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/

Zendy Plus

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

Zendy Tools

Zendy Open

This paper investigates the theoretical and empirical performance ofFisher-Pitman-type permutation tests for assessing the equality of unknownPoisson mixture distributions. Building on nonparametric maximum likelihoodestimators (NPMLEs) of the mixing distribution, these tests are theoreticallyshown to be able to adapt to complicated unspecified structures of count dataand also consistent against their corresponding ANOVA-type alternatives; thelatter is a result in parallel to classic claims made by Robinson (Robinson,1973). The studied methods are then applied to a single-cell RNA-seq dataobtained from different cell types from brain samples of autism subjects andhealthy controls; empirically, they unveil genes that are differentiallyexpressed between autism and control subjects yet are missed using commontests. For justifying their use, rate optimality of NPMLEs is also establishedin settings similar to nonparametric Gaussian (Wu and Yang, 2020a) and binomialmixtures (Tian et al., 2017; Vinayak et al., 2019).

Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics