Type:	Package
Title:	Multimodal Single-Cell Omics Dimensionality Reduction
Version:	1.2.3
Description:	Methods to perform Joint graph Regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization ('jrSiCKLSNMF', pronounced "junior sickles NMF") on quality controlled single-cell multimodal omics count data. 'jrSiCKLSNMF' specifically deals with dual-assay scRNA-seq and scATAC-seq data. This package contains functions to extract meaningful latent factors that are shared across omics modalities. These factors enable accurate cell-type clustering and facilitate visualizations. Methods for pre-processing, clustering, and mini-batch updates and other adaptations for larger datasets are also included. For further details on the methods used in this package please see Ellis, Roy, and Datta (2023) <doi:10.3389/fgene.2023.1179439>.
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	Rcpp (≥ 1.0.9), igraph, umap, kknn, ggplot2, methods, stats, rlang, Matrix, data.table,parallel,pbapply,cluster,MASS,clValid,factoextra, foreach,irlba,scran,Rdpack,ggrepel
Suggests:	knitr, rmarkdown
LinkingTo:	Rcpp, RcppArmadillo, RcppProgress
LazyData:	true
LazyDataCompression:	xz
VignetteBuilder:	knitr
RdMacros:	Rdpack
NeedsCompilation:	yes
Packaged:	2025-07-11 09:06:41 UTC; dorothyellis
Author:	Dorothy Ellis [aut, cre], Susmita Datta [ths], Kenneth Perkins [ctb] (Util.h function author, http://programmingnotes.org/), Renaud Gaujoux [ctb] (Author of .nndsvd R adaptation)
Maintainer:	Dorothy Ellis <ddemoreellis@gmail.com>
Depends:	R (≥ 3.5.0)
Repository:	CRAN
Date/Publication:	2025-07-11 10:10:02 UTC

Add metadata to an object of class SickleJr

Description

Add any type of metadata to an object of class SickleJr. Metadata are stored in list format under the name specified in metadataname of each node in slot metadata.

Usage

AddSickleJrMetadata(SickleJr, metadata, metadataname)

Arguments

Value

An object of class SickleJr with added metadata

Examples

SimSickleJrSmall<-AddSickleJrMetadata(SimSickleJrSmall,
SimData$cell_type,"cell_types_full_data")

Build KNN graphs and generate their graph Laplacians

Description

Generate graph Laplacians for graph regularization of jrSiCKLSNMF from the list of raw count matrices using a KNN graph. Note that this is only appropriate when the number of features is considerably greater than the number of cells in all modalities. If this is not the case, please use BuildSNNGraphLaplacians or any other method of graph construction that does not rely on the Euclidean distance and store the graph Laplacians for each modality as a list in the graph.laplacian.list slot.

Usage

BuildKNNGraphLaplacians(SickleJr, k = 20)

Arguments

Value

An object of class SickleJr with a list of graph Laplacians in sparse matrix format added to the graph.laplacian.list slot

References

Lun AT, McCarthy DJ, Marioni JC (2016). “A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.” F1000Research, 5. ISSN 1759796X, doi:10.12688/F1000RESEARCH.9501.2, https://pubmed.ncbi.nlm.nih.gov/27909575/.

Examples

SimSickleJrSmall<-BuildKNNGraphLaplacians(SimSickleJrSmall)

Build SNN graphs and generate their graph Laplacians

Description

Generate graph Laplacians for graph regularization of jrSiCKLSNMF from the list of raw count matrices using an SNN graph. SNN is more robust to situations where the number of cells outnumbers the number of features. Uses the scran package's BuildSNNGraph function (Lun et al. 2016)

Usage

BuildSNNGraphLaplacians(SickleJr, k = 20)

Arguments

Value

An object of class SickleJr with list of graph Laplacians in sparse matrix format added to its graph.laplacian.list slot

References

Lun AT, McCarthy DJ, Marioni JC (2016). “A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.” F1000Research, 5. ISSN 1759796X, doi:10.12688/F1000RESEARCH.9501.2, https://pubmed.ncbi.nlm.nih.gov/27909575/.

Examples

SimSickleJrSmall<-BuildSNNGraphLaplacians(SimSickleJrSmall)

Calculate the UMAP for an object of class SickleJr

Description

Perform UMAP on the \mathbf{H} matrix alone (default) or within a modality by using UMAP on the W^vH corresponding to modality v.

Usage

CalculateUMAPSickleJr(
  SickleJr,
  umap.settings = umap::umap.defaults,
  modality = NULL
)

Arguments

Value

An object of class SickleJr with UMAP output based on the \mathbf{H} matrix alone or within a modality added to its umap slot

References

McInnes L, Healy J, Saul N, Großberger L (2018). “UMAP: Uniform Manifold Approximation and Projection.” Journal of Open Source Software, 3(29), 861. ISSN 2475-9066, doi:10.21105/JOSS.00861, https://joss.theoj.org/papers/10.21105/joss.00861.

Examples

#Since this example has only 10 observations,
#we need to modify the number of neighbors from the default of 15
umap.settings=umap::umap.defaults
umap.settings$n_neighbors=2
SimSickleJrSmall<-CalculateUMAPSickleJr(SimSickleJrSmall,
umap.settings=umap.settings)
SimSickleJrSmall<-CalculateUMAPSickleJr(SimSickleJrSmall,
umap.settings=umap.settings,modality=1)
SimSickleJrSmall<-CalculateUMAPSickleJr(SimSickleJrSmall,
umap.settings=umap.settings,modality=2)

Cluster the \mathbf{H} matrix

Description

Perform k-means, spectral clustering, clustering based off of the index of the maximum latent factor, or Louvain community detection on the \mathbf{H} matrix. Defaults to k-means.

Usage

ClusterSickleJr(
  SickleJr,
  numclusts,
  method = "kmeans",
  neighbors = 20,
  louvainres = 0.3
)

Arguments

Value

SickleJr- an object of class SickleJr with added clustering information

References

Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008). “Fast unfolding of communities in large networks.” Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. ISSN 1742-5468, doi:10.1088/1742-5468/2008/10/P10008, 0803.0476, https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008.

Lun AT, McCarthy DJ, Marioni JC (2016). “A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.” F1000Research, 5. ISSN 1759796X, doi:10.12688/F1000RESEARCH.9501.2, https://pubmed.ncbi.nlm.nih.gov/27909575/.

Ng AY, Jordan MI, Weiss Y (2001). “On spectral clustering: analysis and an algorithm.” In Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, NIPS'01, 849–856.

Schliep K, Hechenbichler K (2016). “kknn: Weighted k-Nearest Neighbors.” https://cran.r-project.org/package=kknn.

Xu W, Liu X, Gong Y (2003). “Document clustering based on non-negative matrix factorization.” SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval, 267–273. doi:10.1145/860435.860485.

Examples

SimSickleJrSmall<-ClusterSickleJr(SimSickleJrSmall,3)
SimSickleJrSmall<-ClusterSickleJr(SimSickleJrSmall,method="louvain",neighbors=5)
SimSickleJrSmall<-ClusterSickleJr(SimSickleJrSmall,method="spectral",neighbors=5,numclusts=3)
#DO NOT DO THIS FOR REAL DATA; this is just to illustrate max clustering
SimSickleJrSmall<-SetLambdasandRowReg(SimSickleJrSmall,rowReg="L2Norm")
SimSickleJrSmall<-ClusterSickleJr(SimSickleJrSmall,method="max")

Create an object of class SickleJr

Description

Using a list of sparse count matrices, create an object of class SickleJr and specify the names of these count matrices.

Usage

CreateSickleJr(count.matrices, names = NULL)

Arguments

Value

An object of class SickleJr with sparse count matrices added to the count.matrices slot

Examples

ExampleSickleJr<-CreateSickleJr(SimData$Xmatrices)

Perform clustering diagnostics

Description

A wrapper for the clValid and fviz_nbclust functions to perform clustering diagnostics

Usage

DetermineClusters(
  SickleJr,
  numclusts = 2:20,
  clusteringmethod = "kmeans",
  diagnosticmethods = c("wss", "silhouette", "gap_stat"),
  clValidvalidation = "internal",
  createDiagnosticplots = TRUE,
  runclValidDiagnostics = TRUE,
  printPlots = TRUE,
  printclValid = TRUE,
  subset = FALSE,
  subsetsize = 1000,
  seed = NULL
)

Arguments

Value

An object of class SickleJr with cluster diagnostics added to its clusterdiagnostics slot

References

Brock G, Pihur V, Datta S, Datta S (2008). “clValid: An R Package for Cluster Validation.” Journal of Statistical Software, 25(4), 1–22. https://www.jstatsoft.org/v25/i04/.

Kassambara A, Mundt F (2020). factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.7, https://CRAN.R-project.org/package=factoextra.

Examples

#Since these data are too small, the clValid diagnostics do not run
#properly. See the vignette for an example with the clValid diagnostics
SimSickleJrSmall<-DetermineClusters(SimSickleJrSmall,numclusts=2:5,runclValidDiagnostics=FALSE)

Create elbow plots of the singular values derived from IRLBA to determine D for large datasets

Description

This generates v+1 plots, where v is the number of data modalities, of the approximate singular values generated by IRLBA.There is one plot for each modality and then a final plot that concatenates all of the modalities together. Choose the largest elbow value among the three plots.

Usage

DetermineDFromIRLBA(SickleJr, d = 50)

Arguments

Value

An object of class SickleJr with plots for IRLBA diagnostics added to its plots slot

References

Baglama J, Reichel L (2005). “Augmented Implicitly Restarted Lanczos Bidiagonalization Methods.” SIAM Journal on Scientific Computing, 27(1), 19–42. ISSN 10648275, doi:10.1137/04060593X.

Examples

SimSickleJrSmall<-DetermineDFromIRLBA(SimSickleJrSmall,d=5)

Initialize the \mathbf{W} matrices in each modality and the shared \mathbf{H} matrix

Description

Create the \mathbf{W}^v matrices and \mathbf{H} matrix via non-negative double singular value decomposition (NNDSVD) (Boutsidis and Gallopoulus 2008; Gaujoux and Seoighe 2010) or randomization. For randomization, the algorithm runs for 10 rounds for the desired number of random initializations and picks the \mathbf{W}^v matrices and \mathbf{H} matrix with the lowest achieved loss.

Usage

GenerateWmatricesandHmatrix(
  SickleJr,
  d = 10,
  random = FALSE,
  numberReps = 100,
  seed = 5,
  minibatch = FALSE,
  batchsize = -1,
  random_W_updates = FALSE,
  subsample = 1:dim(SickleJr@count.matrices[[1]])[2],
  usesvd = FALSE
)

Arguments

Value

SickleJr An object of class SickleJr with the \mathbf{W}^v matrices and \mathbf{H} matrix added.

References

Boutsidis C, Gallopoulos E (2008). “SVD based initialization: A head start for nonnegative matrix factorization.” Pattern Recognition, 41(4), 1350–1362. ISSN 00313203, doi:10.1016/J.PATCOG.2007.09.010.

Gaujoux R, Seoighe C (2010). “A flexible R package for nonnegative matrix factorization.” BMC Bioinformatics, 11(1), 1–9. ISSN 14712105, doi:10.1186/1471-2105-11-367, https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-367.

Examples

SimSickleJrSmall<-SetLambdasandRowReg(SimSickleJrSmall,
lambdaWlist=list(10,50),lambdaH=500,rowReg="None")
SimSickleJrSmall<-GenerateWmatricesandHmatrix(SimSickleJrSmall,d=5,usesvd=TRUE)

Plot a diagnostic plot for the mini-batch algorithm

Description

To ensure sufficient convergence of the loss for jrSiCKLSNMF with mini-batch updates, we plot the loss vs the number of iterations for the mini-batch algorithm. After a certain number of iterations, the loss should appear to oscillate around a value. Before continuing with downstream analyses, please ensure that the loss exhibits this sort of behavior. For the mini-batch algorithm, it is not possible to use the convergence criteria used for the batch version of the algorithm.

Usage

MinibatchDiagnosticPlot(SickleJr)

Arguments

Value

An object of class SickleJr with mini-batch diagnostic plots added to the plots slot.

Examples

SimSickleJrSmall<-MinibatchDiagnosticPlot(SimSickleJrSmall)

Normalize the count matrices and set whether to use the Poisson KL divergence or the Frobenius norm

Description

Normalize the count data within each modality. The default normalization, which should be used when using the KL divergence, is median library size normalization (Zheng et al. 2017; Elyanow et al. 2020). To perform median library size normalization, each count within a cell is divided by its library size (i.e. the counts within a column are divided by the column sum). Then, all values are multiplied by the median library size (i.e. the median column sum). To use the Frobenius norm, set frob=TRUE to log(x+1) normalize your count data and use a desired scaleFactor. You may also use a different form of normalization and store these results in the normalized.count.matrices slot.

Usage

NormalizeCountMatrices(SickleJr, diffFunc = "klp", scaleFactor = NULL)

Arguments

Value

An object of class SickleJr with a list of sparse, normalized data matrices added to its normalized.count.matrices slot

References

Elyanow R, Dumitrascu B, Engelhardt BE, Raphael BJ (2020). “NetNMF-SC: Leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis.” Genome Research, 30(2), 195–204. ISSN 15495469, doi:10.1101/gr.251603.119, https://pubmed.ncbi.nlm.nih.gov/31992614/.

Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, Gregory MT, Shuga J, Montesclaros L, Underwood JG, Masquelier DA, Nishimura SY, Schnall-Levin M, Wyatt PW, Hindson CM, Bharadwaj R, Wong A, Ness KD, Beppu LW, Deeg HJ, McFarland C, Loeb KR, Valente WJ, Ericson NG, Stevens EA, Radich JP, Mikkelsen TS, Hindson BJ, Bielas JH (2017). “Massively parallel digital transcriptional profiling of single cells.” Nature Communications, 8. ISSN 20411723, doi:10.1038/NCOMMS14049, https://pubmed.ncbi.nlm.nih.gov/28091601/.

Examples

SimSickleJrSmall<-NormalizeCountMatrices(SimSickleJrSmall)
SimSickleJrSmall<-NormalizeCountMatrices(SimSickleJrSmall, diffFunc="fr",scaleFactor=1e6)

Create plots to help determine the number of latent factors

Description

Generate plots of the lowest achieved loss after a pre-specified number of iterations (default 100) for each latent factor (defaults to 2:20). This operates similarly to a scree plot, so please select a number of latent factors that corresponds to the elbow of the plot. This method is not appropriate for larger sets of data (more than 1000 cells)

Usage

PlotLossvsLatentFactors(
  SickleJr,
  rounds = 100,
  differr = 1e-04,
  d_vector = c(2:20),
  parallel = FALSE,
  nCores = detectCores() - 1,
  subsampsize = NULL,
  minibatch = FALSE,
  random = FALSE,
  random_W_updates = FALSE,
  seed = NULL,
  batchsize = -1,
  lossonsubset = FALSE,
  losssubsetsize = dim(SickleJr@count.matrices[[1]])[2]
)

Arguments

Value

An object of class SickleJr with a list of initialized \mathbf{W}^v matrices and an \mathbf{H} matrix for each latent factor d\in\{1,...,D\} added to the WHinitials slot, a data frame holding relevant values for plotting the elbow plot added to the latent.factor.elbow.values slot, diagnostic plots of the loss vs. the number of latent factors added to the plots slot, and the cell indices used to calculate the loss on the subsample added to the lossCalcSubSample slot

References

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis, 2 edition. Springer International Publishing, Cham, Switzerland. ISBN 978-3-319-24277-4, doi:10.1007/978-3-319-24277-4, https://ggplot2.tidyverse.org/.

Examples

SimSickleJrSmall@latent.factor.elbow.values<-data.frame(NULL,NULL)
SimSickleJrSmall<-PlotLossvsLatentFactors(SimSickleJrSmall,d_vector=c(2:5),
rounds=5,parallel=FALSE)
#Next, we commute 2 of these in parallel.
## Not run: 
SimSickleJrSmall<-PlotLossvsLatentFactors(SimSickleJrSmall,
d_vector=c(6:7),rounds=5,parallel=TRUE,nCores=2)
## End(Not run)

Generate UMAP plots for an object of class SickleJr

Description

Plot the first and second dimensions of a UMAP dimension reduction and color either by clustering results or metadata.

Usage

PlotSickleJrUMAP(
  SickleJr,
  umap.modality = "H",
  cluster = "kmeans",
  title = "",
  colorbymetadata = NULL,
  legendname = NULL
)

Arguments

Value

An object of class SickleJr with plots added to the plots slot

References

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis, 2 edition. Springer International Publishing, Cham, Switzerland. ISBN 978-3-319-24277-4, doi:10.1007/978-3-319-24277-4, https://ggplot2.tidyverse.org/.

Examples

SimSickleJrSmall<-PlotSickleJrUMAP(SimSickleJrSmall,
title="K-Means Example")
SimSickleJrSmall<-PlotSickleJrUMAP(SimSickleJrSmall,umap.modality=1)

Run jrSiCKLSNMF on an object of class SickleJr

Description

Wrapper function to run jrSiCKLSNMF on an object of class SickleJr. Performs jrSiCKLSNMF on the given SickleJr

Usage

RunjrSiCKLSNMF(
  SickleJr,
  rounds = 30000,
  differr = 1e-06,
  display_progress = TRUE,
  lossonsubset = FALSE,
  losssubsetsize = dim(SickleJr@H)[1],
  minibatch = FALSE,
  batchsize = 1000,
  random_W_updates = FALSE,
  seed = NULL,
  minrounds = 200,
  suppress_warnings = FALSE,
  subsample = 1:dim(SickleJr@normalized.count.matrices[[1]])[2]
)

Arguments

Value

An object of class SickleJr with updated \mathbf{W}^v matrices, updated \mathbf{H} matrix, and a vector of values for the loss function added to the Wlist, H, and loss slots, respectively

References

Cai D, He X, Wu X, Han J (2008). “Non-negative matrix factorization on manifold.” Proceedings - IEEE International Conference on Data Mining, ICDM, 63–72. ISSN 15504786, doi:10.1109/ICDM.2008.57.

Greene D, Cunningham P (2009). “A matrix factorization approach for integrating multiple data views.” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5781 LNAI(PART 1), 423–438. ISSN 03029743, doi:10.1007/978-3-642-04180-8_45, https://link.springer.com/chapter/10.1007/978-3-642-04180-8_45.

Eddelbuettel D, François R (2011). “Rcpp: Seamless R and C++ Integration.” Journal of Statistical Software, 40(8), 1–18. doi:10.18637/jss.v040.i08.

Eddelbuettel D, Sanderson C (2014). “RcppArmadillo: Accelerating R with high-performance C++ linear algebra.” Computational Statistics and Data Analysis, 71, 1054–1063. http://dx.doi.org/10.1016/j.csda.2013.02.005.

Elyanow R, Dumitrascu B, Engelhardt BE, Raphael BJ (2020). “NetNMF-SC: Leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis.” Genome Research, 30(2), 195–204. ISSN 15495469, doi:10.1101/gr.251603.119, https://pubmed.ncbi.nlm.nih.gov/31992614/.

Le Roux J, Weniger F, Hershey JR (2015). “Sparse NMF: half-baked or well done?” Mitsubishi Electric Research Laboratories (MERL), Cambridge.

Lee DD, Seung HS (2000). “Algorithms for Non-negative Matrix Factorization.” In Leen T, Dietterich T, Tresp V (eds.), Advances in Neural Information Processing Systems, volume 13. https://proceedings.neurips.cc/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf.

Liu J, Wang C, Gao J, Han J (2013). “Multi-view clustering via joint nonnegative matrix factorization.” Proceedings of the 2013 SIAM International Conference on Data Mining, 252–260. doi:10.1137/1.9781611972832.28.

Examples

SimSickleJrSmall<-RunjrSiCKLSNMF(SimSickleJrSmall,rounds=5)

Set lambda values and type of row regularization for an object of class SickleJr

Description

Provide the values for the graph regularization \lambda_{\textbf{W}^v} for each modality as a list, provide the value for the sparsity constraint \lambda_{\mathbf{H}}, and select whether to use L2 norm regularization.

Usage

SetLambdasandRowReg(
  SickleJr,
  lambdaWlist = list(10, 50),
  lambdaH = 500,
  rowReg = "None"
)

Arguments

Value

An object of class SickleJr with the lambda hyperparameter values added to its lambdaWlist and lambdaH slots and with the row regularization value added to its rowRegularization slot

Examples

SimSickleJrSmall<-SetLambdasandRowReg(SimSickleJrSmall,
lambdaWlist=list(10,50),lambdaH=500,rowReg="None")
SimSickleJrSmall<-SetLambdasandRowReg(SimSickleJrSmall,
lambdaWlist=list(3,15),lambdaH=0,rowReg="L2Norm")

Set \mathbf{W} matrices and \mathbf{H} matrix from pre-calculated values

Description

Use values calculated in the step to determine number of latent factors in the initial steps for the jrSiCKLSNMF algorithm. If only a subset was calculated, this produces an error. In this case, please use GenerateWmatricesandHmatrix to generate new \mathbf{W}^v matrices and a new \mathbf{H} matrix.

Usage

SetWandHfromWHinitials(SickleJr, d)

Arguments

Value

An object of class SickleJr with the Wlist slot and the H slot filled from pre-calculated values.

Examples

SimSickleJrSmall<-SetWandHfromWHinitials(SimSickleJrSmall,d=5)

The SickleJr class

Description

Defines the SickleJr class for use with jrSiCKLSNMF. This object contains all of the information required for analysis using jrSiCKLSNMF. This includes count matrices, normalized matrices, graph Laplacians, hyperparameters, diagnostic plots, and plots of cell clusters.

Value

An object of class SickleJr

Slots

count.matrices

A list containing all of the quality controlled count matrices. Note that these count matrices should not use all features and should only include features that appear in at a minimum 10 cells.

normalized.count.matrices

A list that holds the normalized count matrices

graph.laplacian.list

A list of the graph Laplacians to be used for graph regularization

rowRegularization

A string that indicates the type of row regularization to use. Types include "None" and "L2Norm"

diffFunc

A string that holds the name of the function used to measure the discrepancy between data matrix X and WH for each modality; can be "klp" for the Poisson Kullback-Leibler divergence or "fr" for the Frobenius norm

lambdaWlist

A list of lambda values to use as the hyperparameters for the corresponding \mathbf{W}^v in the v^{\text{th}} modality

lambdaH

A numeric value corresponding to the hyperparameter of the sparsity constraint on \mathbf{H}

Wlist

A list of the generated \mathbf{W}^v matrices, one for each modality

H

The shared \mathbf{H} matrix

WHinitials

A list that if, when using PlotLossvsLatentFactors, all of the cells are used to calculate the initial values, stores these initial generated matrices; can be used as initializations when running RunjrSiCKLSNMF to save time

lossCalcSubsample

A vector that holds the cell indices on which PlotLossvsLatentFactors was calculated

latent.factor.elbow.values

A data frame that holds the relevant information to plot the latent factor elbow plot

minibatch

Indicator variable that states whether the algorithm should use mini-batch updates.

clusterdiagnostics

List of the cluster diagnostic results for the SickleJr object. Includes diagnostic plots from fviz_nbclust and and diagnostics from clValid

clusters

List of results of different clustering methods performed on the SickleJr object

metadata

List of metadata

loss

Vector of the value for the loss function

umap

List of different UMAP-based dimension reductions using umap

plots

Holds various ggplot results for easy access of diagnostics and cluster visualizations

A simulated dataset for use with jrSiCKLSNMF

Description

A simulated dataset with \mathcal{U}(1,1.25) multiplicative noise for the scRNA-seq variability parameter in SPARSim for the simulated scRNA-seq data and with \mathcal{N}(-0.25,0.25) additive noise to the expression levels of the scATAC-seq data for data simulated via SimATAC. The simulated matrices are located in SimData$Xmatrices and the identities for the cell types are contained in SimData$cell_type. This corresponds to the Xmatrix data found in both

XandLmatrices25/XandindividLKNNLmatrices1Sparsity5.RData and

XandBulkLmatrix25/XandBulkLKNNmatrices1Sparsity5.RData on our Github

ellisdoro/jrSiCKLSNMF_Simulations

Usage

data(SimData)

Format

A list made up of a two items. The first is list of 2 simulated sparse matrices and the second is a vector containing cell identities.

Xmatrices

A list of 2 sparse matrices, each containing a different simulated omics modality measured on the same set of single cells: the first entry in the list corresponds to simulated scRNA-seq data and has 1000 genes and 300 cells; the second entry in the list corresponds to simulated scATAC-seq data and has 5910 peaks and 300 cells.

cell_type

A vector containing the cell-type identities of the simulated data

Source

jrSicKLSNMF Simulations

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.

Description

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.

Usage

data(SimSickleJrSmall)

Format

A SickleJr object containing a completed analysis using jrSiCKLSNMF

count.matrices

Contains a list of 2 sparse matrices, each containing a different simulated omics modality measured on the same set of single cells

normalized.count.matrices

The normalized versions of the count matrices contained in slot count.matrices

graph.laplacian.list

A list of sparse matrices containing the graph Laplacians corresponding to the KNN feature-feature similarity graphs constructed for each omics modality

rowRegularization

A string indicating the row regularization: here it is set to "None"

diffFunc

A string specifying the function to measure the discrepancy between the normalized data and the fitted matrices: here, it is set to "klp" for the Poisson Kullback-Leibler divergence

lambdaWlist

A list holding the graph regularization parameters: here, they are 10 and 50

lambdaH

A numeric indicating the value for the sparsity parameter. Here it is equals 500

Wlist

A list holding the fitted \mathbf{W}^v matrices

H

A matrix holding \mathbf{H}

WHinitials

A list of initial values for \mathbf{W}^v and \mathbf{H}

lossCalcSubsample

A vector containing a subset on which to calculate the loss

latent.factor.elbow.values

A data frame holding the loss and the number of latent factor that is used for diagnostic plots

minibatch

A Boolean indicating whether or not to use the mini-batch algorithm: FALSE here

clusterdiagnostics

Diagnostic plots and results

clusters

A list holding the "kmeans" clustering results

metadata

A list holding metadata; here this is just cell type information

loss

A list holding a vector called "Loss"

umap

A list holding various UMAP approximations

plots

A list holding ggplots corresponding to different diagnostics and visualizations

Source

jrSicKLSNMF Simulations

Run jrSiCKLSNMF outside of a SickleJr object

Description

Perform joint non-negative matrix factorization (NMF) across multiple modalities of single-cell data. To measure the discrepancy between two distributions, one can use the Poisson Kullback-Leibler divergence (\mathtt{diffFunc}=\mathtt{"klp"}) or the Frobenius norm (\mathtt{diffFunc}=\mathtt{"fr"}). It is also possible to set graph regularization constraints on \mathbf{W}^v and either a sparsity constraint on \mathbf{H} or an L2 norm constraint on the rows of \mathbf{H}. This function passes by reference and updates the variables \mathtt{WL} and \mathtt{H} and does not require data to be in an object of type SickleJr. \mathtt{RunjrSiCKLSNMF} calls this function. If your data are in an object of class SickleJr, please use the \mathtt{RunjrSiCKLSNMF} function instead.

Usage

jrSiCKLSNMF(
  datamatL,
  WL,
  H,
  AdjL,
  DL,
  lambdaWL,
  lambdaH,
  initsamp,
  suppress_warnings,
  diffFunc = "klp",
  Hconstraint = "None",
  differr = 1e-06,
  rounds = 1000L,
  display_progress = TRUE,
  minibatch = TRUE,
  batchsize = 100L,
  random_W_updates = TRUE,
  minrounds = 100L
)

Arguments

Value

An R list containing values for the objective function.

References

Cai D, He X, Wu X, Han J (2008). “Non-negative matrix factorization on manifold.” Proceedings - IEEE International Conference on Data Mining, ICDM, 63–72. ISSN 15504786, doi:10.1109/ICDM.2008.57.

Greene D, Cunningham P (2009). “A matrix factorization approach for integrating multiple data views.” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5781 LNAI(PART 1), 423–438. ISSN 03029743, doi:10.1007/978-3-642-04180-8_45, https://link.springer.com/chapter/10.1007/978-3-642-04180-8_45.

Eddelbuettel D, François R (2011). “Rcpp: Seamless R and C++ Integration.” Journal of Statistical Software, 40(8), 1–18. doi:10.18637/jss.v040.i08.

Eddelbuettel D, Sanderson C (2014). “RcppArmadillo: Accelerating R with high-performance C++ linear algebra.” Computational Statistics and Data Analysis, 71, 1054–1063. http://dx.doi.org/10.1016/j.csda.2013.02.005.

Elyanow R, Dumitrascu B, Engelhardt BE, Raphael BJ (2020). “NetNMF-SC: Leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis.” Genome Research, 30(2), 195–204. ISSN 15495469, doi:10.1101/gr.251603.119, https://pubmed.ncbi.nlm.nih.gov/31992614/.

Le Roux J, Weniger F, Hershey JR (2015). “Sparse NMF: half-baked or well done?” Mitsubishi Electric Research Laboratories (MERL), Cambridge.

Lee DD, Seung HS (2000). “Algorithms for Non-negative Matrix Factorization.” In Leen T, Dietterich T, Tresp V (eds.), Advances in Neural Information Processing Systems, volume 13. https://proceedings.neurips.cc/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf.

Liu J, Wang C, Gao J, Han J (2013). “Multi-view clustering via joint nonnegative matrix factorization.” Proceedings of the 2013 SIAM International Conference on Data Mining, 252–260. doi:10.1137/1.9781611972832.28.