scimap.tl.foldchange

Short description

The sm.tl.foldchange function allows users to compute the foldchange (fc) in cell-type (phenotype) abundance between samples or ROI's.

The reference sample or ROI needs to be passed via the from_group parameter. The column name of from_group should be passed via imageid. The function computes the fc to all other categories within the same imageid column. By default (can be turned off), the cell-abundance will be normalized for the total number of cells within the sample/ROI to account for difference in area. A fisher-exact-test is performed to compute the p-values.

The results are stored in .uns section of the anndata object.

Parameters

adata : AnnData Object

from_group : list, required
Pass in the name of the sample or ROI that will serve as a reference for calculating fold change. If multiple sample names or ROI's are passed in as a list e.g. ['ROI1', 'ROI2''], please note that they will be combined for calculating the fold change.

to_group : list, optional (The default is None)
By default the reference sample/ROI passed via from_group will be compared to all other groups within the same column. However if users wish to restrict the comparision to a subset of samples/ROI's they can be passed though this paramenter as a list. e.g. ['ROI3', 'ROI4'].

imageid : string, optional (The default is imageid)
The column that contains the samples/ROI information.

phenotype : string, optional (The default is phenotype)
The column that contains the cell-type/phenotype information.

normalize : bool, optional (The default is True)
Inorder to account for the sample/ROI area, the cellular abundance is first normalized to the total number of cells within the respective sample/ROI. Please note if you pass values in subset_phenotype, the abundance normalization is restricted to the total cells of the cell types passed in via subset_phenotype.

subset_phenotype : list, optional (The default is None)
If users are interested in only a subset of cell-types, the names of those can be passed in through this parameter. The data is subsetted to include only these cell types before computing foldchange.

label : string, optional (The default is foldchange_fc)
Key for the returned data, stored in adata.uns. The default is 'foldchange'. The foldchange and p-values are returned seperately with the postfix _fc and _pval

Returns Updated AnnData object. Check adata.uns['foldchange_fc'] and adata.uns['foldchange_pval'] for results.

Example

# calculate the foldchange of cell-types between image_1 and all other images in the dataset.

adata = sm.tl.foldchange (adata, from_group='image_1', to_group=None, 
                              imageid='imageid', phenotype='phenotype',
                              normalize=True, 
                              subset_phenotype=['Tcells','Bcells','Macs'], 
                              label='foldchange')