scsurv 0.1.0

a deep generative model for single-cell survival analysis

scSurv (a deep generative model for single-cell survival analysis)

scSurv is a novel method for single-cell survival analysis.

Teppei Shimamura's lab, Institute of Science Tokyo at Tokyo and Nagoya University at Nagoya

Yasuhiro Kojima's lab, National Cancer Center Research Institute at Tokyo

Developed by Chikara Mizukoshi

Instalation

You can use the latest development version from GitHub.

!git clone https://github.com/3254c/scSurv.git

Dependencies

Python >= 3.8.16

torch >= 1.13.1

lifelines >= 0.27.8

scanpy >= 1.9.5

pandas >= 1.5.3

numpy >= 1.23.5

matplotlib >= 3.7.2

scipy >= 1.10.1

Usage

You need to prepare reference scRNA-seq data and bulk RNA-seq data linked to clinical outcome. For instance, TCGA bulk RNA-seq data is available from the Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/). For examples of reference scRNA-seq data, please refer to our paper.

In the tutorial, we demonstrate the usage of scSurv using simulated data. The bulk_adata.obs must contain the survival outcome data and the event indicators (censoring status). The scRNA-seq and bulk RNA-seq data must be raw counts. Please specify the batch_key parameter to indicate the sample origin annotation for each cell.

batch_key = 'orig.ident'
exp_name = 'tutolial_simulation.pt'
epoch = 10000

sc_adata, bulk_adata, model_params_dict, spatial_adata, scsurv_exp = workflow.run_scSurv(sc_adata, bulk_adata, exp_name, epoch, batch_key)