Probabilistic outlier identification for RNA sequencing generalized linear models

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Probabilistic outlier identification for RNA sequencing generalized linear models

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Probabilistic outlier identification for RNA sequencing generalized linear models

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/LM2018131" target="_blank" >LM2018131: Česká národní infrastruktura pro biologická data</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

NAR Genomics and Bioinformatics

ISSN

2631-9268

e-ISSN

2631-9268

Svazek periodika

3

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

lqab005

Kód UT WoS článku

000698594000014

EID výsledku v databázi Scopus

2-s2.0-85110061288