facopy-package: facopy: feature-based association and gene-set enrichment for...
In facopy: Feature-based association and gene-set enrichment for copy number alteration analysis in cancer
Description
facopy is an R package for fine-tuned cancer CNA association modeling. Association is measured directly at the genomic features of interest and, in the case of genes, downstream gene-set enrichment analysis can be performed thanks to novel internal processing of the data. The software opens a way to systematically scrutinize the differences in CNA distribution across tumoral phenotypes, such as those that relate to tumor type, location and progression. Currently, the output format from 11 different methods that analyze data from whole-genome/exome sequencing and SNP microarrays, is supported. Multiple genomes, alteration types and variable types are also supported.
Details
Package: facopy
Type: Package
License: CC BY-NC 4.0
To analyze your data with facopy, first read copy number data through readCNData. Then, addVariables and addFeatures, and you are ready to start scrutinizing the data in multiple ways thanks to the many provided functions.
Author(s)
David Mosen-Ansorena
facopy documentation built on May 2, 2018, 2:30 a.m.
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Description
facopy is an R package for fine-tuned cancer CNA association modeling. Association is measured directly at the genomic features of interest and, in the case of genes, downstream gene-set enrichment analysis can be performed thanks to novel internal processing of the data. The software opens a way to systematically scrutinize the differences in CNA distribution across tumoral phenotypes, such as those that relate to tumor type, location and progression. Currently, the output format from 11 different methods that analyze data from whole-genome/exome sequencing and SNP microarrays, is supported. Multiple genomes, alteration types and variable types are also supported.
Details
| Package: | facopy |
| Type: | Package |
| License: | CC BY-NC 4.0 |
To analyze your data with facopy, first read copy number data through readCNData. Then, addVariables and addFeatures, and you are ready to start scrutinizing the data in multiple ways thanks to the many provided functions.
Author(s)
David Mosen-Ansorena
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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