README.md
In francescojm/CELLector: Genomics guided selection of cancer cell lines
CELLector: Genomics Guided Selection of Cancer in vitro models
News: v2.0.0
From version 2.0.0 CELLector introduces a partitioned search space that is derived from the hierarchical search space implemented in the previous version. In particular, the hierarchical version identifies K recursive co-occurent signature rules. In the partitioned version, these are additionally converted into a K+1 non-overlapping groups, covering the entire sample space.
v1.2.0
Najgebauer, H., Yang, M., Francies, H., Pacini, C., Stronach, E. A., Garnett, M. J., Saez-Rodriguez, J., & Iorio, F. CELLector: Genomics Guided Selection of Cancer in vitro Models. http://doi.org/10.1101/275032
2020-03-03
CELLector is a computational tool for selecting the most clinically relevant cancer cell lines to be included in a new in-vitro study (or to be considered in a retrospective study), in a patient-genomic guided fashion.
CELLector combines methods from graph theory and market basket analysis; it leverages tumour genomics data to explore, rank, and select optimal cell line models in a user-friendly way, through the CELLector Rshiny App. This enables making appropriate and informed choices about model inclusion/exclusion in retrospective analyses, future studies and it makes possible bridging cancer patient genomics with public available databases froom cell line based functional/pharmacogenomic screens, such as CRISPR-cas9 dependency datasets and large-scale in-vitro drug screens.
Furthermore, CELLector includes interface functions to synchronise built-in cell line annotations and genomics data to their latest versions from the Cell Model Passports resource. Through this interface, bioinformaticians can quickly generate binary genomic event matrices (BEMs) accounting for hundreds of cancer cell lines, which can be used in systematic statistical inferences, associating patient-defined cell line subgroups with drug-response/gene-essentiality, for example through GDSC tools.
Additionally, CELLector allows the selection of models within user-defined contexts, for example, by focusing on genomic alterations occurring in biological pathways of interest or considering only predetermined sub-cohorts of cancer patients.
Finally, CELLector identifies combinations of molecular alterations underlying disease subtypes currently lacking representative cell lines, providing guidance for the future development of new cancer models.
License: GNU GPLv3
Running Modalities
CELLector can be used in three different modalities:
- (i) as an R package (within R, code available at: https://github.com/francescojm/CELLector)
Package vignette
-
(ii) as an online R shiny App (available at: https://ot-cellector.shinyapps.io/CELLector_App/)
-
(iii) as a local R shiny App (within Rstudio, code available at: https://github.com/francescojm/CELLector_App)
R package: quick start (interactive vignette)
http://rpubs.com/francescojm/CELLector
R package: Reference manual
https://github.com/francescojm/CELLector/blob/master/CELLector.pdf
francescojm/CELLector documentation built on Sept. 3, 2024, 8:23 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
CELLector: Genomics Guided Selection of Cancer in vitro models
News: v2.0.0
From version 2.0.0 CELLector introduces a partitioned search space that is derived from the hierarchical search space implemented in the previous version. In particular, the hierarchical version identifies K recursive co-occurent signature rules. In the partitioned version, these are additionally converted into a K+1 non-overlapping groups, covering the entire sample space.
v1.2.0
Najgebauer, H., Yang, M., Francies, H., Pacini, C., Stronach, E. A., Garnett, M. J., Saez-Rodriguez, J., & Iorio, F. CELLector: Genomics Guided Selection of Cancer in vitro Models. http://doi.org/10.1101/275032
2020-03-03
CELLector is a computational tool for selecting the most clinically relevant cancer cell lines to be included in a new in-vitro study (or to be considered in a retrospective study), in a patient-genomic guided fashion.
CELLector combines methods from graph theory and market basket analysis; it leverages tumour genomics data to explore, rank, and select optimal cell line models in a user-friendly way, through the CELLector Rshiny App. This enables making appropriate and informed choices about model inclusion/exclusion in retrospective analyses, future studies and it makes possible bridging cancer patient genomics with public available databases froom cell line based functional/pharmacogenomic screens, such as CRISPR-cas9 dependency datasets and large-scale in-vitro drug screens.
Furthermore, CELLector includes interface functions to synchronise built-in cell line annotations and genomics data to their latest versions from the Cell Model Passports resource. Through this interface, bioinformaticians can quickly generate binary genomic event matrices (BEMs) accounting for hundreds of cancer cell lines, which can be used in systematic statistical inferences, associating patient-defined cell line subgroups with drug-response/gene-essentiality, for example through GDSC tools.
Additionally, CELLector allows the selection of models within user-defined contexts, for example, by focusing on genomic alterations occurring in biological pathways of interest or considering only predetermined sub-cohorts of cancer patients.
Finally, CELLector identifies combinations of molecular alterations underlying disease subtypes currently lacking representative cell lines, providing guidance for the future development of new cancer models.
License: GNU GPLv3
Running Modalities
CELLector can be used in three different modalities: - (i) as an R package (within R, code available at: https://github.com/francescojm/CELLector) Package vignette
-
(ii) as an online R shiny App (available at: https://ot-cellector.shinyapps.io/CELLector_App/)
-
(iii) as a local R shiny App (within Rstudio, code available at: https://github.com/francescojm/CELLector_App)
R package: quick start (interactive vignette)
http://rpubs.com/francescojm/CELLector
R package: Reference manual
https://github.com/francescojm/CELLector/blob/master/CELLector.pdf
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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