Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments | Zendy

Nuha Bintayyash | Zendy; Sokratia Georgaka | Zendy; St. John | Zendy; Sumon Ahmed | Zendy; Alexis Boukouvalas | Zendy; James Hensman | Zendy; Magnus Rattray | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments

Author(s) -

Nuha Bintayyash,

Sokratia Georgaka,

St. John,

Sumon Ahmed,

Alexis Boukouvalas,

James Hensman,

Magnus Rattray

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab486

Subject(s) - negative binomial distribution , count data , parametric statistics , computer science , rna seq , regression , spatial analysis , semiparametric model , gaussian process , regression analysis , data mining , statistics , gaussian , computational biology , transcriptome , mathematics , biology , gene expression , machine learning , gene , genetics , poisson distribution , physics , quantum mechanics

The negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modelling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research