Robust principal component analysis for accurate outlier sample detection in RNA-Seq data

• Published in: BMC bioinformatics Vol. 21; no. 1; pp. 269 - 20
• Main Authors: Chen, Xiaoying, Zhang, Bo, Wang, Ting, Bonni, Azad, Zhao, Guoyan
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 29.06.2020; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Animals; Bias; Cerebellum; Cerebellum - metabolism; Chemometrics; Computer simulation; Data acquisition; Data analysis; Data points; Data processing; Datasets; Experiments; Female; Functional analysis; Gene expression; Gene sequencing; Genes; High-dimensional data; Information management; Male; Mice, Knockout; Modelling; Multivariate analysis; Outlier detection; Outliers (statistics); PcaGrid; PcaHubert; Performance enhancement; Performance evaluation; Principal Component Analysis; Principal components analysis; Proto-Oncogene Proteins - genetics; Reverse Transcriptase Polymerase Chain Reaction; Reverse transcription; Ribonucleic acid; RNA; RNA sequencing; RNA-seq; RNA-Seq - methods; Robust principal component analysis; Sample variance; Samples; Simulation; Statistical analysis; Statistical methods; Transcription (Genetics); High-dimensional data; Robust principal component analysis; RNA-seq; Outlier detection; Anomaly detection; PcaGrid; PcaHubert

High throughput RNA sequencing is a powerful approach to study gene expression. Due to the complex multiple-steps protocols in data acquisition, extreme deviation of a sample from samples of the same treatment group may occur due to technical variation or true biological differences. The high-dimens...