Evidence map›Paper›PMID 33868546›Full record

ArticleBayesian analysis2018

Bayesian Analysis of RNA-Seq Data Using a Family of Negative Binomial Models.

Lili Zhao, Weisheng Wu, Dai Feng, Hui Jiang, XuanLong Nguyen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bayesian analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lili ZhaoDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
Weisheng WuDepartment of Computational Medicine & Bioinformatics, University of Michigan.
Dai FengBiometrics Research Department, Merck Research Laboratories, U.S.A.
Hui JiangDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
XuanLong NguyenDepartment of Statistics, University of Michigan, Ann Arbor.

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alexey I Nesvizhskii · 1988 to 2026
$178.2M
NCI NIH HHS P30 CA046592
6 · The paper itself

Abstract

The analysis of RNA-Seq data has been focused on three main categories, including gene expression, relative exon usage and transcript expression. Methods have been proposed independently for each category using a negative binomial (NB) model. However, counts following a NB distribution on one feature (e.g., exon) do not guarantee a NB distribution for the other two features (e.g., gene/transcript). In this paper we propose a family of Negative Binomial models, which integrates the gene, exon and transcript analysis under a coherent NB model. The proposed model easily incorporates the uncertainty of assigning reads to transcripts and simplifies substantially the estimation for the relative usage. We developed simple Gibbs sampling algorithms for the posterior inference by exploiting fully tractable closed-forms of computation via suitable conjugate priors. The proposed models were investigated under extensive simulations. Finally, we applied our model to a real data set.

Indexed as

Bayesian RNA-SeqChinese restaurant table distributionDifferential testExon usageTranscript analysis

Identifiers

PMID33868546
PMCPMC8052637

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.