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-4.5)jrSiCKLSNMF_2.0.0.zip(r-4.4)jrSiCKLSNMF_2.0.0.zip(r-4.3)

jrSiCKLSNMF_2.0.0.tar.gz(r-4.5-noble)jrSiCKLSNMF_2.0.0.tar.gz(r-4.4-noble)
jrSiCKLSNMF.pdf |jrSiCKLSNMF.html✨
jrSiCKLSNMF/json (API)
NEWS

# 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:

openblas cpp openmp

3.00 score 6 scripts 355 downloads 19 exports 178 dependencies

Last updated 9 months agofrom:c0c5253223. Checks:1 ERROR, 4 WARNING. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	FAIL	Feb 18 2025
R-4.5-win-x86_64	WARNING	Feb 18 2025
R-4.5-linux-x86_64	WARNING	Feb 18 2025
R-4.4-win-x86_64	WARNING	Feb 18 2025
R-4.3-win-x86_64	WARNING	Feb 18 2025

Exports:AddSickleJrMetadata BuildKNNGraphLaplacians BuildSNNGraphLaplacians CalculateUMAPSickleJr ClusterSickleJr CreateSickleJr DetermineClusters DetermineDFromIRLBA GenerateWmatricesandHmatrix jrSiCKLSNMF lossCalcRWrapper MinibatchDiagnosticPlot NormalizeCountMatrices PlotLossvsLatentFactors PlotSickleJrUMAP RunjrSiCKLSNMF SetLambdasandRegs SetWandHfromWHinitials SickleJr

Dependencies:abind askpass assorthead backports base64enc beachmat BH Biobase BiocGenerics BiocNeighbors BiocParallel BiocSingular bluster boot broom bslib cachem car carData class cli cluster clValid codetools colorspace corrplot cowplot cpp11 crayon crosstalk curl data.table DelayedArray dendextend Deriv digest doBy dplyr dqrng DT edgeR ellipse emmeans estimability evaluate factoextra FactoMineR fansi farver fastmap flashClust fontawesome foreach formatR Formula fs futile.logger futile.options generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 ggpubr ggrepel ggsci ggsignif glue gridExtra gtable here highr htmltools htmlwidgets httpuv httr igraph IRanges irlba isoband iterators jquerylib jsonlite kknn knitr labeling lambda.r later lattice lazyeval leaps lifecycle limma lme4 locfit magrittr MASS Matrix MatrixGenerics MatrixModels matrixStats memoise metapod mgcv microbenchmark mime minqa modelr multcompView munsell mvtnorm nlme nloptr nnet numDeriv openssl pbapply pbkrtest pillar pkgconfig plyr png polynom promises purrr quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen RcppProgress RcppTOML Rdpack reformulas reshape2 reticulate rlang rmarkdown rprojroot RSpectra rstatix rsvd S4Arrays S4Vectors sass ScaledMatrix scales scatterplot3d scran scuttle SingleCellExperiment sitmo snow SparseArray SparseM statmod stringi stringr SummarizedExperiment survival sys tibble tidyr tidyselect tinytex UCSC.utils umap utf8 vctrs viridis viridisLite withr xfun XVector yaml

Vignettes and/or manual for this package failed to build, some documents may be unavailable. Please inspect thebuild logs for more information.

Help page	Topics
Add metadata to an object of class SickleJr	AddSickleJrMetadata
Build KNN graphs and generate their graph Laplacians	BuildKNNGraphLaplacians
Build SNN graphs and generate their graph Laplacians	BuildSNNGraphLaplacians
Calculate the UMAP for an object of class SickleJr	CalculateUMAPSickleJr
Cluster the \mathbf{H} matrix	ClusterSickleJr
Create an object of class SickleJr	CreateSickleJr
Perform clustering diagnostics	DetermineClusters
Create elbow plots of the singular values derived from IRLBA to determine D for large datasets	DetermineDFromIRLBA
Generate the \mathbf{W}^v matrices and \mathbf{H} matrix	GenerateWmatricesandHmatrix
Run jrSiCKLSNMF outside of a SickleJr object	jrSiCKLSNMF
Calculate the loss function in R	lossCalcRWrapper
Plot a diagnostic plot for the mini-batch algorithm	MinibatchDiagnosticPlot
Normalize the count matrices and set whether to use the Poisson KL divergence or the Frobenius norm within each modality	NormalizeCountMatrices
Create plots to help determine the number of latent factors to use for jrSiCKLSNMF	PlotLossvsLatentFactors
Generate UMAP plots for an object of class SickleJr	PlotSickleJrUMAP
Run jrSiCKLSNMF on an object of class SickleJr	RunjrSiCKLSNMF
Set lambda values and type of row regularization for an object of class SickleJr	SetLambdasandRegs
Set \mathbf{W}^v matrices and \mathbf{H} matrix from pre-calculated values	SetWandHfromWHinitials
The SickleJr class	SickleJr SickleJr-class
A simulated dataset for use with jrSiCKLSNMF	SimData
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

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

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