Add Method SCOT to Multi-modal data Integration Task

docker/openproblems-python-extras/requirements.txt

@@ -5,3 +5,4 @@ phate
 pyensembl
 pybedtools
 git+https://github.com/czbiohub/molecular-cross-validation
+git+https://github.com/atong01/SCOT

openproblems/tasks/multimodal_data_integration/methods/scot.py

@@ -0,0 +1,97 @@
+from ....tools.decorators import method
+from ....tools.normalize import log_cpm
+from ....tools.normalize import log_scran_pooling
+from ....tools.normalize import sqrt_cpm
+from ....tools.utils import check_version
+
+import sklearn.decomposition
+
+
+def _scot(adata, n_svd=100, balanced=False):
+    from SCOT import SCOT
+
+    # PCA reduction
+    n_svd = min([n_svd, min(adata.X.shape) - 1, min(adata.obsm["mode2"].shape) - 1])
+    X_pca = sklearn.decomposition.TruncatedSVD(n_svd).fit_transform(adata.X)
+    Y_pca = sklearn.decomposition.TruncatedSVD(n_svd).fit_transform(adata.obsm["mode2"])
+
+    # Initialize SCOT
+    scot = SCOT(X_pca, Y_pca)
+
+    # call the unbalanced alignment
+    # From https://github.com/rsinghlab/SCOT/blob/master/examples/unbalanced_GW_SNAREseq.ipynb # noqa: 501
+    X_new_unbal, y_new_unbal = scot.align(
+        k=50, e=1e-3, rho=0.0005, normalize=True, balanced=balanced
+    )
+    adata.obsm["aligned"] = X_new_unbal
+    adata.obsm["mode2_aligned"] = y_new_unbal
+
+    return adata
+
+
+@method(
+    method_name="Single Cell Optimal Transport (sqrt CPM unbalanced)",
+    paper_name="Gromov-Wasserstein optimal transport"
+    "to align single-cell multi-omics data",
+    paper_url="https://www.biorxiv.org/content/10.1101/2020.04.28.066787",
+    paper_year=2020,
+    code_url="https://github.com/rsinghlab/SCOT",
+    code_version=check_version("SCOT"),
+    image="openproblems-python-extras",
+)
+def scot_sqrt_cpm_unbalanced(adata, n_svd=100, balanced=False):
+    sqrt_cpm(adata)
+    log_cpm(adata, obsm="mode2", obs="mode2_obs", var="mode2_var")
+    _scot(adata, n_svd=n_svd, balanced=balanced)
+    return adata
+
+
+@method(
+    method_name="Single Cell Optimal Transport (sqrt CPM balanced)",
+    paper_name="Gromov-Wasserstein optimal transport to "
+    "align single-cell multi-omics data",
+    paper_url="https://www.biorxiv.org/content/10.1101/2020.04.28.066787",
+    paper_year=2020,
+    code_url="https://github.com/rsinghlab/SCOT",
+    code_version=check_version("SCOT"),
+    image="openproblems-python-extras",
+)
+def scot_sqrt_cpm_balanced(adata, n_svd=100, balanced=True):
+    sqrt_cpm(adata)
+    log_cpm(adata, obsm="mode2", obs="mode2_obs", var="mode2_var")
+    _scot(adata, n_svd=n_svd, balanced=balanced)
+    return adata
+
+
+@method(
+    method_name="Single Cell Optimal Transport (log scran unbalanced)",
+    paper_name="Gromov-Wasserstein optimal transport to "
+    "align single-cell multi-omics data",
+    paper_url="https://www.biorxiv.org/content/10.1101/2020.04.28.066787",
+    paper_year=2020,
+    code_url="https://github.com/rsinghlab/SCOT",
+    code_version=check_version("SCOT"),
+    image="openproblems-python-extras",
+)
+def scot_log_scran_pooling_unbalanced(adata, n_svd=100, balanced=False):
+    log_scran_pooling(adata)
+    log_cpm(adata, obsm="mode2", obs="mode2_obs", var="mode2_var")
+    _scot(adata, n_svd=n_svd, balanced=balanced)
+    return adata
+
+
+@method(
+    method_name="Single Cell Optimal Transport (log scran balanced)",
+    paper_name="Gromov-Wasserstein optimal transport to "
+    "align single-cell multi-omics data",
+    paper_url="https://www.biorxiv.org/content/10.1101/2020.04.28.066787",
+    paper_year=2020,
+    code_url="https://github.com/rsinghlab/SCOT",
+    code_version=check_version("SCOT"),
+    image="openproblems-python-extras",
+)
+def scot_log_scran_pooling_balanced(adata, n_svd=100, balanced=True):
+    log_scran_pooling(adata)
+    log_cpm(adata, obsm="mode2", obs="mode2_obs", var="mode2_var")
+    _scot(adata, n_svd=n_svd, balanced=balanced)
+    return adata