler: Genomik; RNA-dizileme; Poisson Doğrusal Ayırma Analizi; Negatif Binom Doğrusal Ayırma Analizi; sınıflama
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