summary.lmmSeq

summary.GlmmSeq

Summarise results from glmmSeq or lmmSeq analysis

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

summary.lmmSeq function

<dl><dt>object</dt>
<dd>an object of class <code>"GlmmSeq"</code> or <code>"lmmSeq"</code></dd>
<dt>gene</dt>
<dd>an optional character value specifying a single gene whose
results are summarised</dd>
<dt>digits</dt>
<dd>integer, used for number formatting</dd>
<dt>...</dt>
<dd>arguments to be passed to other methods</dd></dl>

Arguments

Summarise a 'glmmSeq'/'lmmSeq' object — summary.lmmSeq

<dl>

<dt>object</dt>
<dd>an object of class <code>"GlmmSeq"</code> or <code>"lmmSeq"</code></dd>


<dt>gene</dt>
<dd>an optional character value specifying a single gene whose
results are summarised</dd>


<dt>digits</dt>
<dd>integer, used for number formatting</dd>


<dt>...</dt>
<dd>arguments to be passed to other methods</dd>

</dl>

summary.lmmSeq: Summarise a 'glmmSeq'/'lmmSeq' object

Description

Usage

Value

Arguments

See Also