stLearn - a downstream analysis toolkit for Spatial Transcriptomics data¶
stLearn is designed to comprehensively analyse Spatial Transcriptomics (ST) data to investigate complex biological processes within an undissociated tissue. ST is emerging as the “next generation” of single-cell RNA sequencing because it adds spatial and morphological context to the transcriptional profile of cells in an intact tissue section. However, existing ST analysis methods typically use the captured spatial and/or morphological data as a visualisation tool rather than as informative features for model development. We have developed an analysis method that exploits all three data types: Spatial distance, tissue Morphology, and gene Expression measurements (SME) from ST data. This combinatorial approach allows us to more accurately model underlying tissue biology, and allows researchers to address key questions in three major research areas: cell type identification, cell trajectory reconstruction, and the study of cell-cell interactions within an undissociated tissue sample.
We also published stLearn-interactive which is a python-based interactive website for working with all the functions from stLearn and upgrade with some bokeh-based plots.
In the new release, we provide the interactive plots:
Add basic unittest (will add more in the future).
convert_scanpy()to convert object from scanpy to stLearn.
Fixed issue with networkx object cannot write h5ad file.