Description Usage Arguments Value Examples
Calculate the distance between a subset of cells in a high-dimensional embedding
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embed_mat_x |
A matrix or data.frame containing high-dimensional embeddings for each cell (e.g. PCs) in condition x. Assumes cells are in rows and embedding dimensions are in columns. |
embed_mat_y |
A matrix or data.frame containing high-dimensional embeddings for each cell (e.g. PCs) in condition y. Assumes cells are in rows and embedding dimensions are in columns. |
dims_use |
Dimensions of the high-dimensional embeddings to use for calculating the distance. Defaults to 10 dimensions. |
num_cells_sample |
Number of cells to subset from the overall embedding matrices. Defaults to 100 cells. |
distance_metric |
Distance metric to calculate distances between cells. Defaults to "bhatt_dist", that is the Bhattacharyya, which is currently the only high dimensional distance metric implemented. |
random_sample |
Whether to sample cells from each condition or to sample cells irrespective of condition to calculate a background distribution. Defaults to random_sample=FALSE to calculate a background distribution. |
A numeric distance value.
1 2 3 4 5 6 7 8 9 10 | # Generate two matrices of 1000 cells each with different distributions
set.seed("0222")
mat1 <- matrix(data=rnorm(100000,mean=1,sd=1),nrow=2000,ncol=50)
mat2 <- matrix(data=rnorm(100000,mean=2,sd=1),nrow=2000,ncol=50)
mat3 <- matrix(data=rnorm(100000,mean=3,sd=1),nrow=2000,ncol=50)
dim_dist(embed_mat_x=mat1,embed_mat_y=mat2,dims_use=1:10,num_cells_sample=100,random_sample=FALSE)
dim_dist(embed_mat_x=mat1,embed_mat_y=mat2,dims_use=1:10,num_cells_sample=100,random_sample=TRUE)
dim_dist(embed_mat_x=mat1,embed_mat_y=mat3,dims_use=1:10,num_cells_sample=100,random_sample=FALSE)
dim_dist(embed_mat_x=mat1,embed_mat_y=mat3,dims_use=1:10,num_cells_sample=100,random_sample=TRUE)
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