Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region

Jeffrey R. Moffitt, Dhananjay Bambah-Mukku, Stephen W. Eichhorn, Eric Vaughn, Karthik Shekhar, Julio D. Perez, Nimrod D. Rubinstein, Junjie Hao, Aviv Regev, Catherine Dulac, Xiaowei Zhuang

This publication can be found at https://science.sciencemag.org/content/362/6416/eaau5324 and the data referenced below can be downloaded from https://datadryad.org/handle/10255/dryad.192644

Checklist: - [ ] point locations - [ ] cell locations - [x] cell x gene expression matrix

Load the data

import os
import requests
from io import BytesIO

import dask.array as da
import numpy as np
import pandas as pd

import starspace
from starspace.constants import *

response = requests.get(
    "https://d24h2xsgaj29mf.cloudfront.net/raw/merfish_moffit_2018_science_hypothalamic-preoptic/"
    "Moffitt_and_Bambah-Mukku_et_al_merfish_all_cells.csv"
)
data = pd.read_csv(BytesIO(response.content), header=0)

name = "merfish moffit 2018 science hypothalamic preoptic"

This data file is a cell x gene expression matrix that contains additional metadata as columns of the matrix. Extract those extra columns and clean up the data file.

annotation = np.array(data["Cell_class"], dtype="U")
group_id = np.array(data["Neuron_cluster_ID"], dtype="U")
x = data["Centroid_X"]
y = data["Centroid_Y"]
region_id = np.array(data["Cell_ID"], dtype="U")

unstructured_field_names = ["Animal_ID", "Animal_sex", "Behavior", "Bregma"]
unstructured_metadata = data[unstructured_field_names]
non_expression_fields = (
        unstructured_field_names
        + ["Cell_class", "Neuron_cluster_ID", "Centroid_X", "Centroid_Y", "Cell_ID"]
)
expression_data = data.drop(non_expression_fields, axis=1)
gene_name = [v.lower() for v in expression_data.columns]

Write down some important metadata from the publication.

attrs = {
    REQUIRED_ATTRIBUTES.ASSAY: ASSAYS.MERFISH,
    REQUIRED_ATTRIBUTES.SAMPLE_TYPE: "hypothalamic pre-optic nucleus",
    REQUIRED_ATTRIBUTES.AUTHORS: [
        "Jeffrey R. Moffitt", "Dhananjay Bambah-Mukku", "Stephen W. Eichhorn", "Eric Vaughn",
        "Karthik Shekhar", "Julio D. Perez", "Nimrod D. Rubinstein", "Junjie Hao", "Aviv Regev",
        "Catherine Dulac", "Xiaowei Zhuang"
    ],
    REQUIRED_ATTRIBUTES.YEAR: 2018,
    REQUIRED_ATTRIBUTES.ORGANISM: "mouse",
    OPTIONAL_ATTRIBUTES.PUBLICATION_NAME: (
        "Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic "
        "region"
    ),
    OPTIONAL_ATTRIBUTES.PUBLICATION_URL: "https://science.sciencemag.org/content/362/6416/eaau5324",
}

Create the chunked dataset.

chunk_data = da.from_array(expression_data.values, chunks=MATRIX_CHUNK_SIZE)

Wrap the dask array in an xarray, adding the metadata fields as “coordinates”.

# convert columns with object dtype into fixed-length strings

coords = {
    MATRIX_REQUIRED_FEATURES.GENE_NAME: (MATRIX_AXES.FEATURES.value, gene_name),
    MATRIX_REQUIRED_REGIONS.X_REGION: (MATRIX_AXES.REGIONS.value, x),
    MATRIX_REQUIRED_REGIONS.Y_REGION: (MATRIX_AXES.REGIONS.value, y),
    MATRIX_REQUIRED_REGIONS.REGION_ID: (MATRIX_AXES.REGIONS.value, region_id),
    MATRIX_OPTIONAL_REGIONS.GROUP_ID: (MATRIX_AXES.REGIONS.value, group_id),
    MATRIX_OPTIONAL_REGIONS.TYPE_ANNOTATION: (MATRIX_AXES.REGIONS.value, annotation)
}
dims = (MATRIX_AXES.REGIONS.value, MATRIX_AXES.FEATURES.value)
matrix = starspace.Matrix.from_expression_data(
    data=chunk_data, coords=coords, dims=dims, name=name, attrs=attrs
)

s3_url = "s3://starfish.data.output-warehouse/merfish-moffit-2018-science-hypothalamic-preoptic"
url = "merfish-moffit-2018-science-hypothalamic-preoptic"
matrix.save_zarr(url=url)