Ovarian cancer subtype heterogeneity

How can I obtain cell metadata inclluding cell type and subtype annotation (Figure 4)?

Option 1: CSV files

Available as CSV files in the inst/extdata directory of this repository:

• T59 T76 T77 T89 T90

Option 2: R/Bioconductor SingleCellExperiment objects

Assuming familiarity with R and with the SingleCellExperiment container, all you would need to do is to carry out lines 290-310 here:

subtypeHeterogeneity/vignettes/singlecell.Rmd

Line 290 in 0f275d2

Read the pre-processed 10X data for all tumors as `list` of `SingleCellExperiment`s.

and access the colData of the obtained SingleCellExperiments which includes, among other things, the cell type annotation.

For example here for Tumor T59:

> colData(sces[["T59"]])[,c("Barcode", "subtype", "celltype")]
DataFrame with 13040 rows and 3 columns
                 Barcode     subtype    celltype
             <character> <character> <character>
1     AAACCTGAGCTGCCCA-1         DIF         MYE
2     AAACCTGAGTCATCCA-1         DIF         MYE
3     AAACCTGCAAGCCCAC-1         IMR         MYE
4     AAACCTGCAAGCGCTC-1         DIF         EPI
5     AAACCTGCACGTAAGG-1         DIF         EPI
...                  ...         ...         ...
13036 TTTGTCATCCCTTGCA-1         DIF         MYE
13037 TTTGTCATCCGTACAA-1         DIF         MYE
13038 TTTGTCATCCTCCTAG-1         DIF         EPI
13039 TTTGTCATCGGTCTAA-1         DIF         EPI
13040 TTTGTCATCTAACTCT-1         DIF         MYE

Overview

This repository contains code and analysis scripts that were used for conducting analysis and producing the figures of the publication:

Ludwig Geistlinger, Sehyun Oh, Marcel Ramos, Lucas Schiffer, Rebecca S. LaRue, Christine M. Henzler, Sarah A. Munro, Claire Daughters, Andrew C. Nelson, Boris J. Winterhoff, Zenas Chang, Shobhana Talukdar, Mihir Shetty, Sally A. Mullany, Martin Morgan, Giovanni Parmigiani, Michael J. Birrer, Li-Xuan Qin, Markus Riester, Timothy K. Starr and Levi Waldron. Multi-omic analysis of subtype evolution and heterogeneity in high-grade serous ovarian carcinoma. Cancer Research, 2020. doi: 10.1158/0008-5472.CAN-20-0521.

Analysis as documented in this repository was carried out using R-4.0.2 and Bioconductor-3.11.

Installation:

Option 1: Docker

Docker image that comes with all dependencies already installed

Option 2: Local installation

Assuming R and Bioconductor are installed:

BiocManager::install("waldronlab/subtypeHeterogeneity",
                     dependencies = TRUE,
                     build_vignettes = TRUE)

Absolute somatic copy number alteration analysis (Figures 2 and 3)

The code for reproducing Figures 2 and 3 can be found in the vignettes/subtypeHeterogeneity.Rmd file.

To view the fully rendered HTML document containing literate programming output see either here, or from within your local R installation:

browseVignettes("subtypeHeterogeneity")

and select the Absolute SCNA analysis HTML vignette.

10x Genomics single-cell RNA-seq analysis (Figures 4 and 5)

The code for reproducing Figures 4 and 5 can be found in the vignettes/singlecell.Rmd file.

To view the fully rendered HTML document containing literate programming output see either here, or from within your local R installation:

browseVignettes("subtypeHeterogeneity")

and select the Single cell analysis HTML vignette.