context("Prepare the model from different objects")
library(MOFA2)
test_that("a MOFA model can be prepared from a list of matrices", {
m <- as.matrix(read.csv('matrix.csv'))
# Set feature names
rownames(m) <- paste("feature_", seq_len(nrow(m)), paste = "", sep = "")
# Set sample names
colnames(m) <- paste("sample_", seq_len(ncol(m)), paste = "", sep = "")
mofa_model <- create_mofa(list("view1" = m))
model_opts <- get_default_model_options(mofa_model)
model_opts$num_factors <- 10
expect_is(prepare_mofa(mofa_model, model_options = model_opts), "MOFA")
})
test_that("a model can be created from a list of sparse matrices", {
skip_if_not_installed("Matrix")
# Generate a sparse matrix
m <- matrix(rnorm(100 * 5), ncol = 5) %*% t(matrix(rnorm(5 * 50), ncol = 5))
m[sample(1:nrow(m), 100, replace = TRUE), sample(1:ncol(m), 100, replace = TRUE)] <- 0
library(Matrix)
m <- Matrix(m, sparse = TRUE)
# Set feature names
rownames(m) <- paste("feature_", seq_len(nrow(m)), paste = "", sep = "")
# Set sample names
colnames(m) <- paste("sample_", seq_len(ncol(m)), paste = "", sep = "")
# Initialise a model
mofa_model <- create_mofa(list("view1" = m))
model_opts <- get_default_model_options(mofa_model)
model_opts$num_factors <- 10
# Test if a sparse matrix can be used to prepare the MOFA model for training
expect_is(prepare_mofa(mofa_model, model_options = model_opts), "MOFA")
})
test_that("a model can be created from a Seurat object", {
skip_if_not_installed("Seurat")
skip_if_not_installed("SeuratObject")
library(Seurat)
library(Matrix)
m <- readMM('matrix.mtx')
genes <- read.delim('genes.tsv', sep='\t', header=FALSE, stringsAsFactors=FALSE)[,2]
cells <- read.delim('barcodes.tsv', sep='\t', header=FALSE, stringsAsFactors=FALSE)[,1]
colnames(m) <- cells
rownames(m) <- genes
srt <- SeuratObject::CreateSeuratObject(m)
mofa_model <- create_mofa(srt, features = genes, layer = "counts")
model_opts <- get_default_model_options(mofa_model)
model_opts$num_factors <- 10
# Test if a Seurat object can be used to prepare the MOFA model for training
expect_is(prepare_mofa(mofa_model, model_options = model_opts), "MOFA")
})
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