pciSeq 0.0.32

Probabilistic cell typing for spatial transcriptomics

pciSeq: Probabilistic Cell typing by In situ Sequencing

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

Installation

pip install pciSeq

Requirement: Python >= 3.7

Usage

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)

Demo

You can run a pciSeq demo in google colab:

References

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