Evidence map›Paper›PMID 39406498›Full record

ArticleGenome research2024

Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.

Alicia Petrany, Ruoyu Chen, Shaoqiang Zhang, Yong Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Single-Cell Hi-C Technologies and Computational Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
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

4 authors.

Alicia PetranyDepartment of Biological and Biomedical Sciences, Rowan University, Glassboro, New Jersey 08028, USA.
Ruoyu ChenMoorestown High School, Moorestown, New Jersey 08057, USA.
Shaoqiang ZhangCollege of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China chenyong@rowan.edu zhangshaoqiang@tjnu.edu.cn.
Yong ChenDepartment of Biological and Biomedical Sciences, Rowan University, Glassboro, New Jersey 08028, USA; chenyong@rowan.edu zhangshaoqiang@tjnu.edu.cn.ORCID 0000-0001-6827-4321

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput sequencing (HTS) technologies have been instrumental in investigating biological questions at the bulk and single-cell levels. Comparative analysis of two HTS data sets often relies on testing the statistical significance for the difference of two negative binomial distributions (DOTNB). Although negative binomial distributions are well studied, the theoretical results for DOTNB remain largely unexplored. Here, we derive basic analytical results for DOTNB and examine its asymptotic properties. As a state-of-the-art application of DOTNB, we introduce DEGage, a computational method for detecting differentially expressed genes (DEGs) in scRNA-seq data. DEGage calculates the mean of the sample-wise differences of gene expression levels as the test statistic and determines significant differential expression by computing the

Indexed as

High-Throughput Nucleotide SequencingSingle-Cell AnalysisAnimalsBinomial DistributionComputational BiologyGene Expression ProfilingHumansMaleMiceProstatic NeoplasmsSequence Analysis, RNASoftware

Identifiers

PMID39406498
PMCPMC11529838

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Registered trials

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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.