Sm.tl.foldchange
Short Description
sm.tl.foldchange: The 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.
Function
foldchange(adata, from_group, to_group=None, imageid='imageid', phenotype='phenotype', normalize=True, subset_phenotype=None, label='foldchange')
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
adata |
AnnData object |
required | |
from_group |
list, required |
required | |
to_group |
list, optional |
None |
|
imageid |
string, optional |
'imageid' |
|
phenotype |
string, optional |
'phenotype' |
|
normalize |
bool, optional |
True |
|
subset_phenotype |
list, optional |
None |
|
label |
string, optional |
'foldchange' |
Returns:
| Type | Description |
|---|---|
adata |
Updated anndata object
Check |
Examples:
1 2 3 4 5 | |
Source code in scimap/tools/_foldchange.py
def foldchange (adata, from_group, to_group=None, imageid='imageid', phenotype='phenotype',
normalize=True, subset_phenotype=None, label='foldchange'):
"""
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
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 column that contains the samples/ROI information.
phenotype : string, optional
The column that contains the cell-type/phenotype information.
normalize : bool, optional
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
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
Key for the returned data, stored in `adata.uns`. The foldchange and p-values
are returned seperately with the postfix `_fc` and `_pval`.
Returns:
adata : Updated anndata object
Check `adata.uns['foldchange_fc']` and `adata.uns['foldchange_pval']` for results.
Example:
```python
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')
```
"""
# prepare data
data = adata.obs[[imageid,phenotype]]
# convert from and to groups to list
if isinstance(from_group, str):
from_group = [from_group]
if isinstance(to_group, str):
to_group = [to_group]
# subset phenotype of interest
if subset_phenotype is not None:
if isinstance (subset_phenotype, str):
subset_phenotype = [subset_phenotype]
data = data[data[phenotype].isin(subset_phenotype)]
# subset data
from_data = data[data[imageid].isin(from_group)]
if len(from_group) > 1:
combined_name = '_'.join(from_group)
from_data[imageid] = combined_name
from_data[imageid] = from_data[imageid].astype('str').astype('category')
from_data[phenotype] = from_data[phenotype].astype('str').astype('category')
if to_group is None:
to_data = data[~data[imageid].isin(from_group)]
else:
to_data = data[data[imageid].isin(to_group)]
to_data[imageid] = to_data[imageid].astype('str').astype('category')
to_data[phenotype] = to_data[phenotype].astype('str').astype('category')
# consolidated counts dataframe
from_data_consolidated = pd.DataFrame(from_data.groupby([imageid,phenotype]).size()).unstack().fillna(0)
from_data_consolidated.columns = np.unique(from_data_consolidated.columns.get_level_values(1))
to_data_consolidated = pd.DataFrame(to_data.groupby([imageid,phenotype]).size()).unstack().fillna(0)
to_data_consolidated.columns = np.unique(to_data_consolidated.columns.get_level_values(1))
# make backup of the sample names
from_b = list(from_data_consolidated.index)
to_b = list(to_data_consolidated.index)
# make sure from_data_consolidated and to_data_consolidated has the same columns
x = from_data_consolidated.T
x.columns = x.columns.astype(str)
y = to_data_consolidated.T
y.columns = y.columns.astype(str)
consolidated = x.merge(y, how='outer', left_index=True, right_index=True).fillna(0)
# split it back into from and to
from_data_consolidated = consolidated[from_b].T
to_data_consolidated = consolidated[to_b].T
# create the total minus to and from tables
from_data_total = abs(from_data_consolidated.sub( from_data_consolidated.sum(axis=1), axis=0))
to_data_total = abs(to_data_consolidated.sub( to_data_consolidated.sum(axis=1), axis=0))
#
print('calculating P values')
p_vals = []
for i in from_data_consolidated.columns:
a = from_data_consolidated[i][0]
c = from_data_total[i][0]
for j in to_data_consolidated.index:
b = to_data_consolidated[i][j]
d = to_data_total[i][j]
oddsratio, pvalue = stats.fisher_exact([[a, b], [c, d]])
p_vals.append(pvalue)
# replace 0 with a small number (1 cell) to avoind inf
from_data_consolidated_zero = from_data_consolidated.replace(0, 1, inplace=False)
to_data_consolidated_zero = to_data_consolidated.replace(0, 1, inplace=False)
# normalize based on area i.e total cells if user requests
if normalize is True:
# Normalize for total cells
from_data_ratio = from_data_consolidated_zero.div(from_data_consolidated_zero.sum(axis=1), axis=0)
to_data_ratio = to_data_consolidated_zero.div(to_data_consolidated_zero.sum(axis=1), axis=0)
else:
from_data_ratio = from_data_consolidated_zero
to_data_ratio = to_data_consolidated_zero
# foldchange
fold_change = to_data_ratio.div(from_data_ratio.values, axis=1)
fold_change.index.name = '-'.join(from_group)
# reshape the pvalues to the todata df
p_values = np.reshape(p_vals, to_data_consolidated.shape)
p_values = pd.DataFrame(p_values, columns = to_data_consolidated.columns, index= to_data_consolidated.index)
# return data
adata.uns[str(label)+'_pval'] = p_values
adata.uns[str(label)+'_fc'] = fold_change
return adata