moscot.problems.spatiotemporal.SpatioTemporalProblem.prepare¶
- SpatioTemporalProblem.prepare(time_key, spatial_key='spatial', joint_attr=None, normalize_spatial=True, policy='sequential', cost='sq_euclidean', cost_kwargs=mappingproxy({}), a=None, b=None, marginal_kwargs=mappingproxy({}), subset=None, xy_callback=None, x_callback=None, y_callback=None, xy_callback_kwargs=mappingproxy({}), x_callback_kwargs=mappingproxy({}), y_callback_kwargs=mappingproxy({}))[source]¶
Prepare the spatiotemporal problem problem.
See also
See Spatiotemporal trajectory of mouse organogenesis on how to prepare and solve the
SpatioTemporalProblem
.See Creating marginals from proliferation and apoptosis markers in developmental processes on how to
score genes for proliferation and apoptosis
.
- Parameters:
time_key (
str
) – Key inobs
where the time points are stored.spatial_key (
str
) – Key inobsm
where the spatial coordinates are stored.joint_attr (
Union
[str
,Mapping
[str
,Any
],None
]) –How to get the data for the linear term in the fused case:
dict
- it should contain'attr'
and'key'
, the attribute and key inAnnData
, and optionally'tag'
from thetags
.
By default,
tag = 'point_cloud'
is used.normalize_spatial (
bool
) – Whether to normalize the spatial coordinates. IfTrue
, the coordinates are normalized by standardizing them.policy (
Literal
['sequential'
,'triu'
,'tril'
,'explicit'
]) –Rule which defines how to construct the subproblems using
obs['{time_key}']
. Valid options are:'sequential'
- align subsequent time points[(t0, t1), (t1, t2), ...]
.'triu'
- upper triangular matrix[(t0, t1), (t0, t2), ..., (t1, t2), ...]
.'tril'
- lower triangular matrix[(t_n, t_n-1), (t_n, t0), ..., (t_n-1, t_n-2), ...]
.'explicit'
- explicit sequence of subsets passed viasubset = [(b3, b0), ...]
.
cost (
Union
[Literal
['euclidean'
,'sq_euclidean'
,'cosine'
,'pnorm_p'
,'sq_pnorm'
,'geodesic'
],Mapping
[Literal
['xy'
,'x'
,'y'
],Literal
['euclidean'
,'sq_euclidean'
,'cosine'
,'pnorm_p'
,'sq_pnorm'
,'geodesic'
]]]) –Cost function to use. Valid options are:
str
- name of the cost function for all terms, seeget_available_costs()
.dict
- a dictionary with the following keys and values:'xy'
- cost function for the linear term.'x'
- cost function for the source quadratic term.'y'
- cost function for the target quadratic term.
cost_kwargs (
Union
[Mapping
[str
,Any
],Mapping
[Literal
['x'
,'y'
,'xy'
],Mapping
[str
,Any
]]]) – Keyword arguments for theBaseCost
or any backend-specific cost.Source marginals. Valid options are:
bool
- ifTrue
,estimate the marginals
, otherwise use uniform marginals.None
- set toTrue
ifproliferation_key
orapoptosis_key
is notNone
.
Target marginals. Valid options are:
bool
- ifTrue
,estimate the marginals
, otherwise use uniform marginals.None
- set toTrue
ifproliferation_key
orapoptosis_key
is notNone
.
marginal_kwargs (
Mapping
[str
,Any
]) – Keyword arguments forestimate_marginals()
. It always containsproliferation_key
andapoptosis_key
, seescore_genes_for_marginals()
for more information.subset (
Optional
[Sequence
[Tuple
[Union
[int
,float
],Union
[int
,float
]]]]) – Subset ofobs['{key}']
for theExplicitPolicy
. Only used whenpolicy = 'explicit'
.xy_callback (
Union
[Literal
['local-pca'
],Callable
[[Literal
['xy'
,'x'
,'y'
],AnnData
,Optional
[AnnData
]],Optional
[TaggedArray
]],None
]) – Callback function used to prepare the data in the linear term.x_callback (
Union
[Literal
['local-pca'
],Callable
[[Literal
['xy'
,'x'
,'y'
],AnnData
,Optional
[AnnData
]],Optional
[TaggedArray
]],None
]) – Callback function used to prepare the data in the source quadratic term.y_callback (
Union
[Literal
['local-pca'
],Callable
[[Literal
['xy'
,'x'
,'y'
],AnnData
,Optional
[AnnData
]],Optional
[TaggedArray
]],None
]) – Callback function used to prepare the data in the target quadratic term.xy_callback_kwargs (
Mapping
[str
,Any
]) – Keyword arguments for thexy_callback
.x_callback_kwargs (
Mapping
[str
,Any
]) – Keyword arguments for thex_callback
.y_callback_kwargs (
Mapping
[str
,Any
]) – Keyword arguments for they_callback
.
- Return type:
- Returns:
: Returns self and updates the following fields:
problems
- the prepared subproblems.spatial_key
- key inobsm
where spatial coordinates are stored.temporal_key
- key inobs
where time points are stored.stage
- set to'prepared'
.problem_kind
- set to'quadratic'
.