sc-3D 0.1.0

Array alignment and 3D differential expression for 3D sc omics

sc3D

sc3D is a Python library to handle 3D spatial transcriptomic datasets.

You can find it on the GuignardLab GitHub page: GuignardLab/sc3D. In there you will be able to find jupyter notebooks giving examples about how to use the datasets.

This code was developed in the context of the following study:

Spatial transcriptomic maps of whole mouse embryos reveal principles of neural tube patterning. Abhishek Sampath Kumar, Luyi Tian, Adriano Bolondi et al.

The whole code is based on the anndata/Scanpy libraries and allows to read, register arrays and compute 3D differential expression.

The dataset necessary to run the tests and look at the results can be downloaded there (it is under the name mouse_embryo_E8.5_merged_data).

Description of the GitHub repository

• data: a folder containing examples for the tissue color and tissue name files

• src: a folder containing the source code

• txt: a folder containing the text describing the method (LaTeX, pdf and docx formats are available)

• README.md: this file

• Test-embryo.ipynb: Basic read and write examples (many different ways of writing)

• Spatial-differential-expression.ipynb: a jupyter notebook with some examples on how to perform the spatial differential expression

• spec-file.txt: File containing all the libraries necessary to run sc3D

Installation

The current version of sc3D has only been tested with the Python 3.8 (though it is not working with Python>=3.9 because of the open3d dependency)

We strongly advise to use virtual environments to install this package. For example:

conda create -n sc-3D python==3.8
conda activate sc-3D

If necessary, install pip:

conda install pip

Then, the installation can be done directly from pip:

pip install sc-3D

or from the GitHub repository:

pip install .

Basic usage

Once installed, the library can be called the following way:

from sc3D import Embryo

To import some data:

Note: at the time being, the following conventions are expected:

• the x-y coordinates are stored in data.obsm['X_spatial']
• the array number should be stored in data.obs['orig.ident'] in the format "{digits}_{array_id:digit}"
• the tissue type has to be stored in data.obs['predicted.id']
• the gene names have to be stored as indices or in data.var['feature_name']

# To read the data
embryo = Embryo('path/to/data.h5ad')

# To remove potential spatial outliers
embryo.removing_spatial_outliers(th=outlier_threshold)

# To register the arrays and compute the
# spline interpolations
embryo.reconstruct_intermediate(embryo, th_d=th_d,
                                genes=genes_of_interest)

# To compute the 3D differential expression for selected tissues
tissues_to_process = [5, 10, 12, 18, 21, 24, 30, 31, 33, 34, 39]
th_vol = .025
_ = embryo.get_3D_differential_expression(tissues_to_process, th_vol)

The dataset used for the project this code is from can be downloaded there (under the name mouse_embryo_E8.5_merged_data)

Many other functions are available that can be found used in the two provided jupyter notebooks.

Running the notebooks

Two example notebooks are provided. To run them one wants to first install the jupyter notebook:

conda install jupyter

or

pip install jupyter

The notebooks can the be started from a terminal in the folder containing the .ipynb files with the following command:

jupyter notebook

The notebooks should be self content.

Note that the test dataset is not included in this repository put can be downloaded from there: