Package: glmmSeq 0.5.5
glmmSeq: General Linear Mixed Models for Gene-Level Differential Expression
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>.
Authors:
glmmSeq_0.5.5.tar.gz
glmmSeq_0.5.5.zip(r-4.5)glmmSeq_0.5.5.zip(r-4.4)glmmSeq_0.5.5.zip(r-4.3)
glmmSeq_0.5.5.tgz(r-4.4-any)glmmSeq_0.5.5.tgz(r-4.3-any)
glmmSeq_0.5.5.tar.gz(r-4.5-noble)glmmSeq_0.5.5.tar.gz(r-4.4-noble)
glmmSeq_0.5.5.tgz(r-4.4-emscripten)glmmSeq_0.5.5.tgz(r-4.3-emscripten)
glmmSeq.pdf |glmmSeq.html✨
glmmSeq/json (API)
NEWS
| # Install 'glmmSeq' in R: |
| install.packages('glmmSeq', repos = c('https://myles-lewis.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/myles-lewis/glmmseq/issues
bioinformaticsdifferential-gene-expressiongene-expressionglmmmixed-modelstranscriptomics
Last updated 2 years agofrom:8a473e1565. Checks:OK: 5 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 18 2024 |
| R-4.5-win | NOTE | Sep 18 2024 |
| R-4.5-linux | NOTE | Sep 18 2024 |
| R-4.4-win | OK | Sep 18 2024 |
| R-4.4-mac | OK | Sep 18 2024 |
| R-4.3-win | OK | Sep 18 2024 |
| R-4.3-mac | OK | Sep 18 2024 |
Exports:fcPlotggmodelPlotglmmQvalsglmmRefitglmmSeqlmmSeqmaPlotmodelPlot
Dependencies:abindaskpassbackportsbase64encbootbroombslibcachemcarcarDataclicolorspacecorrplotcowplotcpp11crosstalkcurldata.tableDerivdigestdoBydplyrevaluatefansifarverfastmapfontawesomefsgenericsggplot2ggpubrggrepelggsciggsignifglmmTMBgluegridExtragtablehighrhtmltoolshtmlwidgetshttrisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelme4lmerTestmagrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivopensslpbapplypbkrtestpbmcapplypillarpkgconfigplotlyplyrpolynompromisespurrrquantregqvalueR6rappdirsRColorBrewerRcppRcppEigenreshape2rlangrmarkdownrstatixsassscalesSparseMstringistringrsurvivalsystibbletidyrtidyselecttinytexTMButf8vctrsviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Plotly or ggplot fold change plots | fcPlot |
| Mixed model effects plot using ggplot2 | ggmodelPlot |
| Glmm Sequencing qvalues | glmmQvals |
| Refit mixed effects model | glmmRefit glmmRefit.GlmmSeq glmmRefit.lmmSeq |
| GLMM with negative binomial distribution for sequencing count data | glmmSeq |
| An S4 class to define the glmmSeq output | GlmmSeq-class |
| Linear mixed models for data matrix | lmmSeq |
| An S4 class to define the lmmSeq output | lmmSeq-class |
| MA plots | maPlot |
| Minimal metadata from PEAC | metadata |
| Mixed model effects plot | modelPlot |
| Summarise a 'glmmSeq'/'lmmSeq' object | summary.GlmmSeq summary.lmmSeq |
| TPM count data from PEAC | tpm |
