Cross-Modality In Silico Generation & Translation Fidelity

Evaluates the biological accuracy and reconstruction fidelity of cross-modality generation (e.g. predicting scATAC from scRNA or vice versa; Zhai et al., Genome Biology 2024). Computes cell-wise and feature-wise Pearson and Spearman correlations, cosine similarity, RMSE, and MAE between predicted and measured multi-omics profiles.

Usage

calc_cross_modal_generation(true_data, pred_data)

Arguments

true_data

Matrix or data.frame of measured features across cells (features x cells).

pred_data

Matrix or data.frame of in-silico generated / predicted features (features x cells).

Value

A list containing cell-wise and feature-wise correlation, cosine similarity, RMSE, and MAE.

Examples

data(example_multiomics, package = "scSimEval")
r_rna <- example_multiomics$ref_multi$rna
r_atac <- example_multiomics$ref_multi$atac
calc_cross_modal_generation(r_rna, r_atac)
#> $mean_cell_pcc
#> [1] 0.07326002
#> 
#> $median_cell_pcc
#> [1] 0.06511867
#> 
#> $mean_cell_scc
#> [1] 0.07055168
#> 
#> $median_cell_scc
#> [1] 0.07361193
#> 
#> $mean_feat_pcc
#> [1] 0.05053426
#> 
#> $median_feat_pcc
#> [1] 0.05450756
#> 
#> $mean_feat_scc
#> [1] 0.05363397
#> 
#> $median_feat_scc
#> [1] 0.05325754
#> 
#> $mean_cell_cosine
#> [1] 0.4599328
#> 
#> $rmse
#> [1] 6.034916
#> 
#> $mae
#> [1] 3.951458
#>