stimwrap 0.1.7

Stimwrap: a Python interface for STIM

stimwrap - a python interface for STIM

stimwrap is a Python wrapper for the spatial transcriptomics library STIM. It provides an interface for extracting datasets and their attributes from n5 containers that are created by STIM.

stimwrap is created and maintained by Nikos Karaiskos.

Installation

To install the stimwrap try:

pip install stimwrap

or if you do not have sudo rights:

pip install --user stimwrap

Check if the library is successfully installed:

import stimwrap as sw

If installation fails due to conflicting dependencies, create a dedicated environment with python=3.7 and try again to install stimwrap as above.

Usage

The following assumes that the file container.n5 contains the datasets and their attributes as created by STIM:

pucks = sw.get_container('/path/to/container.n5')

Print the names of the datasets:

print(sw.get_dataset_names(pucks))

Focus on a specific puck and extract the relevant information:

puck_name = sw.get_datset_names(pucks)[0]
puck = sw.get_datset(pucks, puck_name)

Get the puck locations either directly from the puck:

locations = puck['locations']

or fetch them from the container:

locations = sw.get_item_from_dataset(pucks, puck_name, item='locations')

Fetch gene expression

It is possible to get the expression vector of a single gene:

hpca_vec = sw.get_gene_expression_from_dataset(pucks, puck_name, gene='Hpca')

or the whole gene expression matrix:

dge = sw.get_gene_expression_from_dataset(pucks, puck_name, gene='all')

Fetch dataset attributes

STIM stores the dataset attributes in the n5 container. These can be directly accessed with stimwrap:

sw.get_attribute_from_dataset(pucks, puck_name, attribute='geneList')

Available options also include: barcodeList and metadataList.

Fetch aligned locations

In the case where multiple consecutive sections are obtained and aligned with STIM, the aligned locations can be obtained with:

aligned_locations = sw.get_aligned_locations(pucks, puck_name,
                                                 transformation='model_sift')