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Probabilistic outlier identification for RNA sequencing generalized linear models

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388971%3A_____%2F21%3A00547151" target="_blank" >RIV/61388971:_____/21:00547151 - isvavai.cz</a>

  • Result on the web

    <a href="https://academic.oup.com/nargab/article/3/1/lqab005/6155871" target="_blank" >https://academic.oup.com/nargab/article/3/1/lqab005/6155871</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1093/nargab/lqab005" target="_blank" >10.1093/nargab/lqab005</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Probabilistic outlier identification for RNA sequencing generalized linear models

  • Original language description

    Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10602 - Biology (theoretical, mathematical, thermal, cryobiology, biological rhythm), Evolutionary biology

Result continuities

  • Project

    <a href="/en/project/LM2018131" target="_blank" >LM2018131: Czech National Infrastructure for Biological Data</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    NAR Genomics and Bioinformatics

  • ISSN

    2631-9268

  • e-ISSN

    2631-9268

  • Volume of the periodical

    3

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    9

  • Pages from-to

    lqab005

  • UT code for WoS article

    000698594000014

  • EID of the result in the Scopus database

    2-s2.0-85110061288