ArticlePeerJ2019
consensusDE: an R package for assessing consensus of multiple RNA-seq algorithms with RUV correction.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 53 citations in OpenAlex.
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- Highly dynamic inflammatory and excitability transcriptional profiles in hippocampal CA1 following status epilepticus.Scientific reports · 2023Article
- A case-control comparison of acute-phase peripheral blood gene expression in participants diagnosed with minor ischaemic stroke or stroke mimics.Human genomics · 2023Article
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- Intrauterine Smoke Exposure, microRNA Expression during Human Lung Development, and Childhood Asthma.International journal of molecular sciences · 2023Article
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- The Australasian dingo archetype:bioRxiv : the preprint server for biology · 2023Article
- Identifying cancer driver genes in individual tumours.Computational and structural biotechnology journal · 2023Review
- HYAL3 as a potential novel marker of BLCA patient prognosis.BMC genomic data · 2022Article
- The Australian dingo is an early offshoot of modern breed dogs.Science advances · 2022Article
- A simple guide to de novo transcriptome assembly and annotation.Briefings in bioinformatics · 2022Review
- Colchicine Does Not Reduce Abdominal Aortic Aneurysm Growth in a Mouse Model.Cardiovascular therapeutics · 2022Article
- Bioinformatic Challenges Detecting Genetic Variation in Precision Medicine Programs.Frontiers in medicine · 2022Review
- Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extensive evaluation of RNA-seq methods have demonstrated that no single algorithm consistently outperforms all others. Removal of unwanted variation (RUV) has also been proposed as a method for stabilizing differential expression (DE) results. Despite this, it remains a challenge to run multiple RNA-seq algorithms to identify significant differences common to multiple algorithms, whilst also integrating and assessing the impact of RUV into all algorithms. consensusDE was developed to automate the process of identifying significant DE by combining the results from multiple algorithms with minimal user input and with the option to automatically integrate RUV. consensusDE only requires a table describing the sample groups, a directory containing BAM files or preprocessed count tables and an optional transcript database for annotation. It supports merging of technical replicates, paired analyses and outputs a compendium of plots to guide the user in subsequent analyses. Herein, we assess the ability of RUV to improve DE stability when combined with multiple algorithms and between algorithms, through application to real and simulated data. We find that, although RUV increased fold change stability between algorithms, it demonstrated improved FDR in a setting of low replication for the intersect, the effect was algorithm specific and diminished with increased replication, reinforcing increased replication for recovery of true DE genes. We finish by offering some rules and considerations for the application of RUV in a consensus-based setting. consensusDE is freely available, implemented in R and available as a Bioconductor package, under the GPL-3 license, along with a comprehensive vignette describing functionality: http://bioconductor.org/packages/consensusDE/.
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Registered trials
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.