OutSingle: a novel method of detecting and injecting outliers in RNA-Seq count data using the optimal hard threshold for singular values

Author:

Salkovic Edin1,Sadeghi Mohammad Amin2,Baggag Abdelkader2ORCID,Salem Ahmed Gamal Rashed3,Bensmail Halima2ORCID

Affiliation:

1. College of Science Engineering, Hamad Bin Khalifa University , Doha, Qatar

2. Qatar Computing Research Institute, Hamad Bin Khalifa University , Doha, Qatar

3. Department of Computer Sciences, College of Engineering, Qatar University , P.O. Box: 2713 , Doha, Qatar

Abstract

AbstractMotivationFinding outliers in RNA-sequencing (RNA-Seq) gene expression (GE) can help in identifying genes that are aberrant and cause Mendelian disorders. Recently developed models for this task rely on modeling RNA-Seq GE data using the negative binomial distribution (NBD). However, some of those models either rely on procedures for inferring NBD’s parameters in a nonbiased way that are computationally demanding and thus make confounder control challenging, while others rely on less computationally demanding but biased procedures and convoluted confounder control approaches that hinder interpretability.ResultsIn this article, we present OutSingle (Outlier detection using Singular Value Decomposition), an almost instantaneous way of detecting outliers in RNA-Seq GE data. It uses a simple log-normal approach for count modeling. For confounder control, it uses the recently discovered optimal hard threshold (OHT) method for noise detection, which itself is based on singular value decomposition (SVD). Due to its SVD/OHT utilization, OutSingle’s model is straightforward to understand and interpret. We then show that our novel method, when used on RNA-Seq GE data with real biological outliers masked by confounders, outcompetes the previous state-of-the-art model based on an ad hoc denoising autoencoder. Additionally, OutSingle can be used to inject artificial outliers masked by confounders, which is difficult to achieve with previous approaches. We describe a way of using OutSingle for outlier injection and proceed to show how OutSingle outperforms its competition on 16 out of 18 datasets that were generated from three real datasets using OutSingle’s injection procedure with different outlier types and magnitudes. Our methods are applicable to other types of similar problems involving finding outliers in matrices under the presence of confounders.Availability and implementationThe code for OutSingle is available at https://github.com/esalkovic/outsingle.

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference27 articles.

1. Differential expression analysis for sequence count data;Anders;Genome Biol,2010

2. Mendelian gene discovery: fast and furious with no end in sight;Bamshad;Am J Hum Genet,2019

3. The control of the false discovery rate in multiple testing under dependency;Benjamini;Ann Stat,2001

4. OUTRIDER: a statistical method for detecting aberrantly expressed genes in RNA sequencing data;Brechtmann;Am J Hum Genet,2018

5. Data-Driven Science and Engineering

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