I am doing Non-Negative Matrix Factorization (NMF) of a matrix A in R. It has Genes on rows and Samples on the columns. For NMF, I am using the CRAN package NMF. Once the basis matrix W and coefficient matrix H are computed, I want to check whether the factorisation was accurate enough. For that, I am trying to calculate the dot product of W and H so as to check whether I can get the original matrix A back.
Although the inner dimensions of basis matrix W and coefficient matrix H are same (rank = 3), I cannot multiply them. I keep getting the following error. This one line error is not very helpful.
Error in x * y : non-conformable arrays
Reproducible code
# Make a 10 * 10 matrix
ranVal= sample(1:10,100,replace =T)
M=matrix(ranVal,nrow=10)
# Rename the rows and columns
rownames(M) <- paste(rep("gene", 10),c(1:10), sep = "_")
colnames(M) <- paste(rep("sample", 10),c(1:10), sep = "_")
# See how it looks
M
# NMF
library(NMF)
tempRes <- nmf(x = M, rank = 3, .options = c("pv"))
# NMF implementation from Brunet et al. (2004)
result <- nmf_update.brunet_R(i = 1, v = M, x = tempRes, eps=.Machine$double.eps)
# Basis Matrix
W <- .basis(result);
# Coeff Matrix
H <- .coef(result)
# Checking dimensions
dim(W)
dim(H)
# Dot product
A <- W*H # Error: non-conformable arrays
A <- geometry::dot(W,H) # Error: non-conformable arrays
How should I go about it? Is there a different way to check it? Thanks!
What about W %*% H?
W %*% H
sample_1 sample_2 sample_3 sample_4 sample_5 sample_6
gene_1 4.406480 6.392739 9.5742884 5.164528 5.074363 3.549958
gene_2 7.165909 4.960877 2.3608356 5.718255 8.405870 5.817709
gene_3 5.070350 3.804750 3.2464367 3.906296 6.383760 4.810851
gene_4 8.356959 4.181312 0.9964025 5.167783 10.955091 8.533261
gene_5 4.671284 5.157582 11.0938515 3.059027 7.946619 7.730591
gene_6 5.899437 6.666820 6.0368747 6.705011 5.718862 2.998982
gene_7 7.801179 7.052729 1.3482074 8.775604 6.398204 2.076385
gene_8 3.501642 4.410587 9.1595473 2.720517 5.693630 5.401830
gene_9 5.271008 7.975740 9.1787413 7.358409 4.472696 1.753683
gene_10 5.855654 4.396895 1.0050132 5.424544 5.950780 3.326661
sample_7 sample_8 sample_9 sample_10
gene_1 6.781347 5.909006 8.431049 4.716241
gene_2 4.884002 6.815600 4.339132 4.531809
gene_3 4.015448 5.548575 3.376917 2.836615
gene_4 4.446931 8.554956 1.793168 3.014138
gene_5 6.644475 8.692351 5.003496 1.000724
gene_6 6.364861 5.479570 8.536569 6.593015
gene_7 5.804209 4.769660 8.798081 9.175741
gene_8 5.469769 6.475237 4.801630 1.365611
gene_9 7.673524 5.183927 11.371303 7.760969
gene_10 3.915477 4.571069 4.548770 5.005138