lmerApply

Fit an lmer model for an individual gene

internal

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

lmerApply function

<dl><dt>geneList</dt>
<dd>List with gene expression and dispersion</dd>
<dt>fullFormula</dt>
<dd>the model formula. For more information of formula
structure see <code><a href="/link/lme4%3A%3Aglmer()?package=glmmSeq&version=0.2.0" data-mini-rdoc="glmmSeq::lme4::glmer()">lme4::glmer()</a></code></dd>
<dt>data</dt>
<dd>The sample metadata.</dd>
<dt>control</dt>
<dd>the lmer control (default = lmerControl()). For more information see
<code><a href="/link/lme4%3A%3AlmerControl()?package=glmmSeq&version=0.2.0" data-mini-rdoc="glmmSeq::lme4::lmerControl()">lme4::lmerControl()</a></code>.</dd>
<dt>modelData</dt>
<dd>Expanded design matrix</dd>
<dt>designMatrix</dt>
<dd>The design matrix</dd>
<dt>offset</dt>
<dd>this can be used to specify an a priori known component to be
included in the linear predictor during fitting. For more information see
<code><a href="/link/lme4%3A%3Almer()?package=glmmSeq&version=0.2.0" data-mini-rdoc="glmmSeq::lme4::lmer()">lme4::lmer()</a></code>.</dd>
<dt>...</dt>
<dd>Other parameters to pass to
<code><a href="/link/lme4%3A%3Almer()?package=glmmSeq&version=0.2.0" data-mini-rdoc="glmmSeq::lme4::lmer()">lme4::lmer()</a></code></dd></dl>

Arguments

Fit an lmer model for an individual gene — lmerApply

<dl>

<dt>geneList</dt>
<dd>List with gene expression and dispersion</dd>


<dt>fullFormula</dt>
<dd>the model formula. For more information of formula
structure see <code><a href='https://rdrr.io/pkg/lme4/man/glmer.html'>lme4::glmer()</a></code></dd>


<dt>data</dt>
<dd>The sample metadata.</dd>


<dt>control</dt>
<dd>the lmer control (default = lmerControl()). For more information see
<code><a href='https://rdrr.io/pkg/lme4/man/lmerControl.html'>lme4::lmerControl()</a></code>.</dd>


<dt>modelData</dt>
<dd>Expanded design matrix</dd>


<dt>designMatrix</dt>
<dd>The design matrix</dd>


<dt>offset</dt>
<dd>this can be used to specify an a priori known component to be
included in the linear predictor during fitting. For more information see
<code><a href='https://rdrr.io/pkg/lme4/man/glmer.html'>lme4::lmer()</a></code>.</dd>


<dt>...</dt>
<dd>Other parameters to pass to
<code><a href='https://rdrr.io/pkg/lme4/man/glmer.html'>lme4::lmer()</a></code></dd>

</dl>

lmerApply: Fit an lmer model for an individual gene

Description

Usage

Value

Arguments