coderdata 0.1.29

A package to download, load, and process multiple benchmark multi-omic drug response datasets

Cancer Omics Drug Experiment Response Dataset

There is a recent explosion of deep learning algorithms that to tackle the computational problem of predicting drug treatment outcome from baseline molecular measurements. To support this,we have built a benchmark dataset that harmonizes diverse datasets to better assess algorithm performance.

This package collects diverse sets of paired molecular datasets with corresponding drug sensitivity data. All data here is reprocessed and standardized so it can be easily used as a benchmark dataset for the This repository leverages existing datasets to collect the data required for deep learning model development. Since each deep learning model requires distinct data capabilities, the goal of this repository is to collect and format all data into a schema that can be leveraged for existing models.

The goal of this repository is two-fold: First, it aims to collate and standardize the data for the broader community. This requires running a series of scripts to build and append to a standardized data model. Second, it has a series of scripts that pull from the data model to create model-specific data files that can be run by the data infrastructure.

coderdata Data Model

The coderdata schema is maintained in LinkML and can be udpated via a commit to the repository. For more details, please see the schema description.

Building the data package

We have created a build script that executes each step of the build process to enable the creation of a local folder with all the requisite folders.

The build requires Python as well as Docker to be installed. To access the data on Synapse (MPNST, BeatAML proteomics), you will need to register for a synapse account and then request access to the CoderData Build Team. Then you will need to create a personal authentication token with Download access and then set the SYNAPSE_AUTH_TOKEN environment variable to that token.

To build the docker images and run them, simply run (though this will take a while!):

python build/build_all.py --all

To only build the docker files:

python build/build_all.py --docker

Then to build the reference files (after dockers have been built):

python build/build_all.py --samples
python build/build_all.py --drugs

Once the sample files have been created, we can collect the omics measurements:

python build/build_all.py --omics

Once the drugs file and samples have been created, we can refit the curves:

python build/build_all.py --exp

Note: this will not build the python package, just generate the data!

Data Source Reference List

Dataset	Data Source	Resource	Authors	AACR Reference Number
DepMap / Sanger	PharmacoGx - CCLE	The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity	Jordi Barretina et al.	1
DepMap / Sanger	PharmacoGx - gCSI	Reproducible pharmacogenomic profiling of cancer cell line panels	Peter M Haverty et al.	2
DepMap / Sanger	PharmacoGx - gCSI	A comprehensive transcriptional portrait of human cancer cell lines	Christiaan Klijn et al.	3
DepMap / Sanger	PharmacoGx - GDSC	Systematic identification of genomic markers of drug sensitivity in cancer cells	Mathew J Garnett et al.	4
DepMap / Sanger	PharmacoGx - GDSC	A Landscape of Pharmacogenomic Interactions in Cancer	Francesco Iorio et al.	5
DepMap / Sanger	PharmacoGx - GDSC	Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells	Wanjuan Yang et al.	6
DepMap / Sanger	PharmacoGx - PRISM	Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling	Steven M Corsello et al.	7
DepMap / Sanger	PharmacoGx - PRISM	High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines	Channing Yu et al.	8
DepMap / Sanger	PharmacoGx - CTRP	Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset	Brinton Seashore-Ludlow et al.	9
DepMap / Sanger	PharmacoGx - FIMM	Consistency in drug response profiling	John Patrick Mpindi et al.	10
DepMap / Sanger	PharmacoGx - FIMM	Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia	Tea Pemovska et al.	11
DepMap / Sanger	PharmacoGx - NCI60	The NCI60 human tumour cell line anticancer drug screen	Robert H. Shoemaker	12
DepMap / Sanger	PharmacoGx - CTRPv2	Correlating chemical sensitivity and basal gene expression reveals mechanism of action	Rees et al.	13
DepMap / Sanger	PharmacoGx - CTRPv2	Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset	Seashore-Ludlow et al.	14
DepMap / Sanger	PharmacoGx - CTRPv2	An Interactive Resource to Identify Cancer Genetic and Lineage Dependencies Targeted by Small Molecules	Bodycombe Basu et al.	15
DepMap / Sanger	COSMIC	COSMIC: a curated database of somatic variants and clinical data for cancer	Zbyslaw Sondka et al.	16
DepMap / Sanger	Cellosaurus	The Cellosaurus, a Cell-Line Knowledge Resource	Amos Bairoch	17
DepMap / Sanger	Cancer Cell Line Encyclopedia	Quantitative Proteomics of the Cancer Cell Line Encyclopedia	David P Nusinow et al	18
DepMap / Sanger	Cancer Cell Line Encyclopedia	The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity	Jordi Barretina	19
CPTAC	Clinical Proteomic Tumor Analysis Consortium	Simplified and Unified Access to Cancer Proteogenomic Data	Caleb M Lindgren et al.	20
HCMI	NCI Genomic Data Commons	Human Cancer Models Initiative	-	21
BeatAML	NCI Genomic Data Commons	Integrative analysis of drug response and clinical outcome in acute myeloid leukemia	Daniel Bottomly et al.	22
BeatAML	NCI Proteomic Data Commons	Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia	James Pino et al.	23
MPNST	NF Data Portal	Chromosome 8 gain is associated with high-grade transformation in MPNST	David P Nusinow et al.	24