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 differences. The high-dimensionality of the data with few biological replicates make it challenging to accurately detect those samples, and this issue is not well studied in the literature currently. Robust statistics is a family of theories and techniques aim to detect the outliers by first fitting the majority of the data and then flagging data points that deviate from it. Robust statistics have been widely used in multivariate data analysis for outlier detection in chemometrics and engineering. Here w

SUBMITTER: Chen X 

PROVIDER: S-EPMC7324992 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature