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

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

Abstract Background 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 differen...

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Bibliographic Details
Main Authors: Xiaoying Chen, Bo Zhang, Ting Wang, Azad Bonni, Guoyan Zhao
Format: Article
Language:English
Published: BMC 2020-06-01
Series:BMC Bioinformatics
Subjects:
Robust principal component analysis
PcaGrid
PcaHubert
Outlier detection
RNA-seq
High-dimensional data
Online Access:http://link.springer.com/article/10.1186/s12859-020-03608-0

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http://link.springer.com/article/10.1186/s12859-020-03608-0

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