Package: jrSiCKLSNMF 2.0.0

jrSiCKLSNMF: Clustering Single-Cell Multimodal Omics Data with Joint Graph Regularized Single-Cell Kullback-Leibler Sparse Non-Negative Matrix Factorization

Methods to perform Joint graph Regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") on quality controlled multi-assay single-cell omics count data, specifically dual-assay scRNA-seq and scATAC-seq data. 'jrSiCKLSNMF' extracts meaningful latent factors that are shared across omics views. These factors enable accurate cell-type clustering, and facilitate visualizations. Also includes methods for mini- batch updates and other adaptations for larger datasets.

Authors:Dorothy Ellis [aut, cre]

jrSiCKLSNMF_2.0.0.tar.gz
jrSiCKLSNMF_2.0.0.zip(r-4.7-x86_64)jrSiCKLSNMF_2.0.0.zip(r-4.7-arm64)jrSiCKLSNMF_2.0.0.zip(r-4.6-x86_64)jrSiCKLSNMF_2.0.0.zip(r-4.6-arm64)jrSiCKLSNMF_2.0.0.zip(r-4.5-x86_64)
jrSiCKLSNMF_2.0.0.tar.gz(r-4.7-arm64)jrSiCKLSNMF_2.0.0.tar.gz(r-4.7-x86_64)jrSiCKLSNMF_2.0.0.tar.gz(r-4.6-arm64)jrSiCKLSNMF_2.0.0.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
jrSiCKLSNMF/json (API)

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

Bug tracker:https://github.com/ellisdoro/jrsicklsnmf/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • SimData - A simulated dataset for use with jrSiCKLSNMF
  • SimSickleJrSmall - A small SickleJr object containing a subset of data from the SimData data object. Contains the completed analysis from the 'Getting Started' vignette for a small subset of 10 cells with 150 genes and 700 peaks. The clusters derived from this dataset are not accurate; this dataset is intended for use with code examples.

On CRAN:

Conda:

openblascppopenmp

2.70 score 9 scripts 497 downloads 19 exports 178 dependencies

Last updated from:c0c5253223. Checks:9 WARNING, 1 ERROR, 5 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING339
linux-devel-x86_64WARNING347
source / vignettesERROR446
linux-release-arm64WARNING318
linux-release-x86_64WARNING370
macos-release-arm64FAIL113
macos-release-x86_64FAIL321
macos-oldrel-arm64FAIL140
macos-oldrel-x86_64FAIL300
windows-devel-arm64WARNING390
windows-devel-x86_64WARNING354
windows-release-arm64WARNING361
windows-release-x86_64WARNING305
windows-oldrel-x86_64WARNING308
wasm-releaseFAIL209

Exports:AddSickleJrMetadataBuildKNNGraphLaplaciansBuildSNNGraphLaplaciansCalculateUMAPSickleJrClusterSickleJrCreateSickleJrDetermineClustersDetermineDFromIRLBAGenerateWmatricesandHmatrixjrSiCKLSNMFlossCalcRWrapperMinibatchDiagnosticPlotNormalizeCountMatricesPlotLossvsLatentFactorsPlotSickleJrUMAPRunjrSiCKLSNMFSetLambdasandRegsSetWandHfromWHinitialsSickleJr

Dependencies:abindaskpassassortheadbackportsbase64encbeachmatBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularblusterbootbroombslibcachemcarcarDataclasscliclusterclValidcodetoolscolorspacecorrplotcowplotcpp11crosstalkdata.tableDelayedArraydendextendDerivdigestdoBydplyrdqrngDTedgeRellipseemmeansestimabilityevaluatefactoextraFactoMineRfarverfastmapflashClustfontawesomeforeachforecastformatRFormulafracdifffsfutile.loggerfutile.optionsgenericsGenomicRangesggplot2ggpubrggrepelggsciggsignifgluegridExtragtableherehighrhtmltoolshtmlwidgetsigraphIRangesirlbaisobanditeratorsjquerylibjsonlitekknnknitrlabelinglambda.rlaterlatticelazyevalleapslifecyclelimmalme4lmtestlocfitmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemetapodmgcvmimeminqamodelrmultcompViewmvtnormnlmenloptrnnetnumDerivopensslotelpbapplypbkrtestpillarpkgconfigpngpolynompromisespurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppProgressRcppTOMLRdpackreformulasreticulaterlangrmarkdownrprojrootRSpectrarstatixrsvdS4ArraysS4VectorsS7sassScaledMatrixscalesscatterplot3dscranscuttleSeqinfoshowtextshowtextdbSingleCellExperimentsitmosnowSparseArraySparseMstatmodstringistringrSummarizedExperimentsurvivalsyssysfontstibbletidyrtidyselecttimeDatetinytexumapurcautf8vctrsviridisviridisLitewithrxfunXVectoryamlzoo

Readme and manuals

Help Manual

Help pageTopics
Add metadata to an object of class SickleJrAddSickleJrMetadata
Build KNN graphs and generate their graph LaplaciansBuildKNNGraphLaplacians
Build SNN graphs and generate their graph LaplaciansBuildSNNGraphLaplacians
Calculate the UMAP for an object of class SickleJrCalculateUMAPSickleJr
Cluster the \mathbf{H} matrixClusterSickleJr
Create an object of class SickleJrCreateSickleJr
Perform clustering diagnosticsDetermineClusters
Create elbow plots of the singular values derived from IRLBA to determine D for large datasetsDetermineDFromIRLBA
Generate the \mathbf{W}^v matrices and \mathbf{H} matrixGenerateWmatricesandHmatrix
Run jrSiCKLSNMF outside of a SickleJr objectjrSiCKLSNMF
Calculate the loss function in RlossCalcRWrapper
Plot a diagnostic plot for the mini-batch algorithmMinibatchDiagnosticPlot
Normalize the count matrices and set whether to use the Poisson KL divergence or the Frobenius norm within each modalityNormalizeCountMatrices
Create plots to help determine the number of latent factors to use for jrSiCKLSNMFPlotLossvsLatentFactors
Generate UMAP plots for an object of class SickleJrPlotSickleJrUMAP
Run jrSiCKLSNMF on an object of class SickleJrRunjrSiCKLSNMF
Set lambda values and type of row regularization for an object of class SickleJrSetLambdasandRegs
Set \mathbf{W}^v matrices and \mathbf{H} matrix from pre-calculated valuesSetWandHfromWHinitials
The SickleJr classSickleJr SickleJr-class
A simulated dataset for use with jrSiCKLSNMFSimData
A small SickleJr object containing a subset of data from the SimData data object. Contains the completed analysis from the `Getting Started` vignette for a small subset of 10 cells with 150 genes and 700 peaks. The clusters derived from this dataset are not accurate; this dataset is intended for use with code examples.SimSickleJrSmall