ArticleBioinformatics (Oxford, England)2011
Length bias correction for RNA-seq data in gene set analyses.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- The effect of aldafermin expressing-Escherichia coli Nissle 1917 along with dietary change on visceral adipose tissue in MASLD mouse model.International journal of obesity (2005) · 2025Article
- Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets.Genome biology · 2023Review
- Characterizing the tumor immune microenvironment of ependymomas using targeted gene expression profiles and RNA sequencing.Cancer immunology, immunotherapy : CII · 2023Article
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- Sensitive and accurate analysis of gene expression signatures enabled by oligonucleotide-labelled cDNA.RNA biology · 2022Article
- Gene Expression Profiling of Skeletal Muscles.Genes · 2021Article
- Modelling RNA-Seq data with a zero-inflated mixture Poisson linear model.Genetic epidemiology · 2019Article
- Family-Based Quantitative Trait Meta-Analysis Implicates Rare Noncoding Variants in DENND1A in Polycystic Ovary Syndrome.The Journal of clinical endocrinology and metabolism · 2019Article
- RNA-Seq Perspectives to Improve Clinical Diagnosis.Frontiers in genetics · 2019Review
- Hierarchical Clustering of DNA k-mer Counts in RNAseq Fastq Files Identifies Sample Heterogeneities.International journal of molecular sciences · 2018Article
- Local sequence and sequencing depth dependent accuracy of RNA-seq reads.BMC bioinformatics · 2017Article
- Gene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data.BMC genomics · 2017Article
- RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting.F1000Research · 2017Article
- Gene set analysis controlling for length bias in RNA-seq experiments.BioData mining · 2017Article
- A nested parallel experiment demonstrates differences in intensity-dependence between RNA-seq and microarrays.Nucleic acids research · 2015Article
- Whole transcriptome analysis with sequencing: methods, challenges and potential solutions.Cellular and molecular life sciences : CMLS · 2015Review
- Differential Expression Analysis in RNA-Seq by a Naive Bayes Classifier with Local Normalization.BioMed research international · 2015Article
- GSAASeqSP: a toolset for gene set association analysis of RNA-Seq data.Scientific reports · 2014Article
- RNA sequencing: from tag-based profiling to resolving complete transcript structure.Cellular and molecular life sciences : CMLS · 2014Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationNext-generation sequencing technologies are being rapidly applied to quantifying transcripts (RNA-seq). However, due to the unique properties of the RNA-seq data, the differential expression of longer transcripts is more likely to be identified than that of shorter transcripts with the same effect size. This bias complicates the downstream gene set analysis (GSA) because the methods for GSA previously developed for microarray data are based on the assumption that genes with same effect size have equal probability (power) to be identified as significantly differentially expressed. Since transcript length is not related to gene expression, adjusting for such length dependency in GSA becomes necessary.
resultsIn this article, we proposed two approaches for transcript-length adjustment for analyses based on Poisson models: (i) At individual gene level, we adjusted each gene's test statistic using the square root of transcript length followed by testing for gene set using the Wilcoxon rank-sum test. (ii) At gene set level, we adjusted the null distribution for the Fisher's exact test by weighting the identification probability of each gene using the square root of its transcript length. We evaluated these two approaches using simulations and a real dataset, and showed that these methods can effectively reduce the transcript-length biases. The top-ranked GO terms obtained from the proposed adjustments show more overlaps with the microarray results. AVAILABILITY: R scripts are at http://www.soph.uab.edu/Statgenetics/People/XCui/r-codes/.
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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.