Package: DIscBIO 1.2.3.9001

Waldir Leoncio

DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

An open, multi-algorithmic pipeline for easy, fast and efficient analysis of cellular sub-populations and the molecular signatures that characterize them. The pipeline consists of four successive steps: data pre-processing, cellular clustering with pseudo-temporal ordering, defining differential expressed genes and biomarker identification. More details on Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements extensions of the work published by Ghannoum et. al. (2019) <doi:10.1101/700989>.

Authors:Salim Ghannoum [aut, cph], Alvaro Köhn-Luque [aut, ths], Waldir Leoncio [cre, aut], Damiano Fantini [ctb]

DIscBIO_1.2.3.9001.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
DIscBIO/json (API)
NEWS

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

Bug tracker:https://github.com/ocbe-uio/discbio/issues

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT
Datasets:

On CRAN:

Conda:

biomarker-discoveryjupyter-notebookscrna-seqsingle-cell-analysistranscriptomicsopenjdk

4.75 score 14 stars 5 scripts 487 downloads 1 mentions 37 exports 103 dependencies

Last updated from:f5c9bd7b8f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK385
source / vignettesOK318
linux-release-x86_64OK396
macos-release-arm64OK242
macos-oldrel-arm64OK238
windows-develOK319
windows-releaseOK335
windows-oldrelOK291
wasm-releaseOK207

Exports:as.DISCBIOClassVectoringDTClustDiffGenesClustexpclustheatmapcomptSNEcustomConvertFeatsDEGanalysisDEGanalysis2clustDISCBIODISCBIO2SingleCellExperimentExprmclustFinalPreprocessingFindOutliersJ48DTJ48DTevalJaccardKmeanOrderNetAnalysisNetworkingNoiseFilteringNormalizedataPCAplotSymbolsplotExptSNEplotGapplotLabelstSNEPlotMBpcaPlotmclustMBplotOrderTsneplotSilhouetteplotSymbolstSNEplottSNEPPIpseudoTimeOrderingRpartDTRpartEVALVolcanoPlot

Dependencies:abindaskpassbase64encBiobaseBiocGenericsbitopsbslibcachemcaToolsclasscliclustercombinatcommonmarkcpp11curlDelayedArrayDEoptimRdigestdiptestfarverfastICAfastmapflexmixfontawesomefpcfsgenericsGenomicRangesggplot2gluegplotsgtablegtoolshtmltoolshttpuvhttrigraphimputeIRangesisobandjquerylibjsonlitekernlabKernSmoothlabelinglaterlatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmclustmemoisemgcvmimemodeltoolsNetIndicesnlmennetopensslotelpermutepkgconfigplyrpngprabcluspromisesR6rappdirsRColorBrewerRcpprJavarlangrobustbaserpartrpart.plotRWekaRWekajarsS4ArraysS4VectorsS7sassscalesSeqinfoshinySingleCellExperimentsourcetoolsSparseArraystatmodSummarizedExperimentsysTrajectoryUtilsTSCANtsnevctrsveganviridisLitewithrxtableXVector

Readme and manuals

Help Manual

Help pageTopics
Convert Single Cell Data Objects to DISCBIO.as.DISCBIO
Check formatcheck.format
Generating a class vector to be used for the decision tree analysis.ClassVectoringDT ClassVectoringDT,DISCBIO-method
ClustDiffGenesClustDiffGenes ClustDiffGenes,DISCBIO-method
Clustering of single-cell transcriptome dataClustexp Clustexp,DISCBIO-method
Plotting clusters in a heatmap representation of the cell distancesclustheatmap clustheatmap,DISCBIO-method
Computing tSNEcomptSNE comptSNE,DISCBIO-method
Automatic Feature Id Conversion.customConvertFeats
Determining differentially expressed genes (DEGs) between all individual clusters.DEGanalysis DEGanalysis,DISCBIO-method
Determining differentially expressed genes (DEGs) between two particular clusters.DEGanalysis2clust DEGanalysis2clust,DISCBIO-method
The DISCBIO ClassDISCBIO DISCBIO-class DISCBIO-class,
Convert a DISCBIO object to a SingleCellExperiment.DISCBIO2SingleCellExperiment
Performing Model-based clustering on expression valuesExprmclust Exprmclust,data.frame-method Exprmclust,DISCBIO-method
Final PreprocessingFinalPreprocessing FinalPreprocessing,DISCBIO-method
Inference of outlier cellsFindOutliers FindOutliers,DISCBIO-method
Foldchange of twoclass unpaired sequencing datafoldchange.seq.twoclass.unpaired
org.Hs.eg.db annotation package datageneList
Human and Mouse Gene Identifiers.HumanMouseGeneIds
J48 Decision TreeJ48DT
Evaluating the performance of the J48 decision tree.J48DTeval
Jaccard’s similarityJaccard
Pseudo-time ordering based on k-means clustersKmeanOrder KmeanOrder,DISCBIO-method
Networking analysis.NetAnalysis
Plotting the network.Networking
Noise FilteringNoiseFiltering NoiseFiltering,DISCBIO-method
Normalizing and filteringNormalizedata Normalizedata,DISCBIO-method
Plot PCA symbolsPCAplotSymbols PCAplotSymbols,DISCBIO-method
Highlighting gene expression in the t-SNE mapplotExptSNE plotExptSNE,DISCBIO-method
Plotting Gap StatisticsplotGap plotGap,DISCBIO-method
tSNE map with labelsplotLabelstSNE plotLabelstSNE,DISCBIO-method
Plotting pseudo-time ordering or gene expression in Model-based clustering in PCAPlotMBpca
Plotting the Model-based clusters in PCA.PlotmclustMB PlotmclustMB,DISCBIO-method
Plotting the pseudo-time ordering in the t-SNE mapplotOrderTsne plotOrderTsne,DISCBIO-method
Silhouette Plot for K-means clusteringplotSilhouette plotSilhouette,DISCBIO-method
tSNE map for K-means clustering with symbolsplotSymbolstSNE plotSymbolstSNE,DISCBIO-method
tSNE mapplottSNE plottSNE,DISCBIO-method
Defining protein-protein interactions (PPI) over a list of genes,PPI
Prepare Example DatasetprepExampleDataset
Pseudo-time orderingpseudoTimeOrdering pseudoTimeOrdering,DISCBIO-method
Rank columnsrankcols
Reformat Siggenes TablereformatSiggenes
Replace DecimalsreplaceDecimals
Resamplingresa
Retries a URLretrieveURL
RPART Decision TreeRpartDT
Evaluating the performance of the RPART Decision Tree.RpartEVAL
Significance analysis of microarrayssammy
Estimate sequencing depthssamr.estimate.depth
Single-cells data from a myxoid liposarcoma cell linevaluesG1msTest
Volcano PlotVolcanoPlot
Twoclass Wilcoxon statisticswilcoxon.unpaired.seq.func