ArticleBMC bioinformatics2010
Empirical bayes analysis of sequencing-based transcriptional profiling without replicates.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
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- Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation.BMC genomics · 2024Article
- Influence of light on the infection of Aureococcus anophagefferens CCMP 1984 by a "giant virus".PloS one · 2020Article
- Transcriptional Shifts Highlight the Role of Nutrients in Harmful Brown Tide Dynamics.Frontiers in microbiology · 2019Article
- Conserved Transcriptional Responses to Nutrient Stress in Bloom-Forming Algae.Frontiers in microbiology · 2017Article
- Functional group-specific traits drive phytoplankton dynamics in the oligotrophic ocean.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Expression profiling of the bloom-forming cyanobacterium Nodularia CCY9414 under light and oxidative stress conditions.The ISME journal · 2015Article
- Bayesian Hierarchical Model for Differential Gene Expression Using RNA-seq Data.Statistics in biosciences · 2015Article
- Metatranscriptome analyses indicate resource partitioning between diatoms in the field.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Variations in the non-coding transcriptome as a driver of inter-strain divergence and physiological adaptation in bacteria.Scientific reports · 2015Article
- RNA sequencing of Populus x canadensis roots identifies key molecular mechanisms underlying physiological adaption to excess zinc.PloS one · 2015Article
- Differential Expression Analysis in RNA-Seq by a Naive Bayes Classifier with Local Normalization.BioMed research international · 2015Article
- Getting the most out of RNA-seq data analysis.PeerJ · 2015Article
- Transcripts and MicroRNAs Responding to Salt Stress in Musa acuminata Colla (AAA Group) cv. Berangan Roots.PloS one · 2015Article
- De novo assembly of Aureococcus anophagefferens transcriptomes reveals diverse responses to the low nutrient and low light conditions present during blooms.Frontiers in microbiology · 2014Article
- The analytical landscape of static and temporal dynamics in transcriptome data.Frontiers in genetics · 2014Review
- Transcriptomic analysis of metabolic function in the giant kelp, Macrocystis pyrifera, across depth and season.The New phytologist · 2013Article
- Comparative studies of differential gene calling using RNA-Seq data.BMC bioinformatics · 2013Article
- Eri1 regulates microRNA homeostasis and mouse lymphocyte development and antiviral function.Blood · 2012Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses.
resultsWe propose an empirical Bayes method, titled Analysis of Sequence Counts (ASC), to detect differential expression based on sequencing technology. ASC borrows information across sequences to establish prior distribution of sample variation, so that biological variation can be accounted for even when replicates are not available. Compared to current approaches that simply tests for equality of proportions in two samples, ASC is less biased towards highly expressed sequences and can identify more genes with a greater log fold change at lower overall abundance.
conclusionsASC unifies the biological and statistical significance of differential expression by estimating the posterior mean of log fold change and estimating false discovery rates based on the posterior mean. The implementation in R is available at http://www.stat.brown.edu/Zwu/research.aspx.
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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.