sc-3D 0.1.7

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 Guignard Lab 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 sc3D code is based on the anndata and 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 for the unregistered dataset (and test the provided algorithms) and there for the already registered atlas to browse with our visualiser.

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

• setup.py: Setup file to install the library

• src/sc3D-visualiser: the script to run the visualiser (for specifics about the visualiser, please look directly there)

Installation

We strongly advise to use virtual environments to install this package. For example using conda or miniconda:

conda create -n sc-3D
conda activate sc-3D

If necessary, install pip:

conda install pip

Then, to install the latest stable version:

pip install sc-3D

or to install the latest version from the GitHub repository:

git clone https://github.com/GuignardLab/sc3D.git
cd sc3D
pip install .

Troubleshooting for latest M1 MacOs chips.

If working with an M1 chip, it is possible that all the necessary libraries are not yet available from the usual channels.

To overcome this issue we recommand to manually install the latest, GitHub version of sc3D using miniforge instead of anaconda or miniconda.

Once miniforge is installed and working, you can run the following commands:

conda create -n sc-3D
conda activate sc-3D

to create your environment, then:

git clone https://github.com/GuignardLab/sc3D.git
cd sc3D
conda install pip scipy numpy matplotlib pandas seaborn anndata napari
pip install .

If the previous commands are still not working, it is possible that you need to install the pkg-config package. You can find some information on how to do it there: install pkg-config

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 ".*_[0-9]*" where the digits after the underscore (_) are the id of the array
• 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']

Since version 0.1.2, one can specify the name of the columns where the different necessary informations are stored using the following parameters:

• tissue_id to inform the tissue id column
• array_id to inform the array/puck/slice id column
• pos_id to inform the position column (an x, y position is expected within this given column)
• gene_name_id to inform the gene name column
• pos_reg_id when to inform the registered position column (an x, y, z position is expected within this given column)

# 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 save the dataset as a registered dataset (to then look at it in the 3D visualizer)
embryo.save_anndata('path/to/out/registered.h5ad')

# 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.

Visualiser

"Quick" start (from scratch):

Installation

1. Installing miniconda

In order to help a smooth installation, one can use miniconda (that is what we do).

You can check there to see how to install miniconda.

In a nutshell, from a terminal, the following lines could work for MacOs:

curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh > Miniconda3-latest-MacOSX-x86_64.sh
bash Miniconda3-latest-MacOSX-x86_64.sh

Similarly, for Linux one could install miniconda by running the following commands:

curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh > Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

2. Installing the visualiser

Once miniconda is installed one can install the visualiser.

To do so, it is necessary to either download or clone the repository from GitHub (https://github.com/GuignardLab/sc3D to download, there is the green "Code" button). To clone the repository one can do it the following way:

git clone https://github.com/GuignardLab/sc3D.git

Once downloaded or cloned, one can access the said folder from a terminal:

cd path/to/sc3D

Once there it is probably better to create a virtual environment thanks to miniconda:

conda create -n sc3D python">=3.9"

Then activate it:

conda activate sc3D

From then you want to install pip:

conda install pip

and finally install the library and the script (still from the folder sc3D):

pip install .

Now, the visualiser is installed, you should close your terminal (even if you plan on using the visualiser directly, you will need to open a new terminal anyway).

2.1 Troubleshooting for latest M1 MacOs chips.

If working with an M1 chip, it is possible that all the necessary libraries are not yet available from the usual channels.

To overcome this issue we recommand to manually install the latest, GitHub version of sc3D using miniforge instead of anaconda or miniconda.

Once miniforge is installed and working, you can run the following commands:

conda create -n sc-3D
conda activate sc-3D

to create your environment, then:

git clone https://github.com/GuignardLab/sc3D.git
cd sc3D
conda install pip scipy numpy matplotlib pandas seaborn anndata napari
pip install .

If the previous commands are still not working, it is possible that you need to install the pkg-config package. You can find some information on how to do it there: install pkg-config

3. Running the visualiser

To run the visualiser, you want to

• start a new terminal
• activate your conda environement: conda activate sc3D
• start the visualiser by typing: sc3D-visualiser (from anywhere in a terminal)

Then you can load the dataset and play with it. The h5ad file can be find there. The Tissue name file can be find in data/corresptissues.json.

"Have fun"