<p>Transcripts Per Million (TPM) count data for PEAC synovial
biopsies.</p>

datasets

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

tpm function

<p>An object of class <code>data.frame</code> with 50 rows and 149 columns.</p>

Format

TPM count data from PEAC — tpm

TPM count data from PEAC

tpm: TPM count data from PEAC

Description

Usage

Arguments

Format