Title	Description
Loading and decoding ISS data [course lab]	The first lab (01_ISS_data_loading.ipynb) includes examples on how to prepare data from starfish, read it, deconvolve and register. The second (02_ISS_decoding.ipynb) shows how to use starfish for decoding spots and performing quality contorl on TissUUmaps.
TissUUmaps to visualize starfish decoding experiments [video]	How to visualize starfish decoding experiments in TissUUmaps. It follows the oficial starfish Pixel-Based Decoding tutorial and then converts the outputs to a compatible format to the spot inspector plugin for quality control. Find the JupyterNotebook at pixel_decoding_starfish.ipynb and the helper functions at starfish2tmap.py.
TissUUmaps to explore ST analysis tools [video]	How to use TissUUmaps to visualize the outputs from Giotto and Squidpy. The original tutorial for analyzing 10X Visium (Giotto) can be found here, and the code for integration with TissUUmaps at giotto2tmap.py The original tutorial for Analyze Visium H&E data (Squidpy) can be found here, and the code for integration with TissUUmaps at squidpy2tmap.ipynb.
Visualizing CNN features in Jupyter Notebook [video]	How to extract morphological features from any spatial transcriptomics (ST) dataset using any pretrained CNN. In the end, we will have a vector of features for every ST spot in the image, that we can then reduce to 3 dimensions and map back to the image as RGB colors. You can find the Jupyter Notebook at tissuumaps_cnn_example.ipynb.