Merge pull request #348 from borcuttjahns/main

Adds IL-2 stimulation dataset

tensorly/datasets/imports.py

@@ -0,0 +1,85 @@
+"""
+Load example datasets.
+"""
+
+from os.path import dirname
+import numpy as np
+
+
+class Bunch(dict):
+    """ A Bunch, exposing dict keys as a keys() method.
+    Definition from scikit-learn. """
+
+    def __init__(self, **kwargs):
+        super().__init__(kwargs)
+
+    def __setattr__(self, key, value):
+        self[key] = value
+
+    def __dir__(self):
+        return self.keys()
+
+    def __getattr__(self, key):
+        try:
+            return self[key]
+        except KeyError:
+            raise AttributeError(key)
+
+    def __setstate__(self, state):
+        pass
+
+
+def IL2data():
+    """
+    Loads tensor of IL-2 mutein treatment responses.
+    Tensor contains the signaling responses of eight different cell types to 13 IL-2 mutants.
+    Dose-responses to 12 concentrations of muteins were collected at four time points for each mutant and cell type.
+    Ligands include IL-2 mutants with modified receptor affinities, and in both mono- and bivalent formats.
+    Useful for demonstrating execution and interpretation of non-negative CP decomposition.
+    """
+    path_here = dirname(__file__)
+    tensor = np.load(path_here + "/data/IL2_Response_Tensor.npy")
+    ligands = ['IL2 (Mono)', 'IL15 (Mono)', 'WT N-term (Biv)', 'H16N N-term (Biv)',
+               'R38Q N-term (Biv)', 'R38Q/H16N (Biv)', 'WT N-term (Mono)',
+               'H16N N-term (Mono)', 'WT C-term (Mono)', 'V91K C-term (Mono)',
+               'R38Q N-term (Mono)', 'F42Q N-Term (Mono)', 'N88D C-term (Mono)']
+    cells = ['Treg IL2Ra_hi', 'Treg', 'Treg IL2Ra_lo', 'Thelper IL2Ra_hi', 'Thelper', 'Thelper IL2Ra_lo', 'CD8+', 'NK']
+    times = [4, 2, 1, 0.5]
+    doses = np.geomspace(84.0, 0.000474, 12)
+    dims = ["ligands", "times", "doses", "cells"]
+    reference = ["Orcutt-Jahns, B., Emmel, P. C., Snyder, E. M., Posner, C., \
+                Carlson, S. M., & Meyer, A. S. (2021). Multivalency enhances the \
+                specificity of Fc-cytokine fusions. BioRxiv."]
+    task = ["Non-Negative CP"]
+    DESC = "This dataset includes a tensor of eight cell types' responses to 13 IL-2 muteins, at 4 different time points, and at 12 different IL-2 concentrations."
+    LICENSE = """Copyright 2021 The Regents of the University of California
+
+        Permission is hereby granted, free of charge, to any person obtaining a copy
+        of this software and associated documentation files (the "Software"), to deal
+        in the Software without restriction, including without limitation the rights
+        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+        copies of the Software, and to permit persons to whom the Software is
+        furnished to do so, subject to the following conditions:
+
+        The above copyright notice and this permission notice shall be included in all
+        copies or substantial portions of the Software.
+
+        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+        SOFTWARE."""
+
+    return Bunch(
+        tensor=tensor,
+        ligands=ligands,
+        cells=cells,
+        times=times,
+        doses=doses,
+        dims=dims,
+        reference=reference,
+        task=task,
+        DESC=DESC,
+        LICENSE=LICENSE)

tensorly/datasets/tests/test_imports.py

@@ -0,0 +1,12 @@
+from ..imports import IL2data
+
+
+def test_IL2data():
+    """ Test that data import dimensions match. """
+    data = IL2data()
+
+    tensor = data["tensor"]
+    assert tensor.shape[0] == len(data["ligands"])
+    assert tensor.shape[1] == len(data["times"])
+    assert tensor.shape[2] == len(data["doses"])
+    assert tensor.shape[3] == len(data["cells"])