simulate_coexpression: Simulate co-expression patterns on an already-simulated...
In ConesaLab/acorde: Isoform co-usage networks from single-cell RNA-seq data

simulate_coexpression

R Documentation

Simulate co-expression patterns on an already-simulated scRNA-seq matrix

Description

Using a existing simulated scRNA-seq matrix, this function creates co-expression relationships between the features, following the cell type-specific patterns of high/low expression supplied by the user. In particular, the output of the SymSim simulator is expected as input (see SymSim package documentation for details).

Usage

simulate_coexpression(sim_data, feature_no, patterns, cluster_size)

Arguments

`sim_data`	A `SingleCellExperiment` object containing an already-simulated scRNA-seq matrix.
`feature_no`	An integer indicating the number of high expression ("top") and low expression ("bottom") features to be selected for co-expression simulation. Note that `feature_no*2` features will be used in total.
`patterns`	A `data.frame` or `tibble` containing cell types as columns (ordered as in the `colData` slot in `sim_data`) and co-expression clusters as rows. For each co-expression cluster, a logical vector indicating the desired expression pattern must be provided, in a row-wise manner. Insert `TRUE` if high expression in that cell type is desired, `FALSE` if the opposite.
`cluster_size`	An integer indicating the number of features to include per cluster.

Value

A list, containing two objects:

sim_matrix: a tibble containing the same number of cells as in sim_data in the columns and feature_no*2 in the rows. Feature IDs are defined in the feature column
sim_clusters: a list with as many elements as simulated clusters, where each element contains all feature IDs that were simulated to follow the same co-expression pattern (that is, the clusters).