A Python package that implements the cell calling algorithm as described in Qian, X., et al. Nature Methods (2020)

python -m pip install pciSeq

Requirement: Python >= 3.8

If you want to work with the source code you can download the repo and then replicate the python environment by

conda env create -n pciSeq -f /path/to/environment.yml

That will create a conda environment with the name pciSeq containing all the necessary packages to run the algorithm. To activate it run

conda activate pciSeq

or, if you open the project in your IDE, then in your project settings, switch your interpreter to the interpreter of the pciSeq env.

You need to create two pandas dataframes for the spots and the single cell data and a coo_matrix for the label image (which in most cases will be the output of some image segmentation application). Then you pass them into the pciSeq.fit() method as follows:

import pciSeq

res = pciSeq.fit(spots_df, label_image, scRNA_df)

See the demo below for a more detailed explanation about the arguments of pciSeq.fit() and its return values.

There is also a fourth argument (optional) to override the default hyperparameter values which are initialised by the config.py module. To pass user-defined hyperparameter values, create a dictionary with keys the hyperparameter names and values their new values. For example, to exclude all Npy and Vip spots you can do:

import pciSeq

opts = { 'exclude_genes': ['Npy', 'Vip'] }
res = pciSeq.fit(spots_df, label_image, scRNA_df, opts)

You can run a pciSeq demo in google colab:

An interactive viewer to explore the data runs on this url. Instructions about building this viewer with your own data are here.
If you have v 0.0.49 or greater you can also launch the viewer automatically by setting opts = {'launch_viewer': True} and passing it to pciSeq.fit(), see

Qian, X., et al. (2020). Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat Methods 17, 101 - 106.