Project new data into a known factor space — project

This function uses the feature weights learned by a MOFA2 model to project unseen data to the factor space

Usage

project_data(model, test_data)

Arguments

model: A MOFA2 model.
test_data: A dataframe in multiview representation as generated by MOFAcellulaR::pb_dat2MOFA

Value

A matrix with samples in rows and projected factors in columns

Details

This function calculates the pseudoinverse of the feature weights of a MOFA2 model and uses it to project unseen data into the factor space. Only shared features are used for the projection

Examples

inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR")
load(file.path(inputs_dir, "testpbcounts.rda"))
load(file.path(inputs_dir, "testcoldata.rda"))
pb_obj <- create_init_exp(counts = testpbcounts,  coldata = testcoldata)

ct_list <- filt_profiles(pb_dat = pb_obj,
                         cts = c("Fib","CM"),
                         ncells = 5,
                         counts_col = "cell_counts",
                         ct_col = "cell_type")

ct_list <- filt_gex_byexpr(pb_dat_list = ct_list,
                           min.count = 5,
                           min.prop = 0.25)
#> Warning: All samples appear to belong to the same group.
#> Warning: All samples appear to belong to the same group.

ct_list <- tmm_trns(pb_dat_list = ct_list,
                    scale_factor = 1000000)

ct_list <- center_views(pb_dat_list = ct_list)

multiview_dat <- pb_dat2MOFA(pb_dat_list = ct_list)

trained_model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5"))
#> Warning: Factor(s) 1 are strongly correlated with the total number of expressed features for at least one of your omics. Such factors appear when there are differences in the total 'levels' between your samples, *sometimes* because of poor normalisation in the preprocessing steps.

projected_factors <- project_data(model = trained_model, test_data = multiview_dat)