stereo.core.ms_data.MSData.to_isolated#

MSData.to_isolated(scope, res_key, to, type='obs', item=None, fill=nan)[source]#

Copy a result from mss group specfied by scope to some single-samples specfied by to.

Parameters:

  • scope (slice) – Which integrate mss group to get result.

  • res_key (str) – the key to get result from mms group.

  • to (slice) – which single-samples are the result copy to.

  • type (Literal['obs', 'var']) – obs or var level, defaults to ‘obs’

  • item (Optional[list]) – New column name in obs or var of single-sample, defaults to None

  • fill – Default value when the single-sample has no conrresponding item, defaults to np.NaN

Note

The length of scope must be equal to to.

Only supports clustering result when type is ‘obs’ and hvg result when type is ‘var’.

Examples

Constructing MSData from 5 single-samples.

>>> import stereo as st
>>> data1 = st.io.read_h5ad('../data/10.h5ad')
>>> data2 = st.io.read_h5ad('../data/11.h5ad')
>>> data3 = st.io.read_h5ad('../data/12.h5ad')
>>> data4 = st.io.read_h5ad('../data/13.h5ad')
>>> data5 = st.io.read_h5ad('../data/14.h5ad')
>>> ms_data = data1 + data2 + data3 + data4 + data5
>>> ms_data
ms_data: {'0': (493, 30254), '1': (285, 30254), '2': (753, 30254), '3': (731, 30254), '4': (412, 30254)}
num_slice: 5
names: ['0', '1', '2', '3', '4']
obs: []
var: []
relationship: other
var_type: intersect to 0
mss: []

Integrating all samples to a merged one.

>>> ms_data.integrate()

… did a clustering, the key of result is ‘leiden’ …

Copy the ‘leiden’ result to first three samples, to name as ‘leiden’.

>>> from stereo.core.ms_pipeline import slice_generator
>>> ms_data.to_isolated(scope=slice_generator[0:3], res_key='leiden', to=slice_generator[0:3], type='obs', item=['leiden'] * 3)

Copy the ‘leiden’ result to all samples, to name as ‘leiden’.

>>> from stereo.core.ms_pipeline import slice_generator
>>> ms_data.to_isolated(scope=slice_generator[:], res_key='leiden', to=slice_generator[:], type='obs', item=['leiden'] * 3)