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Preprocessing benchmarking #337

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merged 11 commits into from
Sep 21, 2023
Merged

Preprocessing benchmarking #337

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702da0c
add imputation metadata
xingzhongyu Sep 9, 2023
24b8af0
add 3 imputation datasets
xingzhongyu Sep 9, 2023
3bf9575
Update gene_holdout.py
xingzhongyu Sep 9, 2023
0e05872
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 9, 2023
7482a04
remove enumerate
RemyLau Sep 9, 2023
2428e9e
Update multimodality.py
xingzhongyu Sep 19, 2023
e5c6377
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 19, 2023
4a7389e
Update base.py
xingzhongyu Sep 19, 2023
1a17cc1
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 19, 2023
9941fb5
Update scmogcn_graph.py
xingzhongyu Sep 19, 2023
e839fe8
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 19, 2023
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1 change: 1 addition & 0 deletions dance/data/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -456,6 +456,7 @@ def get_feature(self, *, split_name: Optional[str] = None, return_type: FeatType
if split_name is not None:
if channel_type in ["X", "raw_X", "obs", "obsm", "obsp", "layers"]:
idx = self.get_split_idx(split_name, error_on_miss=True)
idx = list(filter(lambda a: a < feature.shape[0], idx))
feature = feature[idx][:, idx] if channel_type == "obsp" else feature[idx]
else:
logger.warning(f"Indexing option for {channel_type!r} not implemented yet.")
Expand Down
97 changes: 92 additions & 5 deletions dance/datasets/multimodality.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,8 +62,57 @@ def data_paths(self) -> List[str]:
osp.join(self.root, self.subtask, f"{self.subtask}.censor_dataset.output_train_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.censor_dataset.output_train_mod2.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.censor_dataset.output_test_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.censor_dataset.output_test_mod2.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.censor_dataset.output_test_mod2.h5ad")
]
if self.subtask == "10k_pbmc":
paths = [
osp.join(self.root, self.subtask, f"{self.subtask}.10kanti_dataset_subset.output_train_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.10kanti_dataset_subset.output_train_mod2.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.10kanti_dataset_subset.output_test_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.10kanti_dataset_subset.output_test_mod2.h5ad")
]
if self.subtask == "pbmc_cite":
paths = [
osp.join(self.root, self.subtask, f"{self.subtask}.citeanti_dataset.output_train_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.citeanti_dataset.output_train_mod2.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.citeanti_dataset.output_test_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.citeanti_dataset.output_test_mod2.h5ad")
]
if self.subtask.startswith("5k_pbmc"):
paths = [
osp.join(self.root, self.subtask, f"{self.subtask}.5kanti_dataset.output_train_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.5kanti_dataset.output_train_mod2.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.5kanti_dataset.output_test_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.5kanti_dataset.output_test_mod2.h5ad")
]
if self.subtask.startswith("openproblems_2022"):
paths = [
osp.join(self.root, self.subtask, f"{self.subtask}.open_dataset.output_train_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.open_dataset.output_train_mod2.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.open_dataset.output_test_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.open_dataset.output_test_mod2.h5ad")
]
if self.subtask.startswith("GSE127064"):
paths = [
osp.join(self.root, self.subtask, f"{self.subtask}.GSE126074_dataset.output_train_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.GSE126074_dataset.output_train_mod2.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.GSE126074_dataset.output_test_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.GSE126074_dataset.output_test_mod2.h5ad")
]
if self.subtask.startswith("GSE117089"):
paths = [
osp.join(self.root, self.subtask, f"{self.subtask}.GSE117089_dataset.output_train_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.GSE117089_dataset.output_train_mod2.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.GSE117089_dataset.output_test_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.GSE117089_dataset.output_test_mod2.h5ad")
]
if self.subtask.startswith("GSE140203"):
paths = [
osp.join(self.root, self.subtask, f"{self.subtask}.GSE140203_dataset.output_train_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.GSE140203_dataset.output_train_mod2.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.GSE140203_dataset.output_test_mod1.h5ad"),
osp.join(self.root, self.subtask, f"{self.subtask}.GSE140203_dataset.output_test_mod2.h5ad")
]
elif self.TASK == "match_modality":
paths = [
osp.join(self.root, self.subtask, f"{self.subtask}.censor_dataset.output_train_mod1.h5ad"),
Expand Down Expand Up @@ -100,6 +149,42 @@ class ModalityPredictionDataset(MultiModalityDataset):
"https://www.dropbox.com/s/cz60vp7bwapz0kw/openproblems_bmmc_multiome_phase2_rna.zip?dl=1",
"openproblems_bmmc_cite_phase2_rna_subset":
"https://www.dropbox.com/s/veytldxkgzyoa8j/openproblems_bmmc_cite_phase2_rna_subset.zip?dl=1",
"5k_pbmc":
"https://www.dropbox.com/scl/fi/uoyis946glh0oo7g833qj/5k_pbmc.zip?rlkey=mw9cvqq7e12iowfbr9rp7av5u&dl=1",
"5k_pbmc_subset":
"https://www.dropbox.com/scl/fi/pykqc9zyt1fjypnjf4m1l/5k_pbmc_subset.zip?rlkey=brkmnqhfz5yl9axiuu0f8gmxy&dl=1",
"10k_pbmc":
"https://www.dropbox.com/scl/fi/npz3n36d3w089creppph2/10k_pbmc.zip?rlkey=6yyv61omv2rw7sqqmfp6u7m1s&dl=1",
"pbmc_cite":
"https://www.dropbox.com/scl/fi/8yvel9lu2f4pbemjeihzq/pbmc_cite.zip?rlkey=5f5jpjy1fcg14hwzot0hot7xd&dl=1",
"openproblems_2022_multi_atac2gex":
"https://www.dropbox.com/scl/fi/4ynxepu306g3k6vqpi3aw/openproblems_2022_multi_atac2gex.zip?rlkey=2mq5vjnsh26gg5zgq9d85ikcp&dl=1",
"openproblems_2022_cite_gex2adt":
"https://www.dropbox.com/scl/fi/dalt3qxwe440107ihjbpy/openproblems_2022_cite_gex2adt.zip?rlkey=ps1fvcr622vhibc1wc1umfdaw&dl=1",
"GSE127064_AdBrain_gex2atac":
"https://www.dropbox.com/scl/fi/4ybsx6pgiuy6j9m0y92ly/GSE127064_AdBrain_gex2atac.zip?rlkey=6a5u7p7xr2dqsoduflzxjluja&dl=1",
"GSE127064_p0Brain_gex2atac":
"https://www.dropbox.com/scl/fi/k4p3nkkqq56ev6ljyo5se/GSE127064_p0Brain_gex2atac.zip?rlkey=y7kayqmk2l72jjogzlvfxtl74&dl=1",
"GSE117089_mouse_gex2atac":
"https://www.dropbox.com/scl/fi/egktuwiognr06xebeuouk/GSE117089_mouse_gex2atac.zip?rlkey=jadp3hlopc3112lmxe6nz5cd1&dl=1",
"GSE117089_A549_gex2atac":
"https://www.dropbox.com/scl/fi/b7evc2n5ih5o3xxwcd7uq/GSE117089_A549_gex2atac.zip?rlkey=b5o0ykptfodim59qwnu2m89fh&dl=1",
"GSE117089_sciCAR_gex2atac":
"https://www.dropbox.com/scl/fi/juibpvmtv2otvfsq1xyr7/GSE117089_sciCAR_gex2atac.zip?rlkey=qcdbfqsuhab56bc553cwm78gc&dl=1",
"GSE140203_3T3_HG19_atac2gex":
"https://www.dropbox.com/scl/fi/v1vbypz87t1rz012vojkh/GSE140203_3T3_HG19_atac2gex.zip?rlkey=xmxrwso5e5ty3w53ctbm5bo9z&dl=1",
"GSE140203_3T3_MM10_atac2gex":
"https://www.dropbox.com/scl/fi/po9k064twny51subze6df/GSE140203_3T3_MM10_atac2gex.zip?rlkey=q0b4y58bsvacnjrmvsclk4jqu&dl=1",
"GSE140203_12878.rep2_atac2gex":
"https://www.dropbox.com/scl/fi/jqijimb7h6cv4w4hkax1q/GSE140203_12878.rep2_atac2gex.zip?rlkey=c837xkoacap4wjszffpfrmuak&dl=1",
"GSE140203_12878.rep3_atac2gex":
"https://www.dropbox.com/scl/fi/wlv9dhvylz78kq8ezncmd/GSE140203_12878.rep3_atac2gex.zip?rlkey=5r607plnqzlxdgxtc4le8d6o1&dl=1",
"GSE140203_K562_HG19_atac2gex":
"https://www.dropbox.com/scl/fi/n2he1br3u604p3mgniowz/GSE140203_K562_HG19_atac2gex.zip?rlkey=2lhe7s5run8ly5uk4b0vfemyj&dl=1",
"GSE140203_K562_MM10_atac2gex":
"https://www.dropbox.com/scl/fi/dhdorqy87915uah3xl07a/GSE140203_K562_MM10_atac2gex.zip?rlkey=ecwsy5sp7f2i2gtjo1qyaf4zt&dl=1",
"GSE140203_LUNG_atac2gex":
"https://www.dropbox.com/scl/fi/gabugiw244ky85j3ckq4d/GSE140203_LUNG_atac2gex.zip?rlkey=uj0we276s6ay2acpioj4tmfj3&dl=1"
}
SUBTASK_NAME_MAP = {
"adt2gex": "openproblems_bmmc_cite_phase2_mod2",
Expand All @@ -110,9 +195,10 @@ class ModalityPredictionDataset(MultiModalityDataset):
}
AVAILABLE_DATA = sorted(list(URL_DICT) + list(SUBTASK_NAME_MAP))

def __init__(self, subtask, root="./data", preprocess=None):
def __init__(self, subtask, root="./data", preprocess=None, span=0.3):
# TODO: factor our preprocess
self.preprocess = preprocess
self.span = span
super().__init__(subtask, root)

def _raw_to_dance(self, raw_data):
Expand All @@ -135,7 +221,7 @@ def _maybe_preprocess(self, raw_data, selection_threshold=10000):
if self.preprocess == "feature_selection":
if raw_data[0].shape[1] > selection_threshold:
sc.pp.highly_variable_genes(raw_data[0], layer="counts", flavor="seurat_v3",
n_top_genes=selection_threshold)
n_top_genes=selection_threshold, span=self.span)
raw_data[2].var["highly_variable"] = raw_data[0].var["highly_variable"]
for i in [0, 2]:
raw_data[i] = raw_data[i][:, raw_data[i].var["highly_variable"]]
Expand Down Expand Up @@ -169,10 +255,11 @@ class ModalityMatchingDataset(MultiModalityDataset):
}
AVAILABLE_DATA = sorted(list(URL_DICT) + list(SUBTASK_NAME_MAP))

def __init__(self, subtask, root="./data", preprocess=None, pkl_path=None):
def __init__(self, subtask, root="./data", preprocess=None, pkl_path=None, span=0.3):
# TODO: factor our preprocess
self.preprocess = preprocess
self.pkl_path = pkl_path
self.span = span
super().__init__(subtask, root)

def _raw_to_dance(self, raw_data):
Expand Down Expand Up @@ -252,7 +339,7 @@ def _maybe_preprocess(self, raw_data, selection_threshold=10000):
for i in range(2):
if modalities[i].shape[1] > selection_threshold:
sc.pp.highly_variable_genes(modalities[i], layer="counts", flavor="seurat_v3",
n_top_genes=selection_threshold)
n_top_genes=selection_threshold, span=self.span)
modalities[i + 2].var["highly_variable"] = modalities[i].var["highly_variable"]
modalities[i] = modalities[i][:, modalities[i].var["highly_variable"]]
modalities[i + 2] = modalities[i + 2][:, modalities[i + 2].var["highly_variable"]]
Expand Down
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