breast-bcspatial

Code used to produce the final figures and tables of the study "Spatial Transcriptomics in Breast Cancer Reveals Tumour Microenvironment-Driven Drug Responses and Clonal Therapeutic Heterogeneity".

Installation

Use the git clone command to create a local copy:

git clone https://github.com/cnio-bu/breast-bcspatial

How to run

Set up

You need to download additional data folders from Zenodo (DOI: 10.5281/zenodo.10638906) for the code to be functional:

• visium/: Contains processed spatial transcriptomics Seurat objects with deconvoluted spots, SCTransform-normalised counts, and clonal composition predicted with SCEVAN [1]. Please make sure to merge the contents of this folder with the data/visium/scalefactors folder that is provided in this repository.

• single-cell/: Contains raw and filtered merged single-cell RNA-seq Seurat objects with unnormalised counts used as a reference for spot deconvolution.

• beyondcell/sensitivity: Contains Beyondcell sensitivity objects with prediction scores for all drug response signatures in SSc breast.

• beyondcell/functional: Contains Beyondcell functional objects with enrichment scores for all functional signatures.

• benchmarking/deconvolution: Spot-wise deconvolution according to CARD [2] and the spatialDWLS method [3] implemented in Giotto, two deconvolution tools that were compared to RCTD [4], our final selection.

• benchmarking/normalisation: Beyondcell sensitivity and functional objects computed using Scanpy normalisation with log-transformation [5] or Giotto normalisation with log-transformation and z-scoring [6]. These two methods were compared to Seurat SCTransform [7], our final selection.

These objects were generated with the code available at cnio-bu/ST-preprocess.

References

1. De Falco A, Caruso F, Su X-D, Iavarone A, Ceccarelli M. A variational algorithm to detect the clonal copy number substructure of tumors from scRNA-seq data. Nat Commun. 2023;14(1):1074.
2. Ma Y, Zhou X. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat Biotechnol. 2022;40(9):1349-1359.
3. Dong R, Yuan GC. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 2021;22(1):145.
4. Cable DM, Murray E, Zou LS, Goeva A, Macosko EZ, Chen F, Irizarry RA. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol. 2022;40(4):517-526.
5. Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19(1):15.
6. Dries R, Zhu Q, Dong R, Eng CL, Li H, Liu K, Fu Y, Zhao T, Sarkar A, Bao F, George RE, Pierson N, Cai L, Yuan GC. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 2021;22(1):78.
7. Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 2019;20(1):296.

Execution

Once all data is downloaded, just run the code in the scripts/ folder in order. Then, run the code in the scripts/figures_and_tables folder to generate the final figures and tables. The reviewer's suggested analyses are stored in the scripts/reviewers folder.

Authors

• María José Jiménez-Santos

Support

If you have any questions, feel free to submit an issue.