Multimodal Single-Cell Omics Dimensionality Reduction
Add metadata to an object of class SickleJr
Build KNN graphs and generate their graph Laplacians
Build SNN graphs and generate their graph Laplacians
Calculate the UMAP for an object of class SickleJr
Cluster the matrix
Create an object of class SickleJr
Perform clustering diagnostics
Create elbow plots of the singular values derived from IRLBA to determ...
Initialize the matrices in each modality and the shared $...
Run jrSiCKLSNMF outside of a SickleJr object
Plot a diagnostic plot for the mini-batch algorithm
Normalize the count matrices and set whether to use the Poisson KL div...
Create plots to help determine the number of latent factors
Generate UMAP plots for an object of class SickleJr
Run jrSiCKLSNMF on an object of class SickleJr
Set lambda values and type of row regularization for an object of clas...
Set matrices and matrix from pre-calculated ...
The SickleJr class
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>.