Package: TDbasedUFEadv 1.7.0
TDbasedUFEadv: Advanced package of tensor decomposition based unsupervised feature extraction
This is an advanced version of TDbasedUFE, which is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. In contrast to TDbasedUFE which can perform simple the feature selection and the multiomics analyses, this package can perform more complicated and advanced features, but they are not so popularly required. Only users who require more specific features can make use of its functionality.
Authors:
TDbasedUFEadv_1.7.0.tar.gz
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TDbasedUFEadv.pdf |TDbasedUFEadv.html✨
TDbasedUFEadv/json (API)
NEWS
# Install 'TDbasedUFEadv' in R: |
install.packages('TDbasedUFEadv', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
The latest version of this package failed to build. Look at thebuild logs for more information.
Bug tracker:https://github.com/tagtag/tdbasedufeadv/issues
On BioConductor:TDbasedUFEadv-1.7.0(bioc 3.21)TDbasedUFEadv-1.6.0(bioc 3.20)
geneexpressionfeatureextractionmethylationarraysinglecellsoftwarebioconductor-packagebioinformaticstensor-decomposition
Last updated 2 months agofrom:8134eadaa9. Checks:OK: 4 WARNING: 3. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 19 2024 |
R-4.5-win | WARNING | Oct 31 2024 |
R-4.5-linux | OK | Nov 19 2024 |
R-4.4-win | WARNING | Oct 31 2024 |
R-4.4-mac | OK | Oct 31 2024 |
R-4.3-win | WARNING | Oct 31 2024 |
R-4.3-mac | OK | Oct 31 2024 |
Exports:computeSVDprepareCondDrugandDiseaseprepareCondTCGAprepareexpDrugandDiseaseprepareTensorfromListprepareTensorfromMatrixprepareTensorRectselectFeatureProjselectFeatureRectselectFeatureTransRecttransSVD
Dependencies:abindAnnotationDbiapeaplotaskpassassertthatbackportsbase64encBHBiobaseBiocGenericsBiocParallelBiostringsbitbit64bitopsblobbootbroombslibcachemcarcarDatacaToolschronclicliprcodetoolscolorspacecommonmarkcorrplotcowplotcpp11crayoncurldata.tableDBIDerivdigestdoByDOSEdplyrenrichplotenrichRevaluateexactRankTestsfansifarverfastmapfastmatchfgseafontawesomeformatRFormulafsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggforceggfunggnewscaleggplot2ggplotifyggpubrggrepelggsciggsignifggtangleggtextggthemesggtreeglueGO.dbGOSemSimgplotsgridExtragridGraphicsgridtextgsubfngtablegtoolshashhighrhmshtmltoolshttpuvhttrigraphIRangesisobandjpegjquerylibjsonliteKEGGRESTKernSmoothkm.ciKMsurvknitrlabelinglambda.rlaterlatticelazyevallifecyclelme4magrittrmarkdownMASSMatrixMatrixModelsmaxstatmemoisemgcvmicrobenchmarkmimeminqamodelrMOFAdatamunsellmvtnormnlmenloptrnnetnumDerivopensslpatchworkpbkrtestpillarpkgconfigplogrplotrixplyrpngpolyclippolynomprettyunitsprogresspromisesprotopurrrquantregqvalueR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppRcppEigenRCurlreadrreshape2rjsonrlangrmarkdownRSQLiterstatixRTCGArTensorrvestS4VectorssassscalesscatterpieselectrshinysnowsourcetoolsSparseMsqldfSTRINGdbstringistringrsurvivalsurvminersurvMiscsyssystemfontsTDbasedUFEtibbletidyrtidyselecttidytreetinytextreeiotweenrtximporttximportDatatzdbUCSC.utilsutf8vctrsviridisviridisLitevroomwithrWriteXLSxfunXMLxml2xtableXVectoryamlyulab.utilszlibbioczoo
Enrichment
Rendered fromEnrichment.Rmd
usingknitr::rmarkdown
on Nov 19 2024.Last update: 2023-03-24
Started: 2023-02-06
Explanation of TDbasedUFEadv
Rendered fromExplanation_of_TDbasedUFEadv.Rmd
usingknitr::rmarkdown
on Nov 19 2024.Last update: 2023-03-22
Started: 2023-03-20
How to use TDbasedUFEadv
Rendered fromHow_to_use_TDbasedUFEadv.Rmd
usingknitr::rmarkdown
on Nov 19 2024.Last update: 2023-03-24
Started: 2023-03-20
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Title Perform SVD toward reduced matrix generated from a tensor with partial summation | computeSVD |
Prepare condition matrix for expDrug | prepareCondDrugandDisease |
Prepare Sample label for TCGA data | prepareCondTCGA |
Generating gene expression of drug treated cell lines and a disease cell line | prepareexpDrugandDisease |
Prepare tensor from a list that includes multiple profiles | prepareTensorfromList |
Generate tensor from two matrices | prepareTensorfromMatrix |
Prepare tensor generated from two matrices that share samples | prepareTensorRect |
Select feature when projection strategy is employed for the case where features are shared with multiple omics profiles | selectFeatureProj |
Select features through the selection of singular value vectors | selectFeatureRect |
Select features for a tensor generated from two matrices that share samples. | selectFeatureTransRect |
Class definitions | TensorRect-class |
Convert SVD to that for the case where samples are shared between two matrices | transSVD |