Package: digitalDLSorteR 1.1.2
digitalDLSorteR: Deconvolution of Bulk RNA-Seq Data Based on Deep Learning
Deconvolution of bulk RNA-Seq data using context-specific deconvolution models based on Deep Neural Networks using scRNA-Seq data as input. These models are able to make accurate estimates of the cell composition of bulk RNA-Seq samples from the same context using the advances provided by Deep Learning and the meaningful information provided by scRNA-Seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> for more details.
Authors:
digitalDLSorteR_1.1.2.tar.gz
digitalDLSorteR_1.1.2.zip(r-4.5)digitalDLSorteR_1.1.2.zip(r-4.4)digitalDLSorteR_1.1.2.zip(r-4.3)
digitalDLSorteR_1.1.2.tgz(r-4.4-any)digitalDLSorteR_1.1.2.tgz(r-4.3-any)
digitalDLSorteR_1.1.2.tar.gz(r-4.5-noble)digitalDLSorteR_1.1.2.tar.gz(r-4.4-noble)
digitalDLSorteR_1.1.2.tgz(r-4.4-emscripten)digitalDLSorteR_1.1.2.tgz(r-4.3-emscripten)
digitalDLSorteR.pdf |digitalDLSorteR.html✨
digitalDLSorteR/json (API)
NEWS
# Install 'digitalDLSorteR' in R: |
install.packages('digitalDLSorteR', repos = c('https://diegommcc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/diegommcc/digitaldlsorter/issues
deconvolutiondeep-learningrna-seqsingle-cell
Last updated 6 days agofrom:c40770b26d. Checks:OK: 6 ERROR: 1. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 31 2024 |
R-4.5-win | OK | Oct 31 2024 |
R-4.5-linux | ERROR | Oct 31 2024 |
R-4.4-win | OK | Oct 31 2024 |
R-4.4-mac | OK | Oct 31 2024 |
R-4.3-win | OK | Oct 31 2024 |
R-4.3-mac | OK | Oct 31 2024 |
Exports:barErrorPlotbarPlotCellTypesblandAltmanLehPlotbulk.simulbulk.simul<-calculateEvalMetricscell.namescell.names<-cell.typescell.types<-corrExpPredPlotcreateDDLSobjectdeconv.datadeconv.data<-deconv.resultsdeconv.results<-deconvDDLSObjdeconvDDLSPretrainedDigitalDLSorterDigitalDLSorterDNNdistErrorPlotestimateZinbwaveParamsfeaturesfeatures<-generateBulkCellMatrixgetProbMatrixinstallTFpythoninterGradientsDLlistToDDLSlistToDDLSDNNloadDeconvDataloadTrainedModelFromH5methodmethod<-modelmodel<-plotHeatmapGradsAggplotsplots<-plotTrainingHistorypreparingToSaveprob.cell.typesprob.cell.types<-prob.matrixprob.matrix<-ProbMatrixCellTypesprojectproject<-saveRDSsaveTrainedModelAsH5setset.listset.list<-set<-showProbPlotsimBulkProfilessimSCProfilessingle.cell.realsingle.cell.real<-single.cell.simulsingle.cell.simul<-test.deconv.metricstest.deconv.metrics<-test.metricstest.metrics<-test.predtest.pred<-topGradientsCellTypetrainDDLSModeltrained.modeltrained.model<-training.historytraining.history<-zinb.paramszinb.params<-ZinbParametersModelzinbwave.modelzinbwave.model<-
Dependencies:abindannotateAnnotationDbiaskpassassortheadbackportsbase64encbeachmatBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularBiostringsbitbit64blobblusterbootbroomcachemcarcarDatacliclustercodetoolscolorspaceconfigcorrplotcowplotcpp11crayoncurlDBIDelayedArrayDerivdoBydplyrdqrngedgeRfansifarverfastmapformatRFormulafutile.loggerfutile.optionsgenefiltergenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggpubrggrepelggsciggsignifgluegridExtragrrgtablegtoolsherehttrigraphIRangesirlbaisobandjsonliteKEGGRESTkeraslabelinglambda.rlatticelifecyclelimmalme4locfitmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemetapodmgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivopensslpbapplypbkrtestpillarpkgconfigplogrplyrpngpolynomprocessxpspurrrquantregR6rappdirsRColorBrewerRcppRcppEigenRcppTOMLreshape2reticulaterlangrprojrootRSQLiterstatixrstudioapirsvdS4ArraysS4VectorsScaledMatrixscalesscranscuttleSingleCellExperimentsitmosnowsoftImputeSparseArraySparseMstatmodstringistringrSummarizedExperimentsurvivalsystensorflowtfautographtfrunstibbletidyrtidyselectUCSC.utilsutf8vctrsviridisLitewhiskerwithrXMLxtableXVectoryamlzeallotzinbwavezlibbioc
HDF5 files as back-end
Rendered fromhdf5Backend.Rmd
usingknitr::rmarkdown
on Oct 31 2024.Last update: 2024-04-02
Started: 2021-08-10
Keras/TensorFlow installation and configuration
Rendered fromkerasIssues.Rmd
usingknitr::rmarkdown
on Oct 31 2024.Last update: 2024-09-29
Started: 2021-08-10
Building new deconvoluion models: deconvolution of colorectal cancer samples
Rendered fromrealModelWorkflow.Rmd
usingknitr::rmarkdown
on Oct 31 2024.Last update: 2024-09-30
Started: 2024-04-02
Using pre-trained context-specific deconvolution models
Rendered frompretrainedModels.Rmd
usingknitr::rmarkdown
on Oct 31 2024.Last update: 2024-09-30
Started: 2021-08-10