Description Usage Arguments Details Value References Examples
isi
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sce 

group 
Character. Name of group/batch variable.
Needs to be one of 
k 
Numeric. Number of knearest neighbours (knn) to use. 
dim_red 
Character. Name of embeddings to use as subspace for distance distributions. Default is "PCA". 
assay_name 
Character. Name of the assay to use for PCA. Only relevant if no existing 'dim_red' is provided. 
n_dim 
Numeric. Number of dimensions to include to define the subspace. 
weight 
Boolean. If TRUE, batch probabilities to calculate the isi score are weighted by the mean distance of their cells towards the cell of interest. Relevant for metrics: 'isi'. 
res_name 
Character. Appendix of the result score's name (e.g. method used to combine batches). 
The isi function calculates the inverse Simpson index of the group
variable within each cell's knearest neighbourhood.
The Simpson index describes the probability that two entities are taken at
random from the dataset and its inverse represent the effective number of
batches in a neighbourhood. The inverse Simpson index has been proposed as a
diversity score for batch mixing in single cell RNAseq by Korunsky et al.
They provide a distancebased neighbourhood weightening in their Lisi package.
Here, we provide a simplified way of weightening probabilitities, if the
weight
argument is enabled.
A SingleCellExperiment
with the entropy score within colData.
Korsunsky I Fan J Slowikowski K Zhang F Wei K et. al. (2018). Fast, sensitive, and accurate integration of single cell data with Harmony. bioRxiv (preprint)
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