title: An Iterative Leave-One-Out Approach to Outlier Detection in RNA-Seq Data.
abstract: The discrete data structure and large sequencing depth of RNA sequencing (RNA-seq) experiments can often generate outlier read counts in one or more RNA samples within a homogeneous group. Thus, how to identify and manage outlier observations in RNA-seq data is an emerging topic of interest. One of the main objectives in these research efforts is to develop statistical methodology that effectively balances the impact of outlier observations and achieves maximal power for statistical testing. To reach that goal, strengthening the accuracy of outlier detection is an important precursor. Current outlier detection algorithms for RNA-seq data are executed within a testing framework and may be sensitive to sparse data and heavy-tailed distributions. Therefore, we propose a univariate algorithm that utilizes a probabilistic approach to measure the deviation between an observation and the distribution generating the remaining data and implement it within in an iterative leave-one-out design strategy. Analyses of real and simulated RNA-seq data show that the proposed methodology has higher outlier detection rates for both non-normalized and normalized negative binomial distributed data.
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, FDA, Jefferson, Arkansas, United States of America.
authors.forename: Nysia I
authors.initials: NI
authors.lastname: George
authors.affiliation: Division of Neurotoxicology, National Center for Toxicological Research, FDA, Jefferson, Arkansas, United States of America.
authors.forename: John F
authors.initials: JF
authors.lastname: Bowyer
authors.affiliation: Joint Bioinformatics Graduate Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
authors.forename: Nathaniel M
authors.initials: NM
authors.lastname: Crabtree
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, FDA, Jefferson, Arkansas, United States of America.
authors.forename: Ching-Wei
authors.initials: CW
authors.lastname: Chang
mesh_terms: D004247:DNA / Q000235:genetics*; D030561:Databases, Nucleic Acid; D012313:RNA / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D012313:RNA; D004247:DNA