Robust and efficient identification of biomarkers from RNA-Seq data using median control chart

Background: One of the main goals of RNA-seq data analysis is identification of biomarkers that are differentially expressed (DE) across two or more experimental conditions. RNA-seq uses next generation sequencing technology and it has many advantages over microarrays. Numerous statistical methods have already been developed for identification the biomarkers from RNA-seq data. Most of these methods were based on either Poisson distribution or negative binomial distribution. However, efficient biomarker identification from discrete RNA-seq data is hampered by existing methods when the datasets contain outliers or extreme observations. Specially, the performance of these methods becomes more severe when the data come from a small number of samples in the presence of outliers. Therefore, in this study, an attempt is made to propose an outlier detection and modification approach for RNA-seq data to overcome the aforesaid problems of traditional methods. We make our proposed method facilitate in RNA-seq data by transforming the read count data into continuous data. Methods: We use median control chart to detect and modify the outlying observation in a log-transformed RNA-seq dataset. To investigate the performance of the proposed method in absence and presence of outliers, we employ the five popular biomarker selection methods (edgeR, edgeR_robust, DEseq, DEseq2 and limma) both in simulated and real datasets. Results: The simulation results strongly suggest that the performance of the proposed method improved in the presence of outliers. The proposed method also detected an additional 18 outlying DE genes from a real mouse RNA-seq dataset that were not detected by traditional methods. Using the KEGG pathway and gene ontology analysis results we reveal that these genes may be biomarkers, which require validation in a wet lab. Conclusions: Our proposal is to apply the proposed method for biomarker identification from other RNA-seq data.