ArticleCell & bioscience2012
Statistical methods for identifying differentially expressed genes in RNA-Seq experiments.
Article in Cell & bioscience, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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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Who cites it
26 citing papers in PubMed.
- Identification of differentially expressed genes in RNA-seq data via semi-rigid orthogonal sparse KL-NMTF.BMC bioinformatics · 2026Article
- Molecular signatures and machine learning driven stress biomarkers for rainbow trout aquaculture and climate adaptation.Scientific reports · 2025Article
- The offonome reveals on and off states of gene expression near the detection limit of RNA-seq.Scientific reports · 2025Article
- Whole-blood transcriptomic analysis reveals preoperative complement inhibitor deficiencies linked to postoperative delirium.Molecular psychiatry · 2025Article
- KRAS mutants confer platinum resistance by regulating ALKBH5 posttranslational modifications in lung cancer.The Journal of clinical investigation · 2025Article
- Transcription Factor-Wide Association Studies to Identify Functional SNPs in Alzheimer's Disease.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
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- Human Coronavirus Infection Reorganizes Spatial Genomic Architecture in Permissive Lung Cells.Research square · 2024Article
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- System-wide transcriptome damage and tissue identity loss in COVID-19 patients.Cell reports. Medicine · 2022Article
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- Using EuGeneCiD and EuGeneCiM computational tools for synthetic biology.STAR protocols · 2021Article
- Genes and regulatory mechanisms associated with experimentally-induced bovine respiratory disease identified using supervised machine learning methodology.Scientific reports · 2021Article
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- TGF-β loaded exosome enhances ischemic wound healingTheranostics · 2021Article
- Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments.PLoS computational biology · 2020Article
- RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes.BMC genomics · 2018Article
- Advances in Transcriptomics: Investigating Cardiovascular Disease at Unprecedented Resolution.Circulation research · 2018Review
- Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq.BMC genomics · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA sequencing (RNA-Seq) is rapidly replacing microarrays for profiling gene expression with much improved accuracy and sensitivity. One of the most common questions in a typical gene profiling experiment is how to identify a set of transcripts that are differentially expressed between different experimental conditions. Some of the statistical methods developed for microarray data analysis can be applied to RNA-Seq data with or without modifications. Recently several additional methods have been developed specifically for RNA-Seq data sets. This review attempts to give an in-depth review of these statistical methods, with the goal of providing a comprehensive guide when choosing appropriate metrics for RNA-Seq statistical analyses.
Identifiers
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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.