scimap.tl.phenotype_cells

Short description

The phenotyping function takes in the scaled data and a prior knowledge based phenotype workflow file to assign phenotype to each cell in the dataset.

Phenotype workflow file description:
An example of the phenotype_workflow.csv can be found here.

The phenotype_workflow accepts six categories of gating strategy for performing phenotyping.

• allpos
• allneg
• anypos
• anyneg
• pos
• neg

allpos- All of the defined markers should be positive.
allneg- All of the defined markers should be negative.
anypos- Any one of the defined marker is sufficient to be positive. (e.g) For defining macrophages, one could use a strategy in which a cell is defined as a macrophage if any of CD68, CD163 or CD206 is positive.
anyneg- Any of the defined marker is negative.
pos- A given marker is positive. If this argument is passed to multiple markers. (e.g) If regulatory T cell is defined as CD4+, FOXP3+ by passing pos to each the markers and the algorithm finds that for a few cells one of the two is not, the algorithm will assign the cell as likely-regulatory T cell and will allow the user to make the decision later.
neg- A given marker is negative.
It is always advised to use positive markers over negative markers

Parameters

adata : AnnData Object

phenotype : DataFrame
A gating strategy for phenotyping the cells.

gate : int, optional (The default is 0.5)
By default rescale function, scales the data such that values above 0.5 are considered positive cells.

label : string, optional (The default is "phenotype")
Name the column under which the final phenotype calling will be saved.

imageid : string, optional (The default is 'imageid')
Name of the column that contains the unique imageid. This is only utilized when the user uses pheno_threshold_percent or pheno_threshold_abs parameters.

pheno_threshold_percent : float, optional (The default is None)
Accepts values between (0-100). If any particular phenotype is below the user defined threshold, it is re-categorized as unknown. Generally used to deal with low background false positives.

pheno_threshold_abs : int, optional (The default is None)
Serves the same purpose as that of pheno_threshold_percent. However, an absolute number can be passed. For example, if user passes in 10- any phenotype that contains less than 10 cells will be re-categorized as unknown.

Returns

AnnData object with cell phenotypes added as a new column in adata.obs

Example

phenotype = pd.read_csv('path/to/csv/file/')
adata = sm.tl.phenotype_cells (adata, phenotype=phenotype, label="phenotype")