Interactivity

Spatialproteomics has a variety of functions to ensure interoperability with existing tools. For example, we can export the spatialproteomics object into spatialdata, and the use napari for interactive visualiztations.

%reload_ext autoreload
%autoreload 2

import spatialproteomics as sp
import xarray as xr
from napari_spatialdata import Interactive
import spatialdata
from spatialdata import SpatialData
import numpy as np

# loading in a data set and performing some formatting for convenience
ds = xr.load_dataset('../../data/BNHL_166_4_I2_LK.zarr')
ds["_properties"] = ds["_labels"]
ds = ds.pp.drop_layers("_labels")
ds

/opt/conda/lib/python3.10/site-packages/xarray/backends/plugins.py:159: RuntimeWarning: 'scipy' fails while guessing
  warnings.warn(f"{engine!r} fails while guessing", RuntimeWarning)

<xarray.Dataset>
Dimensions:        (cells: 12560, channels: 56, y: 3000, x: 3000, features: 4,
                    labels: 8, props: 2)
Coordinates:
  * cells          (cells) int64 1 2 3 4 5 6 ... 12556 12557 12558 12559 12560
  * channels       (channels) <U11 'DAPI' 'Helios' 'CD10' ... 'CD79a' 'Ki-67'
  * features       (features) <U10 'centroid-0' 'centroid-1' ... '_original_'
  * labels         (labels) int64 1 2 3 4 5 6 7 8
  * props          (props) <U6 '_color' '_name'
  * x              (x) int64 0 1 2 3 4 5 6 ... 2994 2995 2996 2997 2998 2999
  * y              (y) int64 0 1 2 3 4 5 6 ... 2994 2995 2996 2997 2998 2999
Data variables:
    _arcsinh_mean  (cells, channels) float64 3.111 0.0 1.391 ... 1.324 0.4174
    _arcsinh_sum   (cells, channels) float64 8.346 0.0 6.564 ... 6.625 5.224
    _image         (channels, y, x) uint8 4 4 4 4 5 4 4 3 4 ... 2 2 2 2 2 2 2 2
    _obs           (cells, features) float64 613.3 768.4 4.0 ... 8.0 7.0
    _raw_mean      (cells, channels) float64 56.02 0.0 9.426 ... 8.727 2.148
    _raw_sum       (cells, channels) float64 1.053e+04 0.0 ... 1.885e+03 464.0
    _segmentation  (y, x) int64 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0
    _properties    (labels, props) object 'C3' ... 'B (PAX5)'

xarray.Dataset

• Dimensions:
  • cells: 12560
  • channels: 56
  • y: 3000
  • x: 3000
  • features: 4
  • labels: 8
  • props: 2
• Coordinates: (7)
  • cells
    (cells)
    int64
    1 2 3 4 ... 12557 12558 12559 12560

    array([    1,     2,     3, ..., 12558, 12559, 12560])

  • channels
    (channels)
    <U11
    'DAPI' 'Helios' ... 'CD79a' 'Ki-67'

    array(['DAPI', 'Helios', 'CD10', 'TCF7/TCF1', 'PD-L1', 'BCL-6', 'FOXP3',
           'CD69', 'Perforin', 'CD19', 'LAG3', 'CD21', 'CD62L', 'c-myc', 'CD138',
           'CD15', 'BCL-2', 'CD56', 'IKZF3', 'CD25', 'NOXA', 'Tim3', 'Serpin B9',
           'Podoplanin', 'CD38', 'SPARC', 'ICOS', 'CXCR5', 'CD163', 'FADD', 'p53',
           'Collagen IV', 'CD4', 'CD7', 'Kappa', 'CD20', 'CD34', 'PAX5', 'PD-1',
           'CD45RA', 'CD11b', 'Lambda', 'CD57', 'CD11c', 'CD90', 'HLA DR', 'CD68',
           'CD31', 'CD45', 'CD3', 'Cytokeratin', 'CD45RO', 'CD8', 'Granyzme B',
           'CD79a', 'Ki-67'], dtype='<U11')

  • features
    (features)
    <U10
    'centroid-0' ... '_original_'

    array(['centroid-0', 'centroid-1', '_labels', '_original_'], dtype='<U10')

  • labels
    (labels)
    int64
    1 2 3 4 5 6 7 8

    array([1, 2, 3, 4, 5, 6, 7, 8])

  • props
    (props)
    <U6
    '_color' '_name'

    array(['_color', '_name'], dtype='<U6')

  • x
    (x)
    int64
    0 1 2 3 4 ... 2996 2997 2998 2999

    array([   0,    1,    2, ..., 2997, 2998, 2999])

  • y
    (y)
    int64
    0 1 2 3 4 ... 2996 2997 2998 2999

    array([   0,    1,    2, ..., 2997, 2998, 2999])

• Data variables: (8)
  • _arcsinh_mean
    (cells, channels)
    float64
    3.111 0.0 1.391 ... 1.324 0.4174

    array([[3.1113318 , 0.        , 1.39104035, ..., 0.42827631, 0.528275  ,
            0.4582596 ],
           [2.80498506, 0.        , 1.16832115, ..., 2.02908251, 0.42634428,
            0.52842933],
           [3.38021961, 0.        , 1.73394497, ..., 0.57174597, 0.72733529,
            0.49741472],
           ...,
           [3.19334124, 0.        , 1.35344852, ..., 0.4051073 , 0.92614581,
            0.43286647],
           [3.43748239, 0.        , 1.5590982 , ..., 0.43428781, 1.39939674,
            0.47313597],
           [3.21632852, 0.        , 1.3602909 , ..., 0.39690411, 1.32359853,
            0.41740323]])

  • _arcsinh_sum
    (cells, channels)
    float64
    8.346 0.0 6.564 ... 6.625 5.224

    array([[8.34578789, 0.        , 6.56357539, ..., 5.11202408, 5.33756119,
            5.18405964],
           [8.06400745, 0.        , 6.32936752, ..., 7.27434143, 5.13347749,
            5.36412702],
           [8.54954425, 0.        , 6.87275074, ..., 5.35849345, 5.6319412 ,
            5.20623213],
           ...,
           [8.7446156 , 0.        , 6.83733397, ..., 5.36972904, 6.30846599,
            5.43983577],
           [8.887487  , 0.        , 6.96489236, ..., 5.34139979, 6.78762075,
            5.43286736],
           [8.58999749, 0.        , 6.66746719, ..., 5.17051628, 6.62539413,
            5.22362285]])

  • _image
    (channels, y, x)
    uint8
    4 4 4 4 5 4 4 3 ... 2 2 2 2 2 2 2 2

    array([[[4, 4, 4, ..., 4, 4, 5],
            [5, 5, 5, ..., 4, 4, 4],
            [5, 5, 5, ..., 4, 4, 4],
            ...,
            [5, 5, 5, ..., 4, 4, 4],
            [5, 4, 4, ..., 4, 4, 4],
            [4, 4, 4, ..., 4, 4, 4]],
    
           [[0, 0, 0, ..., 0, 0, 0],
            [0, 0, 0, ..., 0, 0, 0],
            [0, 0, 0, ..., 0, 0, 0],
            ...,
            [0, 0, 0, ..., 0, 0, 0],
            [0, 0, 0, ..., 0, 0, 0],
            [0, 0, 0, ..., 0, 0, 0]],
    
           [[0, 0, 1, ..., 0, 1, 1],
            [0, 0, 1, ..., 1, 0, 0],
            [0, 0, 1, ..., 0, 0, 0],
            ...,
    ...
            ...,
            [2, 2, 1, ..., 1, 2, 1],
            [1, 2, 2, ..., 1, 1, 1],
            [2, 3, 1, ..., 1, 2, 2]],
    
           [[0, 0, 0, ..., 1, 0, 0],
            [1, 1, 0, ..., 1, 0, 1],
            [0, 0, 1, ..., 0, 0, 1],
            ...,
            [1, 0, 0, ..., 1, 0, 0],
            [1, 0, 1, ..., 1, 0, 0],
            [0, 0, 0, ..., 0, 0, 1]],
    
           [[3, 2, 2, ..., 2, 2, 2],
            [2, 2, 2, ..., 2, 2, 2],
            [2, 2, 2, ..., 2, 3, 2],
            ...,
            [2, 1, 2, ..., 3, 2, 3],
            [2, 2, 2, ..., 3, 2, 2],
            [2, 2, 2, ..., 2, 2, 2]]], dtype=uint8)

  • _obs
    (cells, features)
    float64
    613.3 768.4 4.0 ... 8.0 7.0

    array([[6.13329787e+02, 7.68420213e+02, 4.00000000e+00, 3.00000000e+00],
           [7.69098446e+02, 7.07544041e+02, 6.00000000e+00, 5.00000000e+00],
           [7.74528409e+02, 6.44284091e+02, 3.00000000e+00, 2.00000000e+00],
           ...,
           [2.26591085e+03, 2.23199225e+03, 3.00000000e+00, 2.00000000e+00],
           [2.28148069e+03, 2.21842489e+03, 8.00000000e+00, 7.00000000e+00],
           [2.24906944e+03, 2.23656481e+03, 8.00000000e+00, 7.00000000e+00]])

  • _raw_mean
    (cells, channels)
    float64
    56.02 0.0 9.426 ... 8.727 2.148

    array([[56.01595745,  0.        ,  9.42553191, ...,  2.20744681,
             2.76595745,  2.37234043],
           [41.16580311,  0.        ,  7.2642487 , ..., 18.68911917,
             2.19689119,  2.76683938],
           [73.35795455,  0.        , 13.71590909, ...,  3.01704545,
             3.96590909,  2.59090909],
           ...,
           [60.82170543,  0.        ,  9.03100775, ...,  2.08139535,
             5.32170543,  2.23255814],
           [77.69098712,  0.        , 11.36051502, ...,  2.24034335,
             9.51502146,  2.45493562],
           [62.24074074,  0.        ,  9.10185185, ...,  2.03703704,
             8.72685185,  2.14814815]])

  • _raw_sum
    (cells, channels)
    float64
    1.053e+04 0.0 ... 1.885e+03 464.0

    array([[10531.,     0.,  1772., ...,   415.,   520.,   446.],
           [ 7945.,     0.,  1402., ...,  3607.,   424.,   534.],
           [12911.,     0.,  2414., ...,   531.,   698.,   456.],
           ...,
           [15692.,     0.,  2330., ...,   537.,  1373.,   576.],
           [18102.,     0.,  2647., ...,   522.,  2217.,   572.],
           [13444.,     0.,  1966., ...,   440.,  1885.,   464.]])

  • _segmentation
    (y, x)
    int64
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    array([[0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           ...,
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0]])

  • _properties
    (labels, props)
    object
    'C3' ... 'B (PAX5)'

    array([['C3', 'Vascular (CD31+CD34)'],
           ['C7', 'Stroma (CD90)'],
           ['C2', 'Lymphatic (PDPN)'],
           ['C0', 'T (CD3)'],
           ['#FFFF00', 'Dendritic (CD11c)'],
           ['#1CE6FF', 'Macro (CD68)'],
           ['#7B4F4B', 'Granulo (CD15)'],
           ['C1', 'B (PAX5)']], dtype=object)

• Indexes: (7)
  • cells
    PandasIndex

    PandasIndex(Index([    1,     2,     3,     4,     5,     6,     7,     8,     9,    10,
           ...
           12551, 12552, 12553, 12554, 12555, 12556, 12557, 12558, 12559, 12560],
          dtype='int64', name='cells', length=12560))

  • channels
    PandasIndex

    PandasIndex(Index(['DAPI', 'Helios', 'CD10', 'TCF7/TCF1', 'PD-L1', 'BCL-6', 'FOXP3',
           'CD69', 'Perforin', 'CD19', 'LAG3', 'CD21', 'CD62L', 'c-myc', 'CD138',
           'CD15', 'BCL-2', 'CD56', 'IKZF3', 'CD25', 'NOXA', 'Tim3', 'Serpin B9',
           'Podoplanin', 'CD38', 'SPARC', 'ICOS', 'CXCR5', 'CD163', 'FADD', 'p53',
           'Collagen IV', 'CD4', 'CD7', 'Kappa', 'CD20', 'CD34', 'PAX5', 'PD-1',
           'CD45RA', 'CD11b', 'Lambda', 'CD57', 'CD11c', 'CD90', 'HLA DR', 'CD68',
           'CD31', 'CD45', 'CD3', 'Cytokeratin', 'CD45RO', 'CD8', 'Granyzme B',
           'CD79a', 'Ki-67'],
          dtype='object', name='channels'))

  • features
    PandasIndex

    PandasIndex(Index(['centroid-0', 'centroid-1', '_labels', '_original_'], dtype='object', name='features'))

  • labels
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8], dtype='int64', name='labels'))

  • props
    PandasIndex

    PandasIndex(Index(['_color', '_name'], dtype='object', name='props'))

  • x
    PandasIndex

    PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,
           ...
           2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999],
          dtype='int64', name='x', length=3000))

  • y
    PandasIndex

    PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,
           ...
           2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999],
          dtype='int64', name='y', length=3000))

• Attributes: (0)

# turning the spatialproteomics object into a spatialdata object
sd_obj = ds.tl.convert_to_spatialdata(expression_matrix_key='_arcsinh_mean')
sd_obj

INFO     Transposing `data` of type: <class 'dask.array.core.Array'> to ('c', 'y', 'x').
INFO     Transposing `data` of type: <class 'dask.array.core.Array'> to ('y', 'x').

SpatialData object
├── Images
│     └── 'image': DataArray[cyx] (56, 3000, 3000)
├── Labels
│     └── 'segmentation': DataArray[yx] (3000, 3000)
└── Tables
      └── 'table': AnnData (12560, 56)
with coordinate systems:
    ▸ 'global', with elements:
        image (Images), segmentation (Labels)

interactive = Interactive(sd_obj)
interactive.run()