anndata.concat (original) (raw)
anndata.concat#
anndata.concat(adatas, *, axis='obs', join='inner', merge=None, uns_merge=None, label=None, keys=None, index_unique=None, fill_value=None, pairwise=False)[source]#
Concatenates AnnData objects along an axis.
See the concatenation section in the docs for a more in-depth description.
Parameters:
adatas Union[Collection[AnnData], Mapping[str, AnnData]]
The objects to be concatenated. If a Mapping is passed, keys are used for the keysargument and values are concatenated.
axis Literal['obs', 0, 'var', 1] (default: 'obs')
Which axis to concatenate along.
join Literal['inner', 'outer'] (default: 'inner')
How to align values when concatenating. If “outer”, the union of the other axis is taken. If “inner”, the intersection. See concatenationfor more.
merge Union[Literal['same', 'unique', 'first', 'only'], Callable, None] (default: None)
How elements not aligned to the axis being concatenated along are selected. Currently implemented strategies include:
None: No elements are kept."same": Elements that are the same in each of the objects."unique": Elements for which there is only one possible value."first": The first element seen at each from each position."only": Elements that show up in only one of the objects.
uns_merge Union[Literal['same', 'unique', 'first', 'only'], Callable, None] (default: None)
How the elements of .uns are selected. Uses the same set of strategies as the merge argument, except applied recursively.
label str | None (default: None)
Column in axis annotation (i.e. .obs or .var) to place batch information in. If it’s None, no column is added.
keys Collection | None (default: None)
Names for each object being added. These values are used for column values forlabel or appended to the index if index_unique is not None. Defaults to incrementing integer labels.
index_unique str | None (default: None)
Whether to make the index unique by using the keys. If provided, this is the delimiter between “{orig_idx}{index_unique}{key}”. When None, the original indices are kept.
fill_value Any | None (default: None)
When join="outer", this is the value that will be used to fill the introduced indices. By default, sparse arrays are padded with zeros, while dense arrays and DataFrames are padded with missing values.
pairwise bool (default: False)
Whether pairwise elements along the concatenated dimension should be included. This is False by default, since the resulting arrays are often not meaningful.
Return type:
Notes
Warning
If you use join='outer' this fills 0s for sparse data when variables are absent in a batch. Use this with care. Dense data is filled with NaN.
Examples
Preparing example objects
import anndata as ad, pandas as pd, numpy as np from scipy import sparse a = ad.AnnData( ... X=sparse.csr_matrix(np.array([[0, 1], [2, 3]])), ... obs=pd.DataFrame({"group": ["a", "b"]}, index=["s1", "s2"]), ... var=pd.DataFrame(index=["var1", "var2"]), ... varm={ ... "ones": np.ones((2, 5)), ... "rand": np.random.randn(2, 3), ... "zeros": np.zeros((2, 5)), ... }, ... uns={"a": 1, "b": 2, "c": {"c.a": 3, "c.b": 4}}, ... ) b = ad.AnnData( ... X=sparse.csr_matrix(np.array([[4, 5, 6], [7, 8, 9]])), ... obs=pd.DataFrame( ... {"group": ["b", "c"], "measure": [1.2, 4.3]}, index=["s3", "s4"] ... ), ... var=pd.DataFrame(index=["var1", "var2", "var3"]), ... varm={"ones": np.ones((3, 5)), "rand": np.random.randn(3, 5)}, ... uns={"a": 1, "b": 3, "c": {"c.b": 4}}, ... ) c = ad.AnnData( ... X=sparse.csr_matrix(np.array([[10, 11], [12, 13]])), ... obs=pd.DataFrame({"group": ["a", "b"]}, index=["s1", "s2"]), ... var=pd.DataFrame(index=["var3", "var4"]), ... uns={"a": 1, "b": 4, "c": {"c.a": 3, "c.b": 4, "c.c": 5}}, ... )
Concatenating along different axes
ad.concat([a, b]).to_df() var1 var2 s1 0 1 s2 2 3 s3 4 5 s4 7 8 ad.concat([a, c], axis="var").to_df() var1 var2 var3 var4 s1 0 1 10 11 s2 2 3 12 13
Inner and outer joins
inner = ad.concat([a, b]) # Joining on intersection of variables inner AnnData object with n_obs × n_vars = 4 × 2 obs: 'group' (inner.obs_names, inner.var_names) (Index(['s1', 's2', 's3', 's4'], dtype='object'), Index(['var1', 'var2'], dtype='object')) outer = ad.concat([a, b], join="outer") # Joining on union of variables outer AnnData object with n_obs × n_vars = 4 × 3 obs: 'group', 'measure' outer.var_names Index(['var1', 'var2', 'var3'], dtype='object') outer.to_df() # Sparse arrays are padded with zeroes by default var1 var2 var3 s1 0 1 0 s2 2 3 0 s3 4 5 6 s4 7 8 9
Using the axis’ index instead of its name
ad.concat([a, b], axis=0).to_df() # Equivalent to axis="obs" var1 var2 s1 0 1 s2 2 3 s3 4 5 s4 7 8 ad.concat([a, c], axis=1).to_df() # Equivalent to axis="var" var1 var2 var3 var4 s1 0 1 10 11 s2 2 3 12 13
Keeping track of source objects
ad.concat({"a": a, "b": b}, label="batch").obs group batch s1 a a s2 b a s3 b b s4 c b ad.concat([a, b], label="batch", keys=["a", "b"]).obs # Equivalent to previous group batch s1 a a s2 b a s3 b b s4 c b ad.concat({"a": a, "b": b}, index_unique="-").obs group s1-a a s2-a b s3-b b s4-b c
Combining values not aligned to axis of concatenation
ad.concat([a, b], merge="same") AnnData object with n_obs × n_vars = 4 × 2 obs: 'group' varm: 'ones' ad.concat([a, b], merge="unique") AnnData object with n_obs × n_vars = 4 × 2 obs: 'group' varm: 'ones', 'zeros' ad.concat([a, b], merge="first") AnnData object with n_obs × n_vars = 4 × 2 obs: 'group' varm: 'ones', 'rand', 'zeros' ad.concat([a, b], merge="only") AnnData object with n_obs × n_vars = 4 × 2 obs: 'group' varm: 'zeros'
The same merge strategies can be used for elements in .uns
dict(ad.concat([a, b, c], uns_merge="same").uns) {'a': 1, 'c': {'c.b': 4}} dict(ad.concat([a, b, c], uns_merge="unique").uns) {'a': 1, 'c': {'c.a': 3, 'c.b': 4, 'c.c': 5}} dict(ad.concat([a, b, c], uns_merge="only").uns) {'c': {'c.c': 5}} dict(ad.concat([a, b, c], uns_merge="first").uns) {'a': 1, 'b': 2, 'c': {'c.a': 3, 'c.b': 4, 'c.c': 5}}