Package: M3Drop 1.39.0

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_1.39.0.tar.gz
M3Drop_1.39.0.zip(r-4.7-any)M3Drop_1.39.0.zip(r-4.6-any)M3Drop_1.39.0.zip(r-4.5-any)
M3Drop_1.39.0.tgz(r-4.6-any)M3Drop_1.39.0.tgz(r-4.5-any)
M3Drop_1.39.0.tar.gz(r-4.7-any)M3Drop_1.39.0.tar.gz(r-4.6-any)
M3Drop_1.39.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
M3Drop/json (API)

# Install 'M3Drop' in R:

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

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

On BioConductor:M3Drop-1.39.0(bioc 3.24)M3Drop-1.38.0(bioc 3.23)

rnaseq sequencing transcriptomics geneexpression software differentialexpression dimensionreduction featureextraction human-cell-atlas rna-seq single-cell single-cell-rna-seq

8.57 score 33 stars 3 packages 157 scripts 21 mentions 34 exports 143 dependencies

Last updated from:987a662dbb. Checks:1 FAILURE, 6 NOTE, 2 OK, 1 FAIL. Indexed: yes.

Target	Result	Time
bioc-checks	FAILURE	245
linux-devel-x86_64	NOTE	472
source / vignettes	OK	394
linux-release-x86_64	NOTE	540
macos-release-arm64	NOTE	242
macos-oldrel-arm64	NOTE	279
windows-devel-x86_64	FAIL	330
windows-release-x86_64	NOTE	704
windows-oldrel-x86_64	NOTE	685
wasm-release	OK	198

Exports:BrenneckeGetVariableGenes Consensus_FS corFS giniFS irlbaPcaFS M3DropCleanData M3DropConvertData M3DropDropoutModels M3DropExpressionHeatmap M3DropFeatureSelection M3DropGetExtremes M3DropGetHeatmapClusters M3DropGetHeatmapNames M3DropGetMarkers M3DropSimulationTrifecta M3DropTestShift M3DropThreeSetVenn NBumiCheckFit NBumiCheckFitFS NBumiCoexpression NBumiCompareModels NBumiConvertData NBumiConvertToInteger NBumiFeatureSelectionCombinedDrop NBumiFeatureSelectionHighVar NBumiFitBasicModel NBumiFitDispVsMean NBumiFitModel NBumiHVG NBumiImputeNorm NBumiPearsonResiduals NBumiPearsonResidualsApprox NBumiSimulationTrifecta PoissonUMIFeatureSelectionDropouts

Dependencies:abind assorthead backports base64enc bbmle bdsmatrix beachmat beeswarm BH Biobase BiocGenerics BiocNeighbors BiocParallel BiocSingular bitops bslib cachem callr caTools checkmate cli cluster codetools colorspace cpp11 data.table DelayedArray densEstBayes desc digest distributional dqrng evaluate farver fastmap FNN fontawesome foreign formatR Formula fs futile.logger futile.options generics GenomicRanges ggbeeswarm ggplot2 ggrepel glue gplots gridExtra gtable gtools highr Hmisc htmlTable htmltools htmlwidgets inline IRanges irlba isoband jquerylib jsonlite KernSmooth knitr labeling lambda.r lattice lifecycle loo magrittr MASS Matrix MatrixGenerics matrixStats memoise mgcv mime mvtnorm nlme nnet numDeriv otel pheatmap pillar pkgbuild pkgconfig posterior processx ps QuickJSR R6 rappdirs RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppML RcppParallel RcppProgress reldist rlang rmarkdown rpart RSpectra rstan rstantools rstudioapi rsvd Rtsne S4Arrays S4Vectors S7 sass ScaledMatrix scales scater scuttle Seqinfo SingleCellExperiment sitmo snow SparseArray StanHeaders statmod stringi stringr SummarizedExperiment tensorA tibble tinytex utf8 uwot vctrs vipor viridis viridisLite withr xfun XVector yaml

Introduction to M3Drop

Rendered fromM3Drop_Vignette.Rnwusingutils::Sweave

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Calculate Gene Variables	bg__calc_variables
Mean to Dispersion	bg__default_mean2disp
Fit Gamma Distribution	bg__fit_gamma
Fit gene-specific dispersion	bg__fit_size_to_var
Calculate Simulation Statistics	bg__get_stats
Calculate Horizontal Residuals	bg__horizontal_residuals_MM_log10
Make Simulated Data	bg__MakeSimData bg__MakeSimDE bg__MakeSimDVar bg__MakeSimHVar
Perform Traditional Differential Expression	bg__nbumiGroupDE
Shift Size Parameter	bg__shift_size
Variance vs Dropout Rate	bg__var_vs_drop
Identify Highly Variable Genes	BrenneckeGetVariableGenes
Consensus Feature Selection	Consensus_FS
Fit functions to the dropouts vs expression distribution.	bg__fit_logistic bg__fit_MM bg__fit_ZIFA
Normalized Data using the DANB model	NBumiImputeNorm
Filter Expression Data	bg__filter_cells M3DropCleanData
Convert Data to be suitable for M3Drop	M3DropConvertData
Fit functions to the dropouts vs expression distribution.	M3DropDropoutModels
Plot Heatmap of Gene Expression	bg__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 output	M3DropGetHeatmapClusters M3DropGetHeatmapNames
Identify marker genes	M3DropGetMarkers
Make M3Drop Plots	bg__add_model_to_plot bg__dropout_plot_base bg__highlight_genes
Test for horizontal shift.	M3DropTestShift
Three-way Venn Diagram	M3DropThreeSetVenn
Perform Traditional Differential Expression	bg__fitdispersion bg__get_mean2disp bg__m3dropTraditionalDE bg__m3dropTraditionalDEShiftDisp
Check Fit Quality	NBumiCheckFit NBumiCheckFitFS
Variance-based Feature Selection	NBumiCoexpression
Compare negative binomial models	NBumiCompareModels
Convert Data to be suitable for NBumi	NBumiConvertData
Turn a matrix of expression values into integer counts	NBumiConvertToInteger
Dropout-based Feature Selection	NBumiFeatureSelectionCombinedDrop
Variance-based Feature Selection	NBumiFeatureSelectionHighVar
Other Feature Selection Methods	obsolete__nbumiFeatureSelectionDropouts obsolete__nbumiFeatureSelectionHighVarDist2Med
Fit function between mean and dispersion	NBumiFitDispVsMean
Fit Depth-Adjusted Negative Binomial Model	NBumiFitBasicModel NBumiFitModel
Variance-based Feature Selection Accounting for Library Size and Sample Variance	NBumiHVG
Calculate Pearson Residuals	NBumiPearsonResiduals NBumiPearsonResidualsApprox
Other Feature Selection Methods	corFS giniFS irlbaPcaFS
Dropout-based Feature Selection	PoissonUMIFeatureSelectionDropouts
Make Simulated Data from a provided scRNASeq dataset.	M3DropSimulationTrifecta NBumiSimulationTrifecta