metadata

Minimal metadata for paired longitudinal response analysis.

datasets

Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine <doi:10.1038/s41591-022-01789-0>.

Myles Lewis

glmmSeq

General Linear Mixed Models for Gene-Level Differential
Expression

Katriona Goldmann

Elisabetta Sciacca

Cankut Cubuk

Anna Surace

metadata function

A data frame<dl>
<dt>PATID</dt>
<dd>Id for matching patients</dd><dt>Timepoint</dt>
<dd>timepoints</dd><dt>EULAR_6m</dt>
<dd>response data</dd>
</dl>

Format

Minimal metadata from PEAC — metadata

A data frame<dl>
<dt>PATID</dt>
<dd>Id for matching patients</dd>

<dt>Timepoint</dt>
<dd>timepoints</dd>

<dt>EULAR_6m</dt>
<dd>response data</dd>


</dl>

metadata: Minimal metadata from PEAC

Description

Usage

Arguments

Format