Cell.Similarity: Calculation of cell similarity scores for the original and...

In JSB-UCLA/scDED: Single-cell Dubious Embedding Detector

Calculation of cell similarity scores for the original and permuted data

Description

This function calculates the similarity scores for all cells on the original and permuted data. The permuted data will be used to estimate the null distribution.

Usage

Cell.Similarity(pre_embedding_distances,pre_embedding_distances_permuted, reduced_dim_distances,reduced_dim_distances_permuted,similarity_percent = .50)

Arguments

pre_embedding_distances	A matrix containing the cell-cell distances calculated in the pre-embedding space for the original data
pre_embedding_distances_permuted	A matrix containing the cell-cell distances calculated in the pre-embedding space for the permuted data
reduced_dim_distances	A matrix containing the cell-cell distances calculated in the post-embedding space for the original data
reduced_dim_distances_permuted	A matrix containing the cell-cell distances calculated in the post-embedding space for the permuted data
similarity_percent	The percentage of cells to consider in the similarity score calculations (default = 0.5). scDEED uses the nearest floor(number of cells * similarity_percent) neighbors in the similarity percent calculations. Intuitively, a higher similarity score considers more cells as neighbors (emphasis on global preservation) while a lower similarity score considers less cells (emphasis on local preservation)

Value

Returns a list with two vectors, (1) 'rho_original': the similarity scores calculated on the original data (2) 'rho_permuted': the similarity scores calculated on the permuted data

JSB-UCLA/scDED documentation built on Feb. 8, 2025, 11:12 a.m.