A simulated dataset generated using zinb_simdata() for demonstrating network inference functions in the scGraphVerse package.
Usage
data(toy_counts)Format
A MultiAssayExperiment object containing 3 SingleCellExperiment objects (experiments), each with 10 genes x 40 cells. The data was simulated using a known ground truth network (toy_adj_matrix) with zero-inflated negative binomial distributions.
Examples
data(toy_counts)
toy_counts
#> A MultiAssayExperiment object of 3 listed
#> experiments with user-defined names and respective classes.
#> Containing an ExperimentList class object of length 3:
#> [1] experiment_1: SingleCellExperiment with 35 rows and 40 columns
#> [2] experiment_2: SingleCellExperiment with 35 rows and 40 columns
#> [3] experiment_3: SingleCellExperiment with 35 rows and 40 columns
#> Functionality:
#> experiments() - obtain the ExperimentList instance
#> colData() - the primary/phenotype DataFrame
#> sampleMap() - the sample coordination DataFrame
#> `$`, `[`, `[[` - extract colData columns, subset, or experiment
#> *Format() - convert into a long or wide DataFrame
#> assays() - convert ExperimentList to a SimpleList of matrices
#> exportClass() - save data to flat files
# Access individual experiments
MultiAssayExperiment::experiments(toy_counts)
#> ExperimentList class object of length 3:
#> [1] experiment_1: SingleCellExperiment with 35 rows and 40 columns
#> [2] experiment_2: SingleCellExperiment with 35 rows and 40 columns
#> [3] experiment_3: SingleCellExperiment with 35 rows and 40 columns
# Use directly with infer_networks
networks <- infer_networks(
count_matrices_list = toy_counts,
method = "GENIE3",
nCores = 1
)