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Using singscore to predict mutations in AML from transcriptomic signatures

Bioconductor version: Release (3.19)

This workflow package shows how transcriptomic signatures can be used to infer phenotypes. The workflow begins by showing how the TCGA AML transcriptomic data can be downloaded and processed using the TCGAbiolinks packages. It then shows how samples can be scored using the singscore package and signatures from the MSigDB. Finally, the predictive capacity of scores in the context of predicting a specific mutation in AML is shown.The workflow exhibits the interplay of Bioconductor packages to achieve a gene-set level analysis.

Author: Dharmesh D. Bhuva [aut, cre] , Momeneh Foroutan [aut] , Yi Xie [aut] , Ruqian Lyu [aut], Malvika Kharbanda [aut] , Joseph Cursons [aut] , Melissa J. Davis [aut]

Maintainer: Dharmesh D. Bhuva <bhuva.d at>

Citation (from within R, enter citation("SingscoreAMLMutations")):


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Using singscore to predict mutations in AML from transcriptomic signatures HTML R Script
Using singscore to predict mutations in AML from transcriptomic signatures (Chinese version) HTML R Script


biocViews GeneExpressionWorkflow, GenomicVariantsWorkflow, ImmunoOncologyWorkflow, Workflow
Version 1.20.0
License Artistic-2.0
Depends R (>= 4.1.0)
Imports dcanr, edgeR, ggplot2, gridExtra, GSEABase, mclust,, plyr, reshape2, rtracklayer, singscore, SummarizedExperiment, TCGAbiolinks, BiocFileCache
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Source Package SingscoreAMLMutations_1.20.0.tar.gz
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