English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We present a new modelling approach for longitudinal count data that ismotivated by the increasing availability of longitudinal RNA-sequencingexperiments. The distribution of RNA-seq counts typically exhibitsoverdispersion, zero-inflation and heavy tails; moreover, in longitudinaldesigns repeated measurements from the same subject are typically (positively)correlated. We propose a generalized linear mixed model based on thePoisson-Tweedie distribution that can flexibly handle each of theaforementioned features of longitudinal overdispersed counts. We develop acomputational approach to accurately evaluate the likelihood of the proposedmodel and to perform maximum likelihood estimation. Our approach is implementedin the R package ptmixed, which can be freely downloaded from CRAN. We assessthe performance of ptmixed on simulated data and we present an application to adataset with longitudinal RNA-sequencing measurements from healthy anddystrophic mice. The applicability of the Poisson-Tweedie mixed-effects modelis not restricted to longitudinal RNA-seq data, but it extends to any scenariowhere non-independent measurements of a discrete overdispersed responsevariable are available.

Poisson-Tweedie mixed-effects model: a flexible approach for the analysis of longitudinal RNA-seq data