Normalizasyon Yöntemlerinin RNA- Seq Verileri Üzerinde Çıkarılan Gen Birlikte İfade Edilme Ağlarının Performansına Etkisi

Abstract

Protein sentezi sürecinde meydana gelen farklılaşmaların metabolik hastalıklar, kanser gibi kompleks hastalıklara neden olduğu farklı çalışmalarda belirtilmiştir. Protein sentezindeki değişimlerin anlaşılması için proteinleri oluşturan genlerin belirlenmesi ve bu genlerin diğer genlerle ilişkilerin ortaya çıkarılması gerekmektedir. Yeni nesil dizileme teknikleriyle hastalıklara neden olan moleküler düzeyde ilişkilerin doğruluklu olarak belirlenmesi kolaylaşmıştır. Gen birlikte ifade edilme (GBİE) ağları düzenleyen-düzenleyici ilişkisi içermeden benzer biyolojik süreçlere katılan genler arasındaki ilişkileri araştırmacılara göstermektedir. Çalışmamızda RNA-Seq verileri kullanılarak prostat kanseriyle ilişkili GBİE ağları elde edilmiştir. RNA- Seq verileri farklı nükleotit uzunluğundaki genlerden ve farklı sayıda okumalar içeren örneklerden oluştuğu için normalizasyon teknikleri moleküler ilişki çıkarımında önem taşımaktadır. Çalışmamızda gen birlikte ifade edilme ağları ham veri ve farklı iki normalizasyon yaklaşımı olan M- Değerinin Kırpılmış Ortalaması (MDKO), Göreceli Log İfadesi (GLİ) hesaplamalarıyla ayrı ayrı oluşturulmuş veriler üzerinde çıkartılarak örtüşme analizi ve topolojik performans değerlendirilmesi yapılmıştır. Örtüşme analizine göre normalize edilmiş RNA- Seq verileri kullanarak elde edilmiş gen birlikte ifade edilme ağlarının ham verilere göre daha fazla literatürde bulunan ilişkileri tahmin ettiği gözlemlenmiştir. İki normalizasyon yöntemiyle elde edilen GBİE'lere ait örtüşme analizi performans metrikleri değerleri ise birbirlerine yakın çıkmıştır. Topolojik değerlendirme sonuçlara göre normalize edilmiş veriler üzerinde elde edilen GBİE ağlarının ölçeksiz ağ tanımına daha yakın olduğu gözlemlenmiştir. Çalışmamızda aynı zamanda ham ve normalize edilmiş veriler üzerinde GBİE ağ çıkarım algoritmaları olan C3NET, ARACNE ve WGCNA yaklaşımlarının performansları da karşılaştırılmıştır.

Keywords

• RNA- Seq
• Normalizasyon
• Gen Ağı Çıkarımı
• Gen Birlikte İfade Ağları

Effect of Normalization Methods on the Performance of Gene Co-expression Networks Inferred on RNA-Seq Data

Abstract

Different studies prove that differentiation on protein synthesis causes different metabolic disorders such as cancer and diabetics. The inference of disease related genes and to derive their interactions enable us to understand the differentiation on protein synthesis. Next generation sequencing techniques can reveal relations of diseases more precisely at molecular level. Gene co-expression networks can reveal interactions between genes without regulator-regulatee information. We utilized RNA- Seq data to infer gene co-expression networks of prostate cancer in our study. RNA- Seq data consists of genes whose nucleotide size may be different from sample to sample. Sample sizes of RNA- Seq data also vary for each samples. RNA- Seq data normalization is an important task to infer robust and reliable gene co-expression networks. We utilized normalized RNA- Seq data that are obtained using two different normalization methods ,which are Trimmed Mean of M- values (TMM) and Relative Log Expression (RLE), and raw RNA- Seq data to infer gene co-expression networks for the performance comparison. We applied overlap and topological analyses to evalaute the performance of raw data based GCN, normalized data based GCNs in our study. Normalization on RNA-Seq data leads to predict more validated gene- gene relations, which are evaluated in overlap analysis, than gene- gene relations on raw dataset. Two normalization methods based gene co-expression networks present similar performance results in overlap analysis. GCNs that are derived from TMM and RLE normalizations resemble scale free topology more than raw based GCN in topological assessment. We also compare the performance results of gene network inference algorithms, which are C3NET, ARACNE and WGCNA, on raw and normalized datasets. 

Keywords

• RNA- Seq
• Normalization
• Gene Network Inference
• Gene Co-expression Networks

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