Package: timeOmics 1.19.0
Antoine Bodein
timeOmics: Time-Course Multi-Omics data integration
timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step.
Authors:
timeOmics_1.19.0.tar.gz
timeOmics_1.19.0.zip(r-4.5)timeOmics_1.19.0.zip(r-4.4)timeOmics_1.19.0.zip(r-4.3)
timeOmics_1.19.0.tgz(r-4.4-any)timeOmics_1.19.0.tgz(r-4.3-any)
timeOmics_1.19.0.tar.gz(r-4.5-noble)timeOmics_1.19.0.tar.gz(r-4.4-noble)
timeOmics_1.19.0.tgz(r-4.4-emscripten)timeOmics_1.19.0.tgz(r-4.3-emscripten)
timeOmics.pdf |timeOmics.html✨
timeOmics/json (API)
| # Install 'timeOmics' in R: |
| install.packages('timeOmics', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/abodein/timeomics/issues
On BioConductor:timeOmics-1.19.0(bioc 3.21)timeOmics-1.18.0(bioc 3.20)
clusteringfeatureextractiontimecoursedimensionreductionsoftwaresequencingmicroarraymetabolomicsmetagenomicsproteomicsclassificationregressionimmunooncologygenepredictionmultiplecomparisonclusterintegrationmulti-omicstime-series
Last updated 2 months agofrom:6546b5fc22. Checks:OK: 1 WARNING: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 03 2024 |
| R-4.5-win | WARNING | Dec 03 2024 |
| R-4.5-linux | WARNING | Dec 03 2024 |
| R-4.4-win | WARNING | Dec 03 2024 |
| R-4.4-mac | WARNING | Dec 03 2024 |
| R-4.3-win | WARNING | Dec 03 2024 |
| R-4.3-mac | WARNING | Dec 03 2024 |
Exports:get_demo_clusterget_demo_silhouettegetClustergetNcompgetSilhouettegetUpDownClusterlmms.filter.linesplotLongproportionalityremove.low.cvtuneCluster.block.splstuneCluster.spcatuneCluster.splsunscale
Dependencies:backportsbase64encBHBiocParallelbslibcachemcheckmateclicodetoolscolorspacecorpcorcpp11digestdplyrellipseevaluatefansifarverfastmapfontawesomeformatRfsfutile.loggerfutile.optionsgenericsggplot2ggrepelgluegridExtragsignalgtablehighrhtmltoolshtmlwidgetsigraphisobandjquerylibjsonliteknitrlabelinglambda.rlatticelifecyclelmtestmagrittrMASSMatrixmatrixStatsmemoisemgcvmimemixOmicsmunsellnlmepillarpkgconfigplyrpracmapurrrR6rappdirsrARPACKRColorBrewerRcppRcppEigenreshape2rglrlangrmarkdownRSpectrasassscalessnowstringistringrtibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyamlzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| dmatrix.spearman.dissimilarity | dmatrix.spearman.dissimilarity |
| get_demo_cluster | get_demo_cluster |
| Get data for silhouette demo | get_demo_silhouette |
| Get variable cluster from (s)PCA, (s)PLS or block.(s)PLS | getCluster |
| Get optimal number of components | getNcomp |
| Get Silhouette Coefficient from (s)PCA, (s)PLS or block.(s)PLS clusters | getSilhouette |
| Up-Down clustering | getUpDownCluster |
| Filter Linear Profiles from Linear Mixed Model output | lmms.filter.lines |
| Plot Longitudinal Profiles by Cluster | plotLong |
| Proportionality Distance | proportionality |
| Remove features with low variation | remove.low.cv |
| Feature Selection Optimization for block (s)PLS method | tuneCluster.block.spls |
| Feature Selection Optimization for sPCA method | tuneCluster.spca |
| Feature Selection Optimization for sPLS method | tuneCluster.spls |
| Unscales a scaled data.frame | unscale |
