Evidence map›Paper›PMID 36945891›Full record

ArticleBioinformatics (Oxford, England)2023

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

Edin Salkovic, Mohammad Amin Sadeghi, Abdelkader Baggag, Ahmed Gamal Rashed Salem, Halima Bensmail

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Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

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9citing papers in PubMed
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3 · Its place in the literature

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9 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Edin SalkovicCollege of Science Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Mohammad Amin SadeghiQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Abdelkader BaggagQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.ORCID 0000-0001-8742-5519
Ahmed Gamal Rashed SalemDepartment of Computer Sciences, College of Engineering, Qatar University, P.O. Box: 2713, Doha, Qatar.
Halima BensmailQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.ORCID 0000-0001-6700-5752

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationFinding 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 IMPLEMENTATION: The code for OutSingle is available at https://github.com/esalkovic/outsingle.

Indexed as

RNABase SequenceExome SequencingGene ExpressionRNA-SeqSequence Analysis, RNARNA

Identifiers

PMID36945891
PMCPMC10089674

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.