Abstract
Gene expression indicates the amount of mRNA produced by a gene under a particular biological condition. Genes responsible for changes in biological conditions of an organism will have different gene expression values across different conditions. Gene expression analysis is useful in the domain of transcriptomic studies to analyse functions of and interactions among different molecules inside a cell. A significant analysis is that of a differential gene, that is a gene that exhibits strong change in behaviour between two or more conditions. Thus behavioural cell changes can be attributed to the differentially expressed genes. Statistical distributional properties in the read counts that constitute RNA-seq data are used for detecting the differentially expressed genes. In this paper we provide a comparison study of different tools which aid in RNA-seq based differential expression. It is important to note how the results of these tools differ and which tool provides more statistically significant results for the same.
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Joshi, R., Sarmah, R. (2020). Survey of Methods Used for Differential Expression Analysis on RNA Seq Data. In: Dehuri, S., Mishra, B., Mallick, P., Cho, SB., Favorskaya, M. (eds) Biologically Inspired Techniques in Many-Criteria Decision Making. BITMDM 2019. Learning and Analytics in Intelligent Systems, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-39033-4_21
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DOI: https://doi.org/10.1007/978-3-030-39033-4_21
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