ArticleCancer informatics2011
On differential gene expression using RNA-Seq data.
Article in Cancer informatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- An empirical Bayes method for differential expression analysis of single cells with deep generative models.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size.Frontiers in plant science · 2018Article
- Functional regression method for whole genome eQTL epistasis analysis with sequencing data.BMC genomics · 2017Article
- Gene expression changes in the salivary glands of Anopheles coluzzii elicited by Plasmodium berghei infection.Parasites & vectors · 2015Article
- Transcripts and MicroRNAs Responding to Salt Stress in Musa acuminata Colla (AAA Group) cv. Berangan Roots.PloS one · 2015Article
- The analytical landscape of static and temporal dynamics in transcriptome data.Frontiers in genetics · 2014Review
- Stormbow: A Cloud-Based Tool for Reads Mapping and Expression Quantification in Large-Scale RNA-Seq Studies.ISRN bioinformatics · 2013Article
- Time series expression analyses using RNA-seq: a statistical approach.BioMed research international · 2013Article
- Genome-wide characterization of transcriptional patterns in high and low antibody responders to rubella vaccination.PloS one · 2013Article
- β-empirical Bayes inference and model diagnosis of microarray data.BMC bioinformatics · 2012Article
- Accuracy of RNA-Seq and its dependence on sequencing depth.BMC bioinformatics · 2012Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationRNA-Seq is a novel technology that provides read counts of RNA fragments in each gene, including the mapped positions of each read within each gene. Besides many other applications it can be used to detect differentially expressed genes. Most published methods collapse the position-level read data into a single gene-specific expression measurement. Statistical inference proceeds by modeling these gene-level expression measurements.
resultsWe present a Bayesian method of calling differential expression (BM-DE) that directly models the position-level read counts. We demonstrate the potential advantage of the BM-DE method compared to existing approaches that rely on gene-level aggregate data. An important additional feature of the proposed approach is that BM-DE can be used to analyze RNA-Seq data from experiments without biological replicates. This becomes possible since the approach works with multiple position-level read counts for each gene. We demonstrate the importance of modeling for position-level read counts with a yeast data set and a simulation study. AVAILABILITY: A public domain R package is available from http://odin.mdacc.tmc.edu/~ylji/BMDE/.
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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.