GlmmSeq-class

An S4 class to define the glmmSeq output

Using random and fixed effects to model expression at an individual gene level can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, therefore do not capture interaction terms. This package uses negative binomial mixed effects models to fit gene expression with matched samples. This is particularly useful for investigating  changes in gene expression between groups of individuals over time, as seen in: Rivellese F., Surace A.E.A.,  Goldmann K., Sciacca E., Giorli G., Cubuk C., John C.R.,  Nerviani A., Fossati-Jimack L., Thorborn G., Humby F., Bombardieri M.,  Lewis M.J.,  Pitzalis C. (2021) "Molecular Pathology Profiling of Synovial Tissue Predicts Response to Biologic Treatment in Rheumatoid Arthritis" [Manuscript in preparation].

Katriona Goldmann

glmmSeq

General Linear Mixed Models for Gene-Level Differential
Expression

Myles Lewis

Elisabetta Sciacca

Cankut Cubuk

Anna Surace

GlmmSeq-class function

<dl class="dl-horizontal">
<dt><code>formula</code></dt><dd>The model formula</dd></dl><dt><code>stats</code></dt><dd>the statistics from the glmm fit</dd><dt><code>predict</code></dt><dd>The predicted interception values</dd><dt><code>reducedFormula</code></dt><dd>The reduced formula with removed random effects</dd><dt><code>countdata</code></dt><dd>The input expression data</dd><dt><code>metadata</code></dt><dd>The input metadata</dd><dt><code>modelData</code></dt><dd>the model data for the glmm</dd><dt><code>optInfo</code></dt><dd>Information on whether the model was singular or converged</dd><dt><code>errors</code></dt><dd>Any errors</dd><dt><code>variables</code></dt><dd>The variables used in the formula</dd>

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An S4 class to define the glmmSeq output — GlmmSeq-class

An S4 class to define the glmmSeq output

GlmmSeq-class: An S4 class to define the glmmSeq output

Description

Arguments

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