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pancancer-metabolomics
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pancancer metabolomics
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# pancancer-metabolomics This repository contains the code to reproduce the findings and figures presented in [Benedetti, Liu, Tang, et al., _A Multimodal Atlas of Tumor Metabolism Reveals the Architecture of Gene-Metabolite Co-regulation_, bioRxiv (2022)](). ## Data Please source the script **_0_DownloadData.R_** to automatically download the necessary data from Zenodo ([DOI: 10.5281/zenodo.7150252](https://zenodo.org/record/7348648#.Y30rsezMKEs)). The generated data structure is as follows: * "_metabolomics_original_" contains the unprocessed metabolomics data * "_metabolomics_processed_" contains the preprocessed metabolomics data * "_transcriptomics_processed_" contains the preprocessed transcriptomics data * "_TME_deconvolution_processed_" contains the immune deconvolution data * "_flow_sorted_ovarian_metabolomics_" contains the ovarian cancer data from Kilgour et al. (2021) * "_Metabolism_Immune.aDC_exIDO1_" contains the dendritic cell signature without IDO1 ## Code ### Data Preprocessing The preprocessed data can be generated by running the following scripts, in order: * **_1_PreprocessedMetabo.R_** * **_2_ImputeMetabo.R_** * **_3_FilterMetabo.R_** * **_4_FilterRNA.R_** The preprocessed data will be generated and stored in the "_results/preprocessed_data/_" folder. ### Data Analysis In order to generate the main results presented in the paper, please run the follwing scripts. #### Concordance Meta-Analysis The concordance meta-analysis results can be reproduced by running the following scripts, in order: * **_5_ConcordanceMetaAnalysis.R_** * **_6_ConcordancePathwayEnrichmentAnalysis.R_** Please note that script number 5 requires the output of scripts 1-4. Moreover, this script will take a long time to run on a regular laptop with 4 cores (>12h). Script number 6 will take >1h to run on a laptop. In order to make it easier for users to reproduce our findings without having to run these computationally expensive scripts, we are also providing a pre-computed version of the results generated by these two scripts that will be used to generate the corresponding figures in absence of the output of these scripts. These results are downloaded automatically when running the script **_0_DownloadData.R_** and stored in the _data_for_script_/ folder. #### Tumor Versus Normal Analysis The tumor vs normal analysis results can be reproduced by running the following script: * **_7_TumorVsNormalAnalysis.R_** #### Immune Score Analysis The ImmuneScore and immune cell populations results can be reproduced by running the following script: * **_8_ImmuneAnalysis.R_** This script will take roughly 30 mins to run. In order to make it easier for users to reproduce our findings without having to run this computationally expensive script, we are also providing a pre-computed version of the results generated by this script that will be used to generate the corresponding figures in absence of the output of this script. These results are downloaded automatically when running the script **_0_DownloadData.R_** and stored in the _data_for_script_/ folder. ### Figures In order to reproduce the paper figures, please run the following scripts, in order: * **_9_Figure1-2-4.R_** This script will generate the panels in Figure 1, 2, 4, as well as Supplementary Figure S1 and S4, and Supplementary Table S1 and S4. This script needs the output of scripts 1-4. * **_10_Figure3.R_** This script will generate the panels in Figure 3, as well as Supplementary Table S2 and S3. * **_11_Figure5-6.R_** This script will generate the panels in Figure 5 and 6, as well as Supplementary Figure S2 and Supplementary Table S5.
