Package: TiDEomics 0.99.1

Tianen He

TiDEomics: Time-course Differential Expression analysis of omics data

TiDEomics provides tools for time-course omics data analysis, focusing on differential expression across multiple experimental groups (>2 conditions). The package implements workflows for quality control, data processing, and analysis of temporal patterns, enabling integrated analysis of all groups in addition to pairwise comparisons. It supports datasets with missing values, including mass spectrometry-based proteomics, and operates on SummarizedExperiment objects to ensure compatibility with the Bioconductor ecosystem.

Authors:Tianen He [aut, cre]

TiDEomics_0.99.1.tar.gz
TiDEomics_0.99.1.zip(r-4.7)TiDEomics_0.99.1.zip(r-4.6)TiDEomics_0.99.1.zip(r-4.5)
TiDEomics_0.99.1.tgz(r-4.6-any)TiDEomics_0.99.1.tgz(r-4.5-any)
TiDEomics_0.99.1.tar.gz(r-4.7-any)TiDEomics_0.99.1.tar.gz(r-4.6-any)
TiDEomics_0.99.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
TiDEomics/json (API)
NEWS

# Install 'TiDEomics' in R:
install.packages('TiDEomics', repos = c('https://biocstaging.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 CRAN:

Conda:

softwaregeneexpressiondifferentialexpressionproteomicstranscriptomicstimecoursemultiplecomparisonpathwaysvisualizationmassspectrometryqualitycontrol

3.48 score 2 scripts 47 exports 239 dependencies

Last updated from:6f1fffe160. Checks:1 NOTE, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE297
linux-devel-x86_64OK880
source / vignettesOK582
linux-release-x86_64OK850
macos-release-arm64OK1006
macos-oldrel-arm64OK846
windows-develOK851
windows-releaseOK890
windows-oldrelOK819
wasm-releaseOK300

Exports:calc_feature_propertycalc_mean_sdcreate_inputDE_between_groupDE_between_timedecomp_varianceenrichGO_listenrichGO_rankenrichR_listextract_segment_trendsget_custom_palettegroup_specific_featuresimpute_groupsmerge_groupsmerge_replicatesnormalise_to_startplot_breakpointsplot_cor_matrixplot_cvplot_DE_between_timeplot_distributionplot_GOplot_IDplot_missingplot_modules_hplot_modules_vplot_pcaplot_pca_3Dplot_pca_arrowsplot_pca_by_groupplot_segmentsplot_trendplot_umapplot_umap_by_groupplot_varianceplot_volcanoplot_WGCNAprepare_WGCNArun_Trendyrun_WGCNAset_custom_palettesplit_groupssummarise_feature_propertysummarise_module_patternsummarise_Trendytheme_customWGCNA_module

Dependencies:abindaisdkAnnotationDbiapeaplotaskpassassortheadbackportsbase64encbeachmatBHBiobaseBiocGenericsBiocParallelBiocSingularBiostringsbitbit64bitopsblobbootbroombslibcachemcallrcarcarDatacaToolscheckmatecirclizecliclueclusterclusterProfilercodetoolscolorspacecommonmarkComplexHeatmapcorrplotcowplotcpp11crayoncrosstalkcurldata.tableDBIDelayedArrayDelayedMatrixStatsDerivdigestdoBydoParallelDOSEdplyrdqrngDTdynamicTreeCutenrichitenrichplotevaluatefarverfastclusterfastmapfontawesomefontBitstreamVerafontLiberationfontquiverforeachforecastforeignformatRFormulafracdifffsfutile.loggerfutile.optionsgdtoolsgenericsGenomicRangesGetoptLongggforceggfunggh4xggiraphggnewscaleggplot2ggplotifyggpubrggrepelggridgesggsciggsignifggtangleggtreeGlobalOptionsglueGO.dbGOSemSimgplotsgridExtragridGraphicsgsongtablegtoolsherehighrHmischtmlTablehtmltoolshtmlwidgetshttpuvhttrhttr2igraphimputeIRangesirlbaisobanditeratorsjquerylibjsonliteKEGGRESTKernSmoothknitrlabelinglambda.rlaterlatticelazyevallifecyclelimmalme4lmtestmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemgcvmicrobenchmarkmimeminqamodelrnlmenloptrnnetnumDerivopensslotelpatchworkpbapplypbkrtestPCAtoolspillarpkgconfigplyrpngpolyclippolynompreprocessCoreprocessxpromisespspurrrquantregqvalueR6randtestsrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppTOMLRdpackreformulasreshape2reticulaterjsonrlangrmarkdownrpartrprojrootRSpectraRSQLiterstatixrstudioapirsvdS4ArraysS4VectorsS7sassScaledMatrixscalesscatterpiesegmentedSeqinfoshapeshinyshinyFilessitmosnowsourcetoolsSparseArraySparseMsparseMatrixStatsstatmodstringistringrSummarizedExperimentsurvivalsyssystemfontstibbletidydrtidyrtidyselecttidytreetimeDatetinytextreeioTrendytweenrumapurcautf8vctrsviridisLiteWGCNAwithrxfunxtableXVectoryamlyulab.utilszoo

TiDEomics Tutorial

Tianen He

Rendered fromTiDEomics.Rmdusingknitr::rmarkdownon Jun 09 2026.

Last update: 2026-06-09
Started: 2026-04-29

Readme and manuals

Help Manual

Help pageTopics
Calculate feature propertycalc_feature_property
Calculate mean and SDcalc_mean_sd
Create objectcreate_input
DE between groupsDE_between_group
DE between time pointsDE_between_time
Variance decompositiondecomp_variance
GO enrichment with gene setsenrichGO_list
GO enrichment with ranked gene listenrichGO_rank
Gene-set enrichment via enrichRenrichR_list
'run_WGCNA()' output object for runnable examplesexample_net
SummarizedExperiment object for runnable examplesexample_obj
'run_Trendy' output object for runnable examplesexample_res_list
Extract feature trendsextract_segment_trends
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 time 0normalise_to_start
Plot breakpoint distributionplot_breakpoints
Plot correlation matrixplot_cor_matrix
Plot coefficient of variation (CV)plot_cv
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
Plot segmented regressionplot_segments
Plot feature abundance over timeplot_trend
Plot UMAPplot_umap
Plot UMAP by groupplot_umap_by_group
Plot variance decompositionplot_variance
Volcano plot of DE resultsplot_volcano
Plot WGCNA resultsplot_WGCNA
Prepare data and choose power for WGCNAprepare_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 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