title: Introduction to statistical methods for microRNA analysis.
abstract: MicroRNA profiling is an important task to investigate miRNA functions and recent technologies such as microarray, single nucleotide polymorphism (SNP), quantitative real-time PCR (qPCR), and next-generation sequencing (NGS) have played a major role for miRNA analysis. In this chapter, we give an overview on statistical approaches for gene expressions, SNP, qPCR, and NGS data including preliminary analyses (pre-processing, differential expression, classification, clustering, exploration of interactions, and the use of ontologies). Our goal is to outline the key approaches with a brief discussion of problems avenues for their solutions and to give some examples for real-world use. Readers will be able to understand the different data formats (expression levels, sequences etc.) and they will be able to choose appropriate methods for their own research and application. On the other hand, we give brief notes on most popular tools/packages for statistical genetic analysis. This chapter aims to serve as a brief introduction to different kinds of statistical methods and also provides an extensive source of references.
authors.affiliation: Faculty of Medicine, Department of Biostatistics, Hacettepe University, Ankara, Turkey.
authors.forename: Gökmen
authors.initials: G
authors.lastname: Zararsiz
authors.forename: Erdal
authors.initials: E
authors.lastname: Coşgun
mesh_terms: D001185:Artificial Intelligence; D015870:Gene Expression; D035683:MicroRNAs / Q000032:analysis* / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide; D060888:Real-Time Polymerase Chain Reaction; D060388:Support Vector Machine
chemical_list: D035683:MicroRNAs