Package: M3Drop 1.31.0

Tallulah Andrews

M3Drop: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model.

Authors:Tallulah Andrews <[email protected]>

M3Drop_1.31.0.tar.gz
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M3Drop.pdf |M3Drop.html
M3Drop/json (API)
NEWS

# Install 'M3Drop' in R:
install.packages('M3Drop', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/tallulandrews/m3drop/issues

On BioConductor:M3Drop-1.31.0(bioc 3.20)M3Drop-1.30.0(bioc 3.19)

bioconductor-package

34 exports 1.64 score 160 dependencies 2 dependents 21 mentions

Last updated 2 months agofrom:65bab9cacf

Exports:BrenneckeGetVariableGenesConsensus_FScorFSginiFSirlbaPcaFSM3DropCleanDataM3DropConvertDataM3DropDropoutModelsM3DropExpressionHeatmapM3DropFeatureSelectionM3DropGetExtremesM3DropGetHeatmapClustersM3DropGetHeatmapNamesM3DropGetMarkersM3DropSimulationTrifectaM3DropTestShiftM3DropThreeSetVennNBumiCheckFitNBumiCheckFitFSNBumiCoexpressionNBumiCompareModelsNBumiConvertDataNBumiConvertToIntegerNBumiFeatureSelectionCombinedDropNBumiFeatureSelectionHighVarNBumiFitBasicModelNBumiFitDispVsMeanNBumiFitModelNBumiHVGNBumiImputeNormNBumiPearsonResidualsNBumiPearsonResidualsApproxNBumiSimulationTrifectaPoissonUMIFeatureSelectionDropouts

Dependencies:abindaskpassbackportsbase64encbbmlebdsmatrixbeachmatbeeswarmBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularbitopsbslibcachemCairocallrcaToolscheckmatecliclustercodetoolscolorspacecpp11crayoncurldata.tableDelayedArrayDelayedMatrixStatsdensEstBayesdescdigestdistributionaldqrngevaluatefansifarverfastmapFNNfontawesomeforeignformatRFormulafsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggbeeswarmggplot2ggrastrggrepelgluegplotsgridExtragtablegtoolshighrHmischtmlTablehtmltoolshtmlwidgetshttrinlineIRangesirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglambda.rlatticelifecycleloomagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimemunsellmvtnormnlmennetnumDerivopensslpheatmappillarpkgbuildpkgconfigpngposteriorprocessxpsQuickJSRR6raggrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppMLRcppParallelRcppProgressreldistrlangrmarkdownrpartRSpectrarstanrstantoolsrstudioapirsvdRtsneS4ArraysS4VectorssassScaledMatrixscalesscaterscuttleSingleCellExperimentsitmosnowSparseArraysparseMatrixStatsStanHeadersstatmodstringistringrSummarizedExperimentsyssystemfontstensorAtextshapingtibbletinytexUCSC.utilsutf8uwotvctrsviporviridisviridisLitewithrxfunXVectoryamlzlibbioc

Introduction to M3Drop

Rendered fromM3Drop_Vignette.Rnwusingutils::Sweaveon Jun 30 2024.

Last update: 2024-03-06
Started: 2016-09-24

Readme and manuals

Help Manual

Help pageTopics
Calculate Gene Variablesbg__calc_variables
Mean to Dispersionbg__default_mean2disp
Fit Gamma Distributionbg__fit_gamma
Fit gene-specific dispersionbg__fit_size_to_var
Calculate Simulation Statisticsbg__get_stats
Calculate Horizontal Residualsbg__horizontal_residuals_MM_log10
Make Simulated Databg__MakeSimData bg__MakeSimDE bg__MakeSimDVar bg__MakeSimHVar
Perform Traditional Differential Expressionbg__nbumiGroupDE
Shift Size Parameterbg__shift_size
Variance vs Dropout Ratebg__var_vs_drop
Identify Highly Variable GenesBrenneckeGetVariableGenes
Consensus Feature SelectionConsensus_FS
Fit functions to the dropouts vs expression distribution.bg__fit_logistic bg__fit_MM bg__fit_ZIFA
Normalized Data using the DANB modelNBumiImputeNorm
Filter Expression Databg__filter_cells M3DropCleanData
Convert Data to be suitable for M3DropM3DropConvertData
Fit functions to the dropouts vs expression distribution.M3DropDropoutModels
Plot Heatmap of Gene Expressionbg__expression_heatmap M3DropExpressionHeatmap
Differentially Expressed Genes.bg__test_DE_K_equiv M3DropFeatureSelection
Get outliers from MM curve.bg__get_extreme_residuals M3DropGetExtremes
Extracts clusters/ordered names from heatmap outputM3DropGetHeatmapClusters M3DropGetHeatmapNames
Identify marker genesM3DropGetMarkers
Make M3Drop Plotsbg__add_model_to_plot bg__dropout_plot_base bg__highlight_genes
Test for horizontal shift.M3DropTestShift
Three-way Venn DiagramM3DropThreeSetVenn
Perform Traditional Differential Expressionbg__fitdispersion bg__get_mean2disp bg__m3dropTraditionalDE bg__m3dropTraditionalDEShiftDisp
Check Fit QualityNBumiCheckFit NBumiCheckFitFS
Variance-based Feature SelectionNBumiCoexpression
Compare negative binomial modelsNBumiCompareModels
Convert Data to be suitable for NBumiNBumiConvertData
Turn a matrix of expression values into integer countsNBumiConvertToInteger
Dropout-based Feature SelectionNBumiFeatureSelectionCombinedDrop
Variance-based Feature SelectionNBumiFeatureSelectionHighVar
Other Feature Selection Methodsobsolete__nbumiFeatureSelectionDropouts obsolete__nbumiFeatureSelectionHighVarDist2Med
Fit function between mean and dispersionNBumiFitDispVsMean
Fit Depth-Adjusted Negative Binomial ModelNBumiFitBasicModel NBumiFitModel
Variance-based Feature Selection Accounting for Library Size and Sample VarianceNBumiHVG
Calculate Pearson ResidualsNBumiPearsonResiduals NBumiPearsonResidualsApprox
Other Feature Selection MethodscorFS giniFS irlbaPcaFS
Dropout-based Feature SelectionPoissonUMIFeatureSelectionDropouts
Make Simulated Data from a provided scRNASeq dataset.M3DropSimulationTrifecta NBumiSimulationTrifecta