Skip to content

rename

Short Description

sm.hl.rename: The function allows users to rename any string within a column to another and saved in a new column.

Function

rename(adata, rename, from_column='phenotype', to_column='phenotype_renamed')

Parameters:

Name Type Description Default
adata

AnnData object

 required
rename dict

Pass a dictionary with 'values' as elements that need to be altered and 'keys' as the elements that they need to be transformed into.

 required
from_column string

Column that need to be modified.

 'phenotype'
to_column string

Modified names will be stored in a new column with this name.

 'phenotype_renamed'

Returns:

Name Type Description
adata

Modified AnnData Object 

1
2
3
4
5
6
7
    rename= {'tumor': ['cd45 neg tumor', 'cd8 tumor', 'cd4 tumor'],
             'macrophages': ['m1 macrophages', 'm2 macrophages']}
    Here we are renaming cd45 neg tumor, cd8 tumor and cd4 tumor into 'tumor' and 
    m1 macrophages and m2 macrophages into macrophages

    adata = sm.hl.rename_clusters (adata, rename, from_column='phenotype', 
                                    to_column='phenotype_renamed')
Source code in scimap/helpers/_rename.py 
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
def rename (adata, rename, from_column='phenotype', to_column='phenotype_renamed'):
    """
Parameters:

    adata : AnnData object

    rename (dict):  
        Pass a dictionary with 'values' as elements that need to be altered and 
        'keys' as the elements that they need to be transformed into.

    from_column (string):  
        Column that need to be modified.

    to_column (string):  
        Modified names will be stored in a new column with this name.

Returns:
    adata : Modified AnnData Object  

Example:
```python

    rename= {'tumor': ['cd45 neg tumor', 'cd8 tumor', 'cd4 tumor'],
             'macrophages': ['m1 macrophages', 'm2 macrophages']}
    Here we are renaming cd45 neg tumor, cd8 tumor and cd4 tumor into 'tumor' and 
    m1 macrophages and m2 macrophages into macrophages

    adata = sm.hl.rename_clusters (adata, rename, from_column='phenotype', 
                                    to_column='phenotype_renamed')
```
    """

    # Sanity check: if the values are not list convert them into list
    for i in rename:
        if isinstance(rename[i], str):
            rename[i] = [rename[i]]

    # Get the from_column
    rename_from = list(adata.obs[from_column].values)

    # Split multiple renaming events into independent events
    name = functools.reduce( lambda x,y: dict(x, **y), (dict(map(lambda x: (x,i), rename[i])) for i in rename))

    # Rename
    for i in name:
        print ('Renaming ' + str(i) + ' to ' + str(name[i]))
        #rename_from = [x.replace(i, name[i]) for x in rename_from]
        s = str(i)
        s = s.replace('+', '\+')
        #rename_from = [re.sub(r'^\b%s\b$' % s,  name[i], j) for j in rename_from]
        rename_from = [re.sub(r'^\b%s$' % s,  name[i], j) for j in rename_from]


    # Append to adata as a new column
    adata.obs[to_column] = rename_from

    # Return
    return adata