Package: TiDEomics 0.99.6

Tianen He

TiDEomics: Time-course Differential Expression analysis of omics data

TiDEomics provides an integrated workflow for multi-group time-course omics data analysis. It uses model-based differential expression, variance decomposition, and co-expression module analysis with WGCNA to characterise temporal, group, and group-specific temporal effects. Multi-dataset and multi-omics comparisons and consensus module identification can be performed using shared features. The package includes quality control, data processing, functional enrichment, and integrated visualisation. It supports data with missing values, and operates on SummarizedExperiment objects for interoperability with the Bioconductor ecosystem.

Authors:Tianen He [aut, cre]

TiDEomics_0.99.6.tar.gz
TiDEomics_0.99.6.zip(r-4.7-any)TiDEomics_0.99.6.zip(r-4.6-any)TiDEomics_0.99.6.zip(r-4.5-any)
TiDEomics_0.99.6.tgz(r-4.6-any)TiDEomics_0.99.6.tgz(r-4.5-any)
TiDEomics_0.99.6.tar.gz(r-4.7-any)TiDEomics_0.99.6.tar.gz(r-4.6-any)
TiDEomics_0.99.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
TiDEomics/json (API)

# Install 'TiDEomics' in R:
install.packages('TiDEomics', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hte123/tideomics/issues

Pkgdown/docs site:https://hte123.github.io

Datasets:

On BioConductor:TiDEomics-0.99.5(bioc 3.24)

softwaregeneexpressiondifferentialexpressionproteomicstranscriptomicstimecoursemultiplecomparisonpathwaysvisualizationqualitycontrol

5.26 score 1 stars 6 scripts 934 downloads 61 exports 239 dependencies

Last updated from:5a15b0c166. Checks:1 WARNING, 2 ERROR, 7 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING303
linux-develERROR952
source / vignettesOK1057
linux-releaseOK1339
macos-releaseOK1430
macos-oldrelOK1410
windows-develERROR1168
windows-releaseOK1237
windows-oldrelOK935
wasm-releaseOK246

Exports:calc_feature_propertycalc_mean_sdcombine_tidecreate_inputDE_between_groupDE_between_timeDE_timecoursedecomp_varianceenrich_msigdbenrichGO_listenrichGO_rankenrichR_listextract_consensus_hubsextract_hubsextract_segment_trendsflatten_DEflatten_enrichget_custom_palettegroup_specific_featuresimpute_groupsmerge_groupsmerge_replicatesnormalise_to_startplot_breakpointsplot_cor_matrixplot_cvplot_DE_between_groupplot_DE_between_timeplot_distributionplot_GOplot_IDplot_missingplot_modules_hplot_modules_vplot_pcaplot_pca_3Dplot_pca_arrowsplot_pca_by_groupplot_scatterplot_segmentsplot_trendplot_umapplot_umap_by_groupplot_upset_hplot_upset_vplot_varianceplot_volcanoplot_WGCNAprepare_tideprepare_WGCNArun_consensus_WGCNArun_Trendyrun_WGCNAset_custom_palettesplit_groupssummarise_feature_propertysummarise_module_metricssummarise_module_patternsummarise_Trendytheme_customWGCNA_module

Dependencies:abindaisdkAnnotationDbiapeaplotaskpassassortheadbackportsbase64encbeachmatBHBiobaseBiocBaseUtilsBiocGenericsBiocParallelBiocSingularBiostringsbitbit64bitopsblobbootbroombslibcachemcallrcarcarDatacaToolscheckmatecirclizecliclueclusterclusterProfilercodetoolscolorspacecommonmarkComplexHeatmapcorrplotcowplotcpp11crayoncrosstalkcurldata.tableDBIDelayedArrayDelayedMatrixStatsDerivdigestdoBydoParallelDOSEdplyrdqrngDTdynamicTreeCutenrichitenrichplotevaluatefarverfastclusterfastmapfontawesomefontBitstreamVerafontLiberationfontquiverforeachforecastforeignformatRFormulafracdifffsfutile.loggerfutile.optionsgdtoolsgenericsGenomicRangesGetoptLongggforceggfunggh4xggiraphggnewscaleggplot2ggplotifyggpubrggrepelggridgesggsciggsignifggtangleggtreeGlobalOptionsglueGO.dbGOSemSimgplotsgridExtragridGraphicsgsongtablegtoolsherehighrHmischtmlTablehtmltoolshtmlwidgetshttpuvhttrhttr2igraphimputeIRangesirlbaisobanditeratorsjquerylibjsonliteKEGGRESTKernSmoothknitrlabelinglambda.rlaterlatticelazyevallifecyclelimmalme4lmtestmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemgcvmimeminqamodelrnlmenloptrnnetnumDerivopensslotelpatchworkpbapplypbkrtestPCAtoolspillarpkgconfigplyrpngpolyclippolynompreprocessCoreprocessxpromisespspurrrquantregqvalueR6randtestsrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppTOMLRdpackreformulasreshape2reticulaterjsonrlangrmarkdownrpartrprojrootRSpectraRSQLiterstatixrstudioapirsvdS4ArraysS4VectorsS7sassScaledMatrixscalesscatterpiesegmentedSeqinfoshapeshinyshinyFilessitmosnowsourcetoolsSparseArraySparseMsparseMatrixStatsstatmodstringistringrSummarizedExperimentsurvivalsyssystemfontstibbletidydrtidyrtidyselecttidytreetimeDatetinytextreeioTrendytweenrumapurcautf8vctrsviridisLiteWGCNAwithrxfunxtableXVectoryamlyulab.utilszoo

TiDEomics Tutorial
Introduction | Installation | Example input | Preprocessing for different data types | Workflow | Data preparation | Optional: custom color palette | Quality control | Normalisation to starting time point | Splitting groups and merging replicates | Sample relationships (correlation, PCA, UMAP) | Pairwise differential expression | Model-based time course differential expression | Feature properties and classification | Segmented regression analysis | Variance decomposition | WGCNA (co-expression modules) | Functional enrichment | Integrated visualisation of WGCNA modules and functional enrichment | Universal: plot features of interest | One-step preprocessing with prepare_tide() | Interoperability within Bioconductor ecosystem | Common usage scenarios | Session information | References
Rendered fromTiDEomics.Rmdusingknitr::rmarkdown

Last update: 2026-10-27
Started: 2026-04-29

TiDEomics Tutorial: Multi-Dataset/Multi-Omics Analysis
Introduction | Example input | Workflow | Multi-dataset preparation | Multi-dataset visualisation | Consensus WGCNA | Visualisation of co-expressed modules | Feature overlap or property comparison | Session information | References
Rendered fromTiDEomics_multi.Rmdusingknitr::rmarkdown

Last update: 2026-10-27
Started: 2026-09-16

Readme and manuals

Help Manual

Help pageTopics
Calculate feature propertycalc_feature_property
Calculate mean and SDcalc_mean_sd
Combine multiple 'prepare_tide()' outputscombine_tide
Create objectcreate_input
DE between groupsDE_between_group
DE between time pointsDE_between_time
DE over time courseDE_timecourse
Variance decompositiondecomp_variance
Gene set enrichment via MSigDBenrich_msigdb
GO enrichment with gene setsenrichGO_list
GO enrichment with ranked gene listenrichGO_rank
Gene-set enrichment via enrichRenrichR_list
'run_consensus_WGCNA()' output object for runnable examplesexample_cons
'enrichGO_list()' output object for runnable examplesexample_go
'run_WGCNA()' output object for runnable examplesexample_net
'run_WGCNA()' output object for the simulated second dataset (Exp2)example_net_2
SummarizedExperiment object for runnable examplesexample_obj
SummarizedExperiment object for the simulated second dataset (Exp2)example_obj_2
'run_Trendy' output object for runnable examplesexample_res_list
Extract hub features from consensus WGCNA networkextract_consensus_hubs
Extract hub features from WGCNA modulesextract_hubs
Extract feature trendsextract_segment_trends
Flatten nested differential expression resultsflatten_DE
Flatten nested enrichment resultsflatten_enrich
Get custom color paletteget_custom_palette
Group specific featuresgroup_specific_features
Impute missing valuesimpute_groups
Merge groups into one objectmerge_groups
Merge replicatesmerge_replicates
Normalise to starting time pointnormalise_to_start
Plot breakpoint distributionplot_breakpoints
Plot correlation matrixplot_cor_matrix
Plot coefficient of variation (CV)plot_cv
DE number between groupsplot_DE_between_group
DE number between time pointsplot_DE_between_time
Abundance distribution plotplot_distribution
Plot GO enrichmentplot_GO
Plot number of identified featuresplot_ID
Plot missing rateplot_missing
Plot modules (horizontal layout)plot_modules_h
Plot modules (vertical layout)plot_modules_v
Plot PCAplot_pca
Plot PCA in 3Dplot_pca_3D
Plot PCA with arrowsplot_pca_arrows
Plot PCA by groupplot_pca_by_group
Feature property scatter plot between two datasetsplot_scatter
Plot segmented regressionplot_segments
Plot feature abundance over timeplot_trend
Plot UMAPplot_umap
Plot UMAP by groupplot_umap_by_group
UpSet plot of feature sets with horizontally aligned enrichment termsplot_upset_h
UpSet plot of feature sets with vertically aligned enrichment dot matrixplot_upset_v
Plot variance decompositionplot_variance
Volcano plot of DE resultsplot_volcano
Plot WGCNA resultsplot_WGCNA
Prepare TiDEomics inputprepare_tide
Prepare data and choose power for WGCNAprepare_WGCNA
Consensus WGCNA across multiple datasetsrun_consensus_WGCNA
Segmented regression analysisrun_Trendy
Weighted gene co-expression network analysisrun_WGCNA
Set custom color paletteset_custom_palette
Split groupssplit_groups
Summarise feature propertiessummarise_feature_property
Summarise WGCNA module metricssummarise_module_metrics
Summarise module patternssummarise_module_pattern
Summarise Trendy resultssummarise_Trendy
Custom ggplot2 themetheme_custom
Dataset for TiDEomics tutorial, expression matrixtutorial_data
Dataset for TiDEomics tutorial, sample informationtutorial_sample_info
Convert WGCNA output to feature-module data frameWGCNA_module