Exploiting the DepMap cancer dependency data using the depmap R package

The `depmap` package facilitates access in the R environment to the data from the DepMap project, a multi-year collaborative effort by the Broad Institute and Wellcome Sanger Institute, mapping genetic and chemical dependencies and other molecular biological measurements of over 1700 cancer cell lines. The 'depmap' package formats this data to simply the use of popular R data analysis and visualizing tools such as 'dplyr' and 'ggplot2'. In addition, the 'depmap' package utilizes 'ExperimentHub', storing versions of the DepMap data accessible from the Cloud, which may be selectively downloaded, providing a reproducible research framework to support exploiting this data. This paper describes a workflow demonstrating how to access and visualize the DepMap data in R using this package.

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Faculty Opinions recommendation of TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725335383.793503769 ◽

2015 ◽

Author(s):

Zdeněk Valenta

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

R Package ◽

Cancer Cell Lines ◽

Drug Combinations ◽

Targeted Drug

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TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples

Bioinformatics ◽

10.1093/bioinformatics/btv067 ◽

2015 ◽

Vol 31 (11) ◽

pp. 1866-1868 ◽

Cited By ~ 10

Author(s):

Liye He ◽

Krister Wennerberg ◽

Tero Aittokallio ◽

Jing Tang

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

R Package ◽

Cancer Cell Lines ◽

Drug Combinations ◽

Targeted Drug

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CELLector: Genomics Guided Selection of Cancer in vitro Models

10.1101/275032 ◽

2018 ◽

Cited By ~ 3

Author(s):

Hanna Najgebauer ◽

Mi Yang ◽

Hayley E Francies ◽

Clare Pacini ◽

Euan A Stronach ◽

...

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

Cancer Cell Line ◽

R Package ◽

Cancer Cell Lines ◽

In Vitro Models ◽

Computational Method ◽

R Shiny ◽

Selection Of

The selection of appropriate cancer models is a key prerequisite for maximising translational potential and clinical relevance of in vitro oncology studies. We developed CELLector: a computational method (implemented in an open source R Shiny application and R package) allowing researchers to select the most relevant cancer cell lines in a patient-genomic guided fashion. CELLector leverages tumour genomics data to identify recurrent sub-types with associated genomic signatures. It then evaluates these signatures in cancer cell lines to rank them and prioritise their selection. This enables users to choose appropriate models for inclusion/exclusion in retrospective analyses and future studies. Moreover, this allows bridging data from cancer cell line screens to precisely defined sub-cohorts of primary tumours. Here, we demonstrate usefulness and applicability of our method through example use cases, showing how it can be used to prioritise the development of new in vitro models and to effectively unveil patient-derived multivariate prognostic and therapeutic markers.

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