This repository contains vignettes (jupyter notebooks) and processed data to recapitulate the analyses and generate the figures of our preprint Semi-supervised Omics Factor Analysis (SOFA) disentangles known and latent sources of variation in multi-omic data: https://doi.org/10.1101/2024.10.10.617527.

Data

DepMap (pancan_depmap.h5mu)

Disclaimer

The DepMap data were generated and shared by the Broad Institute of Harvard and MIT and the Sanger Institute.

We reprocessed the data and packaged it in a single h5mu file for easier access and to reproduce analyses with Semi-supervised Omics Factor Analysis (SOFA).  Please see https://www.biorxiv.org/content/10.1101/2024.10.10.617527v3 for more details on how the data was processed and analysed.

Data Usage Policy

The Broad Institute publishes its data under the Terms and Conditions linked here.

The Sanger Institute publishes its data under the Terms and Conditions linked here.

The DepMap data  are provided under Creative Commons Attribution 4.0 license.

Contact depmap@broadinstitute.org or depmap@sanger.ac.uk for more information

Please cite the following when using these data

• Drug response: Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849.e8 (2022). (https://figshare.com/articles/dataset/Pan-cancer_proteomic_map_of_949_human_cell_lines/19345397)
• Proteomics: Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849.e8 (2022). (https://figshare.com/articles/dataset/Pan-cancer_proteomic_map_of_949_human_cell_lines/19345397)

• RNA-Seq: Garcia-Alonso, L. et al. Transcription factor activities enhance markers of drug sensitivity in cancer. Cancer Res. 78, 769–780 (2018). (https://cellmodelpassports.sanger.ac.uk/downloads)
• Methylation: Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016). (https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources/Home.html)
• Mutation: Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016). (https://cellmodelpassports.sanger.ac.uk/downloads)
• CRISPR-Cas9: Pacini, C. et al. Integrated cross-study datasets of genetic dependencies in cancer. Nat. Commun. 12, 1661 (2021). (https://score.depmap.sanger.ac.uk/downloads)
• Metadata for all cell lines was obtained from:
  • Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849.e8 (2022). 
  • Pacini, C. et al. Integrated cross-study datasets of genetic dependencies in cancer. Nat. Commun. 12, 1661 (2021).

TCGA (tcga_gyn_data.h5mu)

Disclaimer

The TCGA data were generated and shared by the The Cancer Genome Atlas project. It contains transcriptomics, proteomics, miRNA, Methylation and Survival data from 5 gynecologic cancers (brca, ov, cesc, ucs, ucec).

We reprocessed the data and packaged it in a single h5mu file for easier access and to reproduce analyses with Semi-supervised Omics Factor Analysis (SOFA).  Please see https://www.biorxiv.org/content/10.1101/2024.10.10.617527v3 for more details on how the data was processed and analysed.

Please cite the following when using these data

• CESC: Integrated genomic and molecular characterization of cervical cancer. The Cancer Genome Atlas Research Network. Nature volume 543, pages 378–384 (2017)
• BRCA: 
  • Comprehensive molecular portraits of human breast tumours. The Cancer Genome Atlas Network. Nature volume 490, pages 61–70 (2012)
  • Ciriello, G., Gatza, M.L., Beck, A.H., Wilkerson, M.D., Rhie, S.K., Pastore, A., Zhang, H., McLellan, M., Yau, C., Kandoth, C. and Bowlby, R., 2015. Comprehensive molecular portraits of invasive lobular breast cancer. Cell, 163(2), pp.506-519.
• UCS: Cherniack, A.D., Shen, H., Walter, V., Stewart, C., Murray, B.A., Bowlby, R., Hu, X., Ling, S., Soslow, R.A., Broaddus, R.R. and Zuna, R.E., 2017. Integrated molecular characterization of uterine carcinosarcoma. Cancer cell, 31(3), pp.411-423.
• UCEC: Integrated genomic characterization of endometrial carcinoma. Douglas A. Levine & The Cancer Genome Atlas Research Network. Nature volume 497, pages 67–73 (2013)
• OV: Integrated genomic analyses of ovarian carcinoma. The Cancer Genome Atlas Research Network. Nature volume 474, pages 609–615 (2011)

Single-cell human cortex multiome data (atac.h5ad, rna.h5ad)

This data set contains single-cell RNA-Seq and ATAC-Seq human cerebral cortex at six different developmental stages. The data was generated by Zhu et al. 2023.

Please cite the following when using these data

Zhu, K. et al. Multi-omic profiling of the developing human cerebral cortex at the single-cell level. Sci Adv 9, eadg3754 (2023).

Heart failure

This directory contains results from multiple MOFA and SOFA runs with single-cell RNA-Seq data of seven cell types from chronic human heart failure cases (HF) and controls (NF), along with covariates including age, sex, and heart failure status, a binary variable indicating patients with any heart disease etiology that required heart transplantation. The data was taken from 4 different publications (see below). See https://doi.org/10.7554/eLife.93161 and https://doi.org/10.1101/2024.11.04.621815 for more information how the data was processed.

Please cite the following when using these data

• Ramirez Flores, R. O., Lanzer, J. D., Dimitrov, D., Velten, B. & Saez-Rodriguez, J. Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease. Elife 12, (2023).
• Lanzer, J. D., Ramirez Flores, R. O., Liñares Blanco, J. & Saez-Rodriguez, J. A cross-study transcriptional patient map of heart failure defines conserved multicellular coordination in cardiac remodeling. bioRxiv (2024) doi:10.1101/2024.11.04.621815.
• Chaffin, M. et al. Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy. Nature 608, 174–180 (2022).
• Koenig, A. L. et al. Single-cell transcriptomics reveals cell-type-specific diversification in human heart failure. Nat. Cardiovasc. Res. 1, 263–280 (2022).
• Reichart, D. et al. Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies. Science 377, eabo1984 (2022).
• Simonson, B. et al. Single-nucleus RNA sequencing in ischemic cardiomyopathy reveals common transcriptional profile underlying end-stage heart failure. Cell Rep. 42, 112086 (2023).