Sm.hl.classify
Short Description
sm.hl.classify: Helper function that allow users to annotate cells based on positivity/negativity
of defined markers. Users can classify the entire data or a subset of data that
has been previously phenotyped or clustered.
Function
classify(adata, pos=None, neg=None, classify_label='passed_classify', phenotype=None, subclassify_phenotype=None, threshold=0.5, collapse_failed=True, label='classify')
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
adata |
AnnData object |
required | |
pos |
list, optional |
None |
|
neg |
list, optional |
None |
|
classify_label |
string, optional |
'passed_classify' |
|
subclassify_phenotype |
list, optional |
None |
|
threshold |
float, optional |
0.5 |
|
phenotype |
string, required |
None |
|
collapse_failed |
bool, optional |
True |
|
label |
string, optional |
'classify' |
adata : AnnData
Updated AnnData Object.
Examples:
1 2 3 4 5 6 7 8 9 10 11 12 13 | |
Source code in scimap/helpers/_classify.py
def classify (adata, pos=None, neg=None, classify_label='passed_classify',
phenotype=None, subclassify_phenotype=None, threshold = 0.5,
collapse_failed=True, label="classify"):
"""
Parameters:
adata : AnnData object
pos : list, optional
Pass a list of markers that should be expressed in the resultant cells.
neg : list, optional
Pass a list of markers that should not be expressed in the resultant cells.
classify_label : string, optional
Provide a name for the calssified cells.
subclassify_phenotype : list, optional
If only a subset of phenotypes require to classified, pass the name of those phenotypes as a list
through this argument.
threshold: float, optional
Above or below the given value will be considered for positive and negative classification.
If the data was scaled using the `sm.pp.rescale` function, 0.5 is the classification threshold.
phenotype : string, required
Column name of the column containing the phenotype information.
This is important if `subclassify_phenotype` or `collapse_failed` arguments are used.
collapse_failed : bool, optional
If set to true, the cells that were not classified based on the given criteria will be
binned into a single category named 'failed_classify'. When False, the phenotype
inforamation for other cells will be borrowed from the `phenotype` argument.
label : string, optional
Key for the returned data, stored in `adata.obs`.
Returns:
adata : AnnData
Updated AnnData Object.
Example:
```python
# Classify all cells with both pos and neg markers
# (Identify cytotoxic T-cells)
adata = sm.hl.classify(adata, pos=['CD3D','CD8A'], neg=['ASMA'])
# Classify specific sub-types of cells
adata = sm.hl.classify(adata, pos=['CD3D','FOXP3'],
neg=['ASMA'], subclassify_phenotype=['T cells','Regulatory T cells'])
# Classify specific sub-types of cells and borrow labels
# from another column
adata = sm.hl.classify(adata, pos=['CD3D'], neg=['ASMA'],
subclassify_phenotype=['T cells'], collapse_failed=False,
phenotype='phenotype')
```
"""
# clean the input
if isinstance(pos, str):
pos = [pos]
if isinstance(neg, str):
neg = [neg]
if isinstance(subclassify_phenotype, str):
subclassify_phenotype = [subclassify_phenotype]
# Create a dataFrame with the necessary inforamtion
data = pd.DataFrame(adata.X, index= adata.obs.index, columns = adata.var.index)
# if user requests to subset a specific phenotype
if subclassify_phenotype is not None:
meta = pd.DataFrame(adata.obs[phenotype])
subset_index = meta[meta[phenotype].isin(subclassify_phenotype)].index
data = data.loc[subset_index]
# Subset cells that pass the pos criteria
if pos is not None:
for i in pos:
# subset data
data = data[data[i] >= threshold]
# Subset cells that pass the neg criteria
if neg is not None and not data.empty:
for j in neg:
# subset data
data = data[data[j] < threshold]
# cells that passed the classify criteria
if data.empty:
raise TypeError("No cells were found to satisfy your `classify` criteria")
else:
classify_idx = data.index
classified = pd.DataFrame(np.repeat(classify_label, len(classify_idx)), index = classify_idx)
classified.columns = [label]
#classified = pd.DataFrame(np.repeat(classify_label, len(classify_idx)), index = classify_idx, columns = [phenotype])
if collapse_failed is True:
meta = pd.DataFrame(adata.obs.iloc[:, 0])
meta = meta.merge(classified, how='outer', left_index=True, right_index=True)
meta[label] = meta[label].fillna('failed_classify')
meta = meta[label]
else:
if phenotype is None:
raise ValueError("Please pass a column name to the PHENOTYPE argument")
meta = pd.DataFrame(adata.obs[phenotype])
classified = pd.DataFrame(np.repeat(classify_label, len(classify_idx)), index = classify_idx, columns = [phenotype])
meta.update(classified)
# Add to Anndata
meta = meta.reindex(adata.obs.index)
adata.obs[label] = meta
# return
return adata