<jupyter_start><jupyter_text>Mouse: Quake_10x_Mammary_Gland (new selected genes, no batch effect) NPQuake_Smart-seq2_Mammary_Gland (new selected genes, "donor" batch effect) NPBach (new selected genes, no batch effect) four lifestage: L, G, PI, NPGiraddi_10x (not ok! pseudotime,)Sun (not ok! small cell number, abandoned)two lifestage: pregnant, virginMouse_aligned: not ok! （Maybe we can delete B cell and T cell?）Quake_10x_Mammary_Gland, Quake_Smart-seq2_Mammary_Gland, Bach, SunHuman:Nguyen_10x (new selected genes, "donor" batch effect)Nguyen_C1 (not ok! small cell number, abandoned)Human_aligned: not ok! --- Mouse Quake_10x_Mammary_Gland<jupyter_code>quake_10x_mammary_gland = cb.data.ExprDataSet.read_dataset("../../Datasets/data/Quake_10x_Mammary_Gland/data.h5")
utils.peek(quake_10x_mammary_gland, "build/mammary_gland/Quake_10x_Mammary_Gland")
quake_10x_mammary_gland.obs.head()
quake_10x_mammary_gland.obs["cluster"] = "cluster_" + quake_10x_mammary_gland.obs["cluster"].astype(int).astype(str)
quake_10x_mammary_gland.obs.dtypes
quake_10x_mammary_gland_model = cb.directi.fit_DIRECTi(
    quake_10x_mammary_gland, quake_10x_mammary_gland.uns["seurat_genes"],
    **fixed_model_kwargs
)
quake_10x_mammary_gland.latent = quake_10x_mammary_gland_model.inference(quake_10x_mammary_gland)
ax = quake_10x_mammary_gland.visualize_latent("cell_ontology_class", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Quake_10x_Mammary_Gland/cell_ontology_class.svg", dpi=utils.DPI, bbox_inches="tight")
ax = quake_10x_mammary_gland.visualize_latent("cell_type1", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Quake_10x_Mammary_Gland/cell_type1.svg", dpi=utils.DPI, bbox_inches="tight")
ax = quake_10x_mammary_gland.visualize_latent("cluster", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Quake_10x_Mammary_Gland/cluster.svg", dpi=utils.DPI, bbox_inches="tight")
ax = quake_10x_mammary_gland.visualize_latent("free_annotation", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Quake_10x_Mammary_Gland/free_annotation.svg", dpi=utils.DPI, bbox_inches="tight")
ax = quake_10x_mammary_gland.visualize_latent("donor", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Quake_10x_Mammary_Gland/donor.svg", dpi=utils.DPI, bbox_inches="tight")
quake_10x_mammary_gland.write_dataset("build/mammary_gland/Quake_10x_Mammary_Gland/Quake_10x_Mammary_Gland.h5")
%%capture capio
quake_10x_mammary_gland_models = [quake_10x_mammary_gland_model]
for i in range(1, cb.config.N_JOBS):
    print("==== Model %d ====" % i)
    quake_10x_mammary_gland_models.append(cb.directi.fit_DIRECTi(
        quake_10x_mammary_gland, quake_10x_mammary_gland.uns["seurat_genes"],
        **fixed_model_kwargs, 
        random_seed=i
    ))
quake_10x_mammary_gland_blast = cb.blast.BLAST(quake_10x_mammary_gland_models, quake_10x_mammary_gland)
quake_10x_mammary_gland_blast.save("build/mammary_gland/Quake_10x_Mammary_Gland")
with open("build/mammary_gland/Quake_10x_Mammary_Gland/stdout.txt", "w") as f:
    f.write(capio.stdout)
with open("build/mammary_gland/Quake_10x_Mammary_Gland/stderr.txt", "w") as f:
    f.write(capio.stderr)
utils.self_projection(quake_10x_mammary_gland_blast, "build/mammary_gland/Quake_10x_Mammary_Gland")
%%writefile build/mammary_gland/Quake_10x_Mammary_Gland/predictable.txt
cell_ontology_class
cell_type1
cluster
free_annotation<jupyter_output><empty_output><jupyter_text>Quake_Smart-seq2_Mammary_Gland<jupyter_code>quake_smart_seq2_mammary_gland = cb.data.ExprDataSet.read_dataset("../../Datasets/data/Quake_Smart-seq2_Mammary_Gland/data.h5")
utils.peek(quake_smart_seq2_mammary_gland, "build/mammary_gland/Quake_Smart-seq2_Mammary_Gland")
quake_smart_seq2_mammary_gland.obs.head()
quake_smart_seq2_mammary_gland.obs["cluster"] = "cluster_" + quake_smart_seq2_mammary_gland.obs["cluster"].astype(int).astype(str)
quake_smart_seq2_mammary_gland.obs.dtypes
opt_model_kwargs = dict(batch_effect="donor")
quake_smart_seq2_mammary_gland_model = cb.directi.fit_DIRECTi(
    quake_smart_seq2_mammary_gland, quake_smart_seq2_mammary_gland.uns["seurat_genes"],
    **fixed_model_kwargs, **opt_model_kwargs
)
quake_smart_seq2_mammary_gland.latent = quake_smart_seq2_mammary_gland_model.inference(quake_smart_seq2_mammary_gland)
ax = quake_smart_seq2_mammary_gland.visualize_latent("cell_ontology_class", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Quake_Smart-seq2_Mammary_Gland/cell_ontology_class.svg", dpi=utils.DPI, bbox_inches="tight")
ax = quake_smart_seq2_mammary_gland.visualize_latent("cell_type1", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Quake_Smart-seq2_Mammary_Gland/cell_type1.svg", dpi=utils.DPI, bbox_inches="tight")
ax = quake_smart_seq2_mammary_gland.visualize_latent("cluster", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Quake_Smart-seq2_Mammary_Gland/cluster.svg", dpi=utils.DPI, bbox_inches="tight")
ax = quake_smart_seq2_mammary_gland.visualize_latent("free_annotation", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Quake_Smart-seq2_Mammary_Gland/free_annotation.svg", dpi=utils.DPI, bbox_inches="tight")
ax = quake_smart_seq2_mammary_gland.visualize_latent("donor", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Quake_Smart-seq2_Mammary_Gland/donor.svg", dpi=utils.DPI, bbox_inches="tight")
quake_smart_seq2_mammary_gland.write_dataset("build/mammary_gland/Quake_Smart-seq2_Mammary_Gland/Quake_Smart-seq2_Mammary_Gland.h5")
%%capture capio
quake_smart_seq2_mammary_gland_models = [quake_smart_seq2_mammary_gland_model]
for i in range(1, cb.config.N_JOBS):
    print("==== Model %d ====" % i)
    quake_smart_seq2_mammary_gland_models.append(cb.directi.fit_DIRECTi(
        quake_smart_seq2_mammary_gland, quake_smart_seq2_mammary_gland.uns["seurat_genes"],
        **fixed_model_kwargs, **opt_model_kwargs,
        random_seed=i
    ))
quake_smart_seq2_mammary_gland_blast = cb.blast.BLAST(quake_smart_seq2_mammary_gland_models, quake_smart_seq2_mammary_gland)
quake_smart_seq2_mammary_gland_blast.save("build/mammary_gland/Quake_Smart-seq2_Mammary_Gland")
with open("build/mammary_gland/Quake_Smart-seq2_Mammary_Gland/stdout.txt", "w") as f:
    f.write(capio.stdout)
with open("build/mammary_gland/Quake_Smart-seq2_Mammary_Gland/stderr.txt", "w") as f:
    f.write(capio.stderr)
utils.self_projection(quake_smart_seq2_mammary_gland_blast, "build/mammary_gland/Quake_Smart-seq2_Mammary_Gland")
%%writefile build/mammary_gland/Quake_Smart-seq2_Mammary_Gland/predictable.txt
cell_ontology_class
cell_type1
cluster
free_annotation<jupyter_output><empty_output><jupyter_text>Bach<jupyter_code>bach = cb.data.ExprDataSet.read_dataset("../../Datasets/data/Bach/data.h5")
utils.peek(bach, "build/mammary_gland/Bach")
bach.obs.head()
bach.obs["donor"] = "donor_" + bach.obs["donor"]
bach.obs.dtypes
bach_model = cb.directi.fit_DIRECTi(
    bach, bach.uns["seurat_genes"],
    **fixed_model_kwargs
)
bach.latent = bach_model.inference(bach)
plot = bach.visualize_latent("cell_ontology_class")
ax.get_figure().savefig("build/mammary_gland/Bach/cell_ontology_class.svg", dpi=utils.DPI, bbox_inches="tight")
ax = bach.visualize_latent("cell_type1", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Bach/cell_type1.svg", dpi=utils.DPI, bbox_inches="tight")
ax = bach.visualize_latent("lifestage", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Bach/lifestage.svg", dpi=utils.DPI, bbox_inches="tight")
ax = bach.visualize_latent("donor", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Bach/donor.svg", dpi=utils.DPI, bbox_inches="tight")
bach.write_dataset("build/mammary_gland/Bach/Bach.h5")
%%capture capio
bach_models = [bach_model]
for i in range(1, cb.config.N_JOBS):
    print("==== Model %d ====" % i)
    bach_models.append(cb.directi.fit_DIRECTi(
        bach, bach.uns["seurat_genes"],
        **fixed_model_kwargs, 
        random_seed=i
    ))
bach_blast = cb.blast.BLAST(bach_models, bach)
bach_blast.save("build/mammary_gland/Bach")
with open("build/mammary_gland/Bach/stdout.txt", "w") as f:
    f.write(capio.stdout)
with open("build/mammary_gland/Bach/stderr.txt", "w") as f:
    f.write(capio.stderr)
utils.self_projection(bach_blast, "build/mammary_gland/Bach")
%%writefile build/mammary_gland/Bach/predictable.txt
cell_ontology_class
cell_type1
lifestage<jupyter_output><empty_output><jupyter_text>Giraddi_10x<jupyter_code>giraddi_10x = cb.data.ExprDataSet.read_dataset("../../Datasets/data/Giraddi_10x/data.h5")
utils.peek(giraddi_10x, "build/mammary_gland/Giraddi_10x")
giraddi_10x.obs.head()
giraddi_10x.obs["pseudotime"] = giraddi_10x.obs["pseudotime"].astype(np.int)
giraddi_10x.obs.dtypes
giraddi_10x_model = cb.directi.fit_DIRECTi(
    giraddi_10x, giraddi_10x.uns["seurat_genes"],
    **{**fixed_model_kwargs, "cat_dim": None}
)
giraddi_10x.latent = giraddi_10x_model.inference(giraddi_10x)
ax = giraddi_10x.visualize_latent("cell_ontology_class", method="UMAP", reuse=False, dr_kws=dict(n_neighbors=5), scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Giraddi_10x/cell_ontology_class.svg", dpi=utils.DPI, bbox_inches="tight")
ax = giraddi_10x.visualize_latent("cell_type1", method="UMAP", reuse=False, dr_kws=dict(n_neighbors=5), scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Giraddi_10x/cell_type1.svg", dpi=utils.DPI, bbox_inches="tight")
ax = giraddi_10x.visualize_latent("lifestage", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Giraddi_10x/lifestage.svg", dpi=utils.DPI, bbox_inches="tight")
ax = giraddi_10x.visualize_latent("pseudotime", method="UMAP", reuse=False, dr_kws=dict(n_neighbors=5), scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Giraddi_10x/pseudotime.svg", dpi=utils.DPI, bbox_inches="tight")
giraddi_10x.write_dataset("build/mammary_gland/Giraddi_10x/Giraddi_10x.h5")
%%capture capio
giraddi_10x_models = [giraddi_10x_model]
for i in range(1, cb.config.N_JOBS):
    print("==== Model %d ====" % i)
    giraddi_10x_models.append(cb.directi.fit_DIRECTi(
        giraddi_10x, giraddi_10x.uns["seurat_genes"],
        **{**fixed_model_kwargs, "cat_dim": None},
        random_seed=i
    ))
giraddi_10x_blast = cb.blast.BLAST(giraddi_10x_models, giraddi_10x)
giraddi_10x_blast.save("build/mammary_gland/Giraddi_10x")
with open("build/mammary_gland/Giraddi_10x/stdout.txt", "w") as f:
    f.write(capio.stdout)
with open("build/mammary_gland/Giraddi_10x/stderr.txt", "w") as f:
    f.write(capio.stderr)
utils.self_projection(giraddi_10x_blast, "build/mammary_gland/Giraddi_10x")
%%writefile build/mammary_gland/Giraddi_10x/predictable.txt
cell_ontology_class
cell_type1
lifestage
pseudotime<jupyter_output><empty_output><jupyter_text>Mouse aligned<jupyter_code>if not os.path.exists("build/mammary_gland/ALIGNED_Mus_musculus_Mammary_Gland"):
    os.makedirs("build/mammary_gland/ALIGNED_Mus_musculus_Mammary_Gland")
sun = cb.data.ExprDataSet.read_dataset("../../Datasets/data/Sun/data.h5")
quake_smart_seq2_mammary_gland.obs["quake_smart_seq2_mammary_gland_donor"] = \
    quake_smart_seq2_mammary_gland.obs["donor"]
mouse = cb.data.ExprDataSet.merge_datasets(dict(
    quake_10x_mammary_gland=quake_10x_mammary_gland,
    quake_smart_seq2_mammary_gland=quake_smart_seq2_mammary_gland,
    sun=sun,
    bach=bach
), merge_uns_slots=["seurat_genes"])
mouse.obs.loc[mouse.obs["lifestage"] == "pregnant", "lifestage"] = "G"
mouse.obs.loc[np.logical_or(
    mouse.obs["lifestage"] == "virgin",
    np.in1d(mouse.obs["dataset_name"], ["Quake_10x_Mammary_Gland", "Quake_Smart-seq2_Mammary_Gland"])
), "lifestage"] = "NP"
mouse.obs["NP_dataset"] = mouse.obs["dataset_name"]
mouse.obs["G_dataset"] = mouse.obs["dataset_name"]
mouse.obs.loc[mouse.obs["lifestage"] != "NP", "NP_dataset"] = np.nan
mouse.obs.loc[mouse.obs["lifestage"] != "G", "G_dataset"] = np.nan
mouse.obs.head()
mouse.obs.dtypes
opt_model_kwargs = dict(
    batch_effect=["quake_smart_seq2_mammary_gland_donor", "NP_dataset", "G_dataset"],
    #rmbatch_module=["MNNAdversarial", "Adversarial", "Adversarial", "Adversarial"],
    rmbatch_module_kwargs=dict(lambda_reg=0.005)
)  # Many batch effects are being aligned, decrease regularization strength accordingly
mouse_model = cb.directi.fit_DIRECTi(
    mouse, mouse.uns["seurat_genes"],
    **fixed_model_kwargs, **opt_model_kwargs
)
mouse.latent = mouse_model.inference(mouse)
ax = mouse.visualize_latent("cell_ontology_class", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/ALIGNED_Mus_musculus_Mammary_Gland/cell_ontology_class.svg", dpi=utils.DPI, bbox_inches="tight")
ax = mouse.visualize_latent("dataset_name", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/ALIGNED_Mus_musculus_Mammary_Gland/dataset_name.svg", dpi=utils.DPI, bbox_inches="tight")
ax = mouse.visualize_latent("lifestage", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/ALIGNED_Mus_musculus_Mammary_Gland/lifestage.svg", dpi=utils.DPI, bbox_inches="tight")
mouse.write_dataset("build/mammary_gland/ALIGNED_Mus_musculus_Mammary_Gland/ALIGNED_Mus_musculus_Mammary_Gland.h5")
%%capture capio
mouse_models = [mouse_model]
for i in range(1, cb.config.N_JOBS):
    print("==== Model %d ====" % i)
    mouse_models.append(cb.directi.fit_DIRECTi(
        mouse, mouse.uns["seurat_genes"],
        **fixed_model_kwargs, **opt_model_kwargs,
        random_seed=i
    ))
mouse_blast = cb.blast.BLAST(mouse_models, mouse)
mouse_blast.save("build/mammary_gland/ALIGNED_Mus_musculus_Mammary_Gland")
with open("build/mammary_gland/ALIGNED_Mus_musculus_Mammary_Gland/stdout.txt", "w") as f:
    f.write(capio.stdout)
with open("build/mammary_gland/ALIGNED_Mus_musculus_Mammary_Gland/stderr.txt", "w") as f:
    f.write(capio.stderr)
utils.self_projection(mouse_blast, "build/mammary_gland/ALIGNED_Mus_musculus_Mammary_Gland")
%%writefile build/mammary_gland/ALIGNED_Mus_musculus_Mammary_Gland/predictable.txt
cell_ontology_class<jupyter_output><empty_output><jupyter_text>--- Human Nguyen_10x<jupyter_code>nguyen_10x = cb.data.ExprDataSet.read_dataset("../../Datasets/data/Nguyen_10x/data.h5")
utils.peek(nguyen_10x, "build/mammary_gland/Nguyen_10x")
nguyen_10x.obs.head()
nguyen_10x.obs.dtypes
opt_model_kwargs = dict(batch_effect="donor")
nguyen_10x_model = cb.directi.fit_DIRECTi(
    nguyen_10x, nguyen_10x.uns["seurat_genes"],
    **fixed_model_kwargs, **opt_model_kwargs
)
nguyen_10x.latent = nguyen_10x_model.inference(nguyen_10x)
ax = nguyen_10x.visualize_latent("cell_ontology_class", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Nguyen_10x/cell_ontology_class.svg", dpi=utils.DPI, bbox_inches="tight")
ax = nguyen_10x.visualize_latent("cell_type1", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Nguyen_10x/cell_type1.svg", dpi=utils.DPI, bbox_inches="tight")
ax = nguyen_10x.visualize_latent("lifestage", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Nguyen_10x/lifestage.svg", dpi=utils.DPI, bbox_inches="tight")
ax = nguyen_10x.visualize_latent("donor", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/Nguyen_10x/donor.svg", dpi=utils.DPI, bbox_inches="tight")
nguyen_10x.write_dataset("build/mammary_gland/Nguyen_10x/Nguyen_10x.h5")
%%capture capio
nguyen_10x_models = [nguyen_10x_model]
for i in range(1, cb.config.N_JOBS):
    print("==== Model %d ====" % i)
    nguyen_10x_models.append(cb.directi.fit_DIRECTi(
        nguyen_10x, nguyen_10x.uns["seurat_genes"],
        **fixed_model_kwargs, **opt_model_kwargs,
        random_seed=i
    ))
nguyen_10x_blast = cb.blast.BLAST(nguyen_10x_models, nguyen_10x)
nguyen_10x_blast.save("build/mammary_gland/Nguyen_10x")
with open("build/mammary_gland/Nguyen_10x/stdout.txt", "w") as f:
    f.write(capio.stdout)
with open("build/mammary_gland/Nguyen_10x/stderr.txt", "w") as f:
    f.write(capio.stderr)
utils.self_projection(nguyen_10x_blast, "build/mammary_gland/Nguyen_10x")
%%writefile build/mammary_gland/Nguyen_10x/predictable.txt
cell_ontology_class
cell_type1<jupyter_output><empty_output><jupyter_text>Human aligned<jupyter_code>if not os.path.exists("build/mammary_gland/ALIGNED_Homo_sapiens_Mammary_Gland"):
    os.makedirs("build/mammary_gland/ALIGNED_Homo_sapiens_Mammary_Gland")
nguyen_c1 = cb.data.ExprDataSet.read_dataset("../../Datasets/data/Nguyen_C1/data.h5")
nguyen_10x.obs["nguyen_10x_donor"] = nguyen_10x.obs["donor"]
human = cb.data.ExprDataSet.merge_datasets(dict(
    nguyen_10x=nguyen_10x,
    nguyen_c1=nguyen_c1
), merge_uns_slots=["seurat_genes"])
human.obs.dtypes
opt_model_kwargs = dict(
    batch_effect=["dataset_name", "nguyen_10x_donor"],
    rmbatch_module_kwargs=[dict(lambda_reg=0.02), dict(lambda_reg=0.01)],
    prob_module_kwargs=dict(lambda_reg=0.01), patience=100
)
human_model = cb.directi.fit_DIRECTi(
    human, human.uns["seurat_genes"],
    **{**fixed_model_kwargs, **opt_model_kwargs}
)
human.latent = human_model.inference(human)
ax = human.visualize_latent("cell_ontology_class", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/ALIGNED_Homo_sapiens_Mammary_Gland/cell_ontology_class.svg", bbox_inches="tight")
ax = human.visualize_latent("nguyen_10x_donor", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/ALIGNED_Homo_sapiens_Mammary_Gland/donor.svg", dpi=utils.DPI, bbox_inches="tight")
ax = human.visualize_latent("dataset_name", scatter_kws=dict(rasterized=True))
ax.get_figure().savefig("build/mammary_gland/ALIGNED_Homo_sapiens_Mammary_Gland/dataset_name.svg", dpi=utils.DPI, bbox_inches="tight")
human.write_dataset("build/mammary_gland/ALIGNED_Homo_sapiens_Mammary_Gland/ALIGNED_Homo_sapiens_Mammary_Gland.h5")
%%capture capio
human_models = [human_model]
for i in range(1, cb.config.N_JOBS):
    print("==== Model %d ====" % i)
    human_models.append(cb.directi.fit_DIRECTi(
        human, human.uns["seurat_genes"],
        **{**fixed_model_kwargs, **opt_model_kwargs},
        random_seed=i
    ))
human_blast = cb.blast.BLAST(human_models, human)
human_blast.save("build/mammary_gland/ALIGNED_Homo_sapiens_Mammary_Gland")
with open("build/mammary_gland/ALIGNED_Homo_sapiens_Mammary_Gland/stdout.txt", "w") as f:
    f.write(capio.stdout)
with open("build/mammary_gland/ALIGNED_Homo_sapiens_Mammary_Gland/stderr.txt", "w") as f:
    f.write(capio.stderr)
utils.self_projection(human_blast, "build/mammary_gland/ALIGNED_Homo_sapiens_Mammary_Gland")
%%writefile build/mammary_gland/ALIGNED_Homo_sapiens_Mammary_Gland/predictable.txt
cell_ontology_class<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1324939416289116, 'fraction_numerical': 0.02038773574965757, 'mean_word_length': 8.46783042394015, 'pattern_counts': {'":': 2, '<': 29, '<?xml version=': 0, '>': 29, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '183949', 'license': 'MIT', 'n_tokens_mistral': 8360, 'n_tokens_neox': 8014, 'n_words': 723, 'path': 'Notebooks/Database/mammary_gland.ipynb', 'repo_name': 'gao-lab/Cell_BLAST', 'size': '18982'}