RcppPlanc, Fast NMF and iNMF Implementation with C++

Running Non-negative Matrix Factorization (NMF)

NMF problem is stated with the objective below and it aims at factorizing a given non-negative X matrix into two low-rank non-negative matrices W and H so that WH ≈ X. Denote the dimensionality of X to be m features by n sample points, then W will have the size of m × k and H is of k × n. k is the inner dimension of the factorization and should be less than m and n.

The implementation currently supports the factorization of both dense and sparse (dgCMatrix) matrix. The following code chunk factorizes a randomly initialized non-negative dense matrix.

mat <- matrix(runif(50*100), nrow = 50, ncol = 100)
res <- nmf(mat, k = 10)

The output result res is a list object with entries res$W and res$H for the output matrices as described above. Note that here matrix res$H is transposed (i.e. n × k) for the sake of efficient implementation. res$objErr is the objective error computed for the result.

There are other variant algorithms for NMF that can be specified with argument algo= and also symmetric NMF approach implemented in symNMF(). Please see ?nmf or ?symNMF for more detailed documentation of the functions.

Running Integrative Non-negative Matrix Factorization (iNMF)

iNMF jointly factorizes multiple datasets that share the same set of features. It is originally designed for integrating single-cell biological data, such as transcriptomics and other modalities. As its objective function stated as below, it factorizes given datasets E_i (m × n_i) into W (m × k), V_i (m × k) and H_i (k × n_i), where W is shared across all datasets and V and H are dataset specific. λ is regularization parameter, m is the number of shared features (e.g. gene expression), n_i is the number of sample points (e.g. cells) of each dataset and d is the number of datasets, It is expected that (W + V_i)H_i ≈ E_i. Please see ?inmf for more detailed explanation.

We prepared some example datasets that are down-sampled from public available study (Hyun Min Kang, 2018). They can be loaded with the following code chunk. These datasets are presented as sparse matrices.

data("ctrl.sparse")
data("stim.sparse")

To run iNMF with the datasets in hand, simply create a list of them and call the function inmf().

res <- inmf(list(ctrl.sparse, stim.sparse), k = 20, lambda = 5)

The returned result is also a list object, which contains the following entries:

• res$H, a list of dataset specific H matrices, each of size n_i × k. Similarly, they are also transposed.
• res$V, a list of dataset specific V matrices, each of size m × k.
• res$W, the W matrix, m × k.
• res$objErr, the objective error value calculated for the result.

We also implemented the other variant of iNMF in onlineINMF() (C. Gao, 2021) and uinmf() (A.R. Kriebel, 2022). Please see their help pages for the detail.

Using Data Stored in HDF5 Files for iNMF

HDF5 files have been widely adpoted for storing large-scale datasets on hard drive. Relevant examples can be found such as the H5AD files used by Python AnnData package that store annotated data, and the output file of 10X CellRanger Count which stores pre-processed single-cell expression matrix and metadata.

We support using either dense matrices stored in 2-D datasets or CSC (compressed sparse column) sparse matrices stored with sets of three 1-D array datasets. Currently, we don’t provide interfaces for interactively exploring the data in an HDF5, but this can be easily achieved with other packages, such as hdf5r. To use datasets stored in HDF5 files, we provide argument list constructors H5Mat() and H5SpMat(), respectively for dense and sparse matrix, to organize the necessary information for retrieving data.

We prepared example HDF5 files with identical information as the package data ctrl.sparse and stim.sparse, in both dense and sparse forms. The argument lists for them can be constructed as shown below:

ctrl.denseH5FilePath <- system.file("extdata/ctrl_dense.h5", package = "RcppPlanc")
ctrl.h5dense <- H5Mat(filename = ctrl.denseH5FilePath, dataPath = "data")
ctrl.h5dense
#> Argument list for constructing HDF5 dense matrix
#> filename:  /tmp/Rtmpgcqs9I/Rinst9ae1028d78a/RcppPlanc/extdata/ctrl_dense.h5
#> data path: data

stim.denseH5FilePath <- system.file("extdata/stim_dense.h5", package = "RcppPlanc")
stim.h5dense <- H5Mat(filename = stim.denseH5FilePath, dataPath = "data")
stim.h5dense
#> Argument list for constructing HDF5 dense matrix
#> filename:  /tmp/Rtmpgcqs9I/Rinst9ae1028d78a/RcppPlanc/extdata/stim_dense.h5
#> data path: data

ctrl.sparseH5FilePath <- system.file("extdata/ctrl_sparse.h5", package = "RcppPlanc")
ctrl.h5sparse <- H5SpMat(filename = ctrl.sparseH5FilePath, 
                         valuePath = "scaleDataSparse/data", rowindPath = "scaleDataSparse/indices", colptrPath = "scaleDataSparse/indptr",
                         nrow = nrow(ctrl.sparse), ncol = ncol(ctrl.sparse))
ctrl.h5sparse
#> Argument list for constructing HDF5 CSC sparse matrix
#> filename:    /tmp/Rtmpgcqs9I/Rinst9ae1028d78a/RcppPlanc/extdata/ctrl_sparse.h5
#> value path:  scaleDataSparse/data
#> rowind path: scaleDataSparse/indices
#> colptr path: scaleDataSparse/indptr
#> dimension:   173 x 300

stim.sparseH5FilePath <- system.file("extdata/stim_sparse.h5", package = "RcppPlanc")
stim.h5sparse <- H5SpMat(filename = stim.sparseH5FilePath,
                         valuePath = "scaleDataSparse/data", rowindPath = "scaleDataSparse/indices", colptrPath = "scaleDataSparse/indptr",
                         nrow = nrow(stim.sparse), ncol = ncol(stim.sparse))
stim.h5sparse
#> Argument list for constructing HDF5 CSC sparse matrix
#> filename:    /tmp/Rtmpgcqs9I/Rinst9ae1028d78a/RcppPlanc/extdata/stim_sparse.h5
#> value path:  scaleDataSparse/data
#> rowind path: scaleDataSparse/indices
#> colptr path: scaleDataSparse/indptr
#> dimension:   173 x 300

With the argument list constructed, we can simply run any iNMF algorithm with having them in a list, which is the same as what we did with in-memory matrix objects.

Note that it is recommended to apply onlineINMF() with HDF5 files due to the nature of the HALS algorithm it adopts. inmf() and uinmf() use ANLS updating approach which requires subsetting on rows of the data and could be very slow. Although this is still supported. Optimization might be updated in future versions.

res <- onlineINMF(list(ctrl.h5sparse, stim.h5sparse), k = 20, lambda = 5, minibatchSize = 50)

Here, the result is also a similar list object but with two additional entries res$A and res$B. These are dataset specific matrices storing the algorithm update information but not the primary result of iNMF. These are needed for performing other scenarios of online iNMF. Please see ?onlineINMF for more explanation.

Note that the argument minibatchSize is set to 50 only for demonstrating this minimal example of this vignette. This is a parameter specifying the chunk size of each updates in the iterative algorithm, which obviously should be a smaller number than the number of sample points in provided datasets. Users testing with real life large-scale data should ignore it or determine it basing on the specification (e.g. RAM) of the machines.