spatialx-connector 0.1.3

BioTuring SpatialX Connector

BioTuring SpatialX Connector

Import

import os
import spatialx_connector
from spatialx_connector import SpatialXConnector
import warnings

warnings.filterwarnings("ignore")

Domain and Token

DOMAIN = "INPUT DOMAIN HERE"
TOKEN = "INPUT TOKEN HERE"

Explore Account

connector = SpatialXConnector(domain=DOMAIN, token=TOKEN)
spatialx_connector.format_print(connector.info)

spatialx_connector.format_print(connector.groups)

spatialx_connector.format_print(connector.s3)

spatialx_connector.format_print(connector.folders)

studies = connector.list_study(
    group=spatialx_connector.DefaultGroup.PERSONAL_WORKSPACE.value,
    species=spatialx_connector.Species.HUMAN,
)
spatialx_connector.format_print(studies)

study_details = connector.get_study_detail(studies[0]["study_id"])
spatialx_connector.format_print(study_details)

samples = connector.list_sample(studies[0]["study_id"])
spatialx_connector.format_print(samples)

sample_details = connector.get_sample_detail(samples[0]["sample_id"])
spatialx_connector.format_print(sample_details)

Uploading

uploading_results = connector.upload_file("/s3/colab/content/xenium/experiment.xenium")
spatialx_connector.format_print(uploading_results)

uploading_results = connector.upload_big_file("/s3/colab/content/xenium/morphology_mip.ome.tif", debug_mode=True)
spatialx_connector.format_print(uploading_results)

uploading_results = connector.upload_folder("/s3/colab/content/xenium", debug_mode=True)
spatialx_connector.format_print(uploading_results)

Submission

visium_submission_information = connector.parse_data_information(
    "Visium_V2_Human_Colon_Cancer_P2",
    spatialx_connector.Technologies.VISIUM,
    os.path.join(
        connector.s3["bioturingpublic"],
        "SpatialX_datasets/Human_Colon_Cancer_P2/Visium_V2_Human_Colon_Cancer_P2"
    )
)
spatialx_connector.format_print(visium_submission_information)

xenium_submission_information = connector.parse_data_information(
    "Xenium_V1_Human_Colon_Cancer_P2_CRC_Add_on_FFPE",
    spatialx_connector.Technologies.XENIUM,
    os.path.join(
        connector.s3["bioturingpublic"],
        "SpatialX_datasets/Human_Colon_Cancer_P2/Xenium_V1_Human_Colon_Cancer_P2_CRC_Add_on_FFPE"
    )
)
spatialx_connector.format_print(xenium_submission_information)

submission_results = connector.submit(
    spatialx_connector.DefaultGroup.PERSONAL_WORKSPACE.value,
    spatialx_connector.Species.HUMAN,
    "10xgenomics",
    "Human_Colon_Cancer_P2",
    visium_submission_information
)
spatialx_connector.format_print(submission_results)

xenium_submission_results = connector.add_sample_data(
    submission_results["study_id"],
    submission_results["sample_id"],
    xenium_submission_information,
)
submission_results["sample_data"].extend(xenium_submission_results["sample_data"])
spatialx_connector.format_print(submission_results)

Analysis

data_id = submission_results["sample_data"][-1]["data_id"]
data_id

Embeddings

response = connector.analysis.embeddings.pca(data_id=data_id, title="Connector - PCA")
spatialx_connector.format_print(response)

response = connector.analysis.embeddings.scvi(data_id=data_id, title="Connector - scVI", n_top_genes=2000)
spatialx_connector.format_print(response)

embeddings = connector.analysis.list_embedding(data_id)
spatialx_connector.format_print(embeddings)

response = connector.analysis.embeddings.umap(data_id=data_id, embedding_key=embeddings[0], title="Connector - UMAP")
spatialx_connector.format_print(response)

response = connector.analysis.embeddings.tsne(data_id=data_id, embedding_key=embeddings[0], title="Connector - tSNE")
spatialx_connector.format_print(response)

Clustering

response = connector.analysis.clustering.louvain(
    data_id=data_id,
    embedding_key=embeddings[0],
    resolution=0.1,
    title="Connector - Louvain",
)
spatialx_connector.format_print(response)

response = connector.analysis.clustering.kmeans(
    data_id=data_id,
    embedding_key=embeddings[0],
    n_clusters=5,
    title="Connector - k-means",
)
spatialx_connector.format_print(response)

Prediction

embeddings = connector.analysis.list_embedding(data_id)
spatialx_connector.format_print(embeddings)

metadata = connector.analysis.list_metadata(data_id)
spatialx_connector.format_print(metadata)

response = connector.analysis.prediction.metadata_reference(
    data_id=data_id,
    cluster_key=metadata[0],
    species=spatialx_connector.Species.HUMAN,
    title="Connector - Metadata Reference",
)
spatialx_connector.format_print(response)

Differential Expression

response = connector.analysis.de.differential_expression_genes(
    data_id_1=data_id,
    data_id_2=data_id,
    group_1_indices=[i for i in range(10000)],
    group_2_indices=[i for i in range(10000, 20000)],
    title="Connector - DE genes",
)
spatialx_connector.format_print(response)

Spatial Analysis

response = connector.analysis.spatial_analysis.region_segmentation(
    data_id=data_id,
    radius=50,
    mpp=0.2125,
    resolution=0.5,
    species=spatialx_connector.Species.HUMAN,
    title="Connector - Region Segmentation",
)
spatialx_connector.format_print(response)

Convert data from Lens

!pip install bioturing_connector

LENS_SC_HOST: str = "LENS_SC_HOST"
LENS_SC_TOKEN: str = "LENS_SC_TOKEN"
lens_sc_studies = connector.list_lens_sc_studies(
    host=LENS_SC_HOST, token=LENS_SC_TOKEN,
    group=spatialx_connector.DefaultGroup.PERSONAL_WORKSPACE,
    species=spatialx_connector.Species.HUMAN,
)
spatialx_connector.format_print(lens_sc_studies)

# Convert a study
connector.convert_data_from_lens(lens_sc_studies[0])

LENS_BULK_HOST: str = "LENS_BULK_HOST"
LENS_BULK_TOKEN: str = "LENS_BULK_TOKEN"
lens_bulk_studies = connector.list_lens_bulk_studies(
    host=LENS_BULK_HOST, token=LENS_BULK_TOKEN,
    group=spatialx_connector.DefaultGroup.PERSONAL_WORKSPACE,
    species=spatialx_connector.Species.HUMAN,
)
spatialx_connector.format_print(lens_bulk_studies)

# Convert multiple studies
connector.convert_data_from_lens(lens_bulk_studies)