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Discussion: multiple tables in a SpatialData object #43
Description
This issue is to discuss the possibility of allowing multiple tables per SpatialData object. The implementation may be a list of tables, a dictionary of tables, or a MuData object containing multiple tables. Below are the notes from the Oct 28, 2022 hackathon in Munich. cc @giovp , @LucaMarconato , @ivirshup .
Single-table
Pro
- Intuitive compounds statements for queries on tables
idx = (sdata.tables['expression'].obs.cell_type == "Neuron") & (sdata.obs.n_counts > 100)
sdata = sdata[idx, :]
Cons
- We 100% need a
SpatialDataContainer
sc = SpatialContainer('...')
sdata = sc.to_spatialdata(elements=['/images/...', '...'])
everyting = SpatialData('...')
sdata = everyhing.query.elements(elements=['/images/...', '...'])
subsdata = sdata.query.elements(elements=['/images/...'])
- Points can't be annotated with a table -> For Points (but not for Circles), you can store expression inside the Points.
Multi-table
Pro
- No need for SpatialDataContainer
- Points and Circles are unified, because both can be annotated by a Table
sdata0 = sdata.query.coordinate_system('coordinate_system_name', filter_rows=False)
sdata1 = sdata.query.bounding_box()
sdata1 = sdata.query.polygon('/polygons/annotations')
sdata1 = sdata.query.table(table_key=..., attr: ["obs", "var"], query="""""")
import squidpy as sq
adata = sq.datasets.mibitof()
df = adata.obs
out1 = df.query("(point == 23) & (cell_size > 200)")
out2 = df.loc[(df.point == 23) & (df.cell_size > 200)]
pd.testing.assert_frame_equal(out1, out2)
Cons
- Complex selection of multiple tables
idx = (sdata.table.obs.cell_type == "Neuron") & (sdata.table.obs.n_counts > 100)
sdata = sdata.query.table(key='expression', index=idx)
class SpatialData
@property
def table(self) -> AnnData:
if len(self.tables) == 1:
k = list(self.tables.keys())[0]
return self.tables[k]
else:
raise ValueError("...")
('A > B' & 'cell_type == "Neuron"')
Reacted by Giovanni Palla and LucaMarconato
Activity