Analysis Results of SpatialOne Pipeline

This file contains the results of the analysis of the sample files used to test and validate [SpatialOne](https://github.com/Sanofi-Public/spatialone-pipeline).

Datasets

These analyses correspond to the following datasets:

2 Human Lung Cancer Formalin-Fixed, Paraffin-Embedded (FFPE) Samples

These samples are prepared following the CG000495 protocol, sequenced with the 10x Visium CytAssist, and processed using the 10x SpaceRanger version 2.

2 Mice Kidney Samples

Two kidney samples processed using SpaceRanger v1.

1 Mice Brain Sample

Fresh frozen brain tissue section processed using SpaceRanger v2.

Methodology

Cell segmentation was performed using the CellPose nuclei model, and for cell deconvolution we used Cell2Location. 

For the human lung cancer samples, single-cell data from the Human Lung Cell Atlas  (Sikkema et al., 2023) and the Lung Cancer Atlas (Salcher et al., 2022) was used as reference-labeled. This dataset was filtered to include only Chromium-generated data.

For the mice datasets, the GSE107585  single-cell dataset served as reference for the kidney samples and the one presented in Kleshchevnikov et al. (2022) was used for the brain.

In the human lung cancer datasets, a pathologist annotated regions of interest corresponding to tumors, blood vessels, and alveolar regions. This annotation is used to showcase regional analysis capabilities.

Visualization

This tar also contains the WSI images corresponding to the different images under `prep/sample_name/wsi.tif` so the results can be visualized using hi-res images in TissUUmaps.

Reproducibility

To execute these analyses you need a machine with Docker support and the following requirements:

• 200GB RAM
• 1 GPU (CUDA enabled)
• 16 CPUs

To run the analysis, please execute:

git clone https://github.com/Sanofi-Public/spatialone-pipeline.git
cd spatialone-pipeline
./run_instructions_SO.sh --gpu