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PANcancer invasiveness analysis using consensus frameworks of RGBM + FGSEA, RGBM + Viper, ARACNE + FGSEA and ARACNE + Viper

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In the scripts folder, just run each R script after installing all the required R packages to obtain each result figure.

Each result figure will appear in Results/Revised_Figures/Revised_Figure_(v2)_i.pdf , where i represents figure number.

Text results are provided in Results/Revised_Text_Results/ folder. Similarly, figures are provided in Results/Revised_Figures/ folder.

The data used for all the experiments is also available at: Mall, Raghvendra (2020), “Transcriptomic Dataset for Network based identification of key Master Regulators for Immunologic Constant of Rejection”, Mendeley Data, V3, doi: 10.17632/d9ffb7kkzt.3

In the Data/Others/ folder, please unzip the me_net_full.Rdata.gz file.

In the Data/PRECOG/ folder, please put the es_list1.rds and es_list2.rds files from Mendeley Data for performing the validation on PRECOG repository.

Run the misc_figures.R in scripts/ folder for validation results on the PRECOG repository.

Run the INV_Consensus_Classification.R followed by High_Medium_Low_INV_classification.R in scripts/ folder to get the INV-High, INV-Medium and INV-Low labels for each cancer sample per cancer type.

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PANcancer invasiveness analysis using consensus frameworks of RGBM + FGSEA, RGBM + Viper, ARACNE + FGSEA and ARACNE + Viper

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