Metlinkr files

inst/extdata/README.md

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+# extdata File Descriptions
+
+This directory contains example data files to aid in learning how to use RaMP
+
+## metLinkR
+These files were used for benchmarking the metLinkR package in the [original publication](https://pmc.ncbi.nlm.nih.gov/articles/PMC12053952/). See the [github repository](https://github.com/ncats/MetLinkR) for more information, including a vignette.
+
+The "manifest" file in this directory (HarmInputFiles.csv) is properly formatted for MetLinkR to analyze the input metabolomics data sets (the other 5 csvs in the directory). MetLinkR recognizes common names, HMDB IDs, KEGG IDs, LIPID MAPS IDs, PubChem IDs, and ChEBI IDs for human metabolites.
+
+## CCLE data
+
+The Cancer Cell Line Encyclopedia (CCLE) is an initiative from the Broad Institute to perform large-scale characterize of ~1000 cancer cell lines. Our example data is a list of analyte IDs which were found to be significantly different between cancer types in a subset of the data. 
+
+Herein, we took a subset of data and focused on primary tumors of: 
+
+- Acute Myeloid Leukaemia within the HAEMATOPOIETIC AND LYMPHOID TISSUE (n = 30)
+- Glioma within the CENTRAL NERVOUS SYSTEM (n = 45)
+- FIBROBLAST (n = 10)
+- OESOPHAGUS (n = 25)
+
+within 3 data files: 
+- RNAseq: CCLE_RNAseq_rsem_genes_tpm_20180929.csv
+- Proteomics: CCLE_RPPA_20181003.csv
+- Metablomics: CCLE_metabolomics_20190502.csv
+
+We identified analytes differentiating cancer types by: 
+ 1) An ANOVA was performed across all 4 cancer types and p-values were FDR adjusted
+ 2) Tukey HSD (Honestly Significant Difference) was performed for each analyte
+     - A post hoc statistical test used after a significant ANOVA to determine which specific group means differ from each other
+ 3) Analytes which have a significant FDR adjusted ANOVA p-value 
+    AND 
+    significant p-adj between >4 comparisons in the Tukey's test were selected

inst/extdata/metLinkR/HarmInputFiles.csv

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+FileNames,ShortFileName,HMDB,Metabolite_Name,PubChem_CID,KEGG,LIPIDMAPS,chebi
+SamplefromCOMETS.csv,inputfile,HMDB_ID,metabolite_name,PUBCHEM,NA,NA,NA
+2019_Metabolon_Metadata.csv,VickyFile,HMDB,BIOCHEMICAL.NAME,PUBCHEM,KEGG,NA,NA
+Metabolon_Annotations_Serum_hmdbformatted.csv,JohnFile,HMDB,CHEMICAL_NAME,PUBCHEM,KEGG,NA,NA
+CSVModifiedBroadfilefromVicky20182019.csv,broadfileVicky,HMDB_ID,Metabolite,COMP_ID,NA,NA,NA
+Broad_2022Aug_annotations.csv,broadfileEwy,hmdbId,Metabolite,pubChemId,NA,NA,NA

inst/extdata/metLinkR/README.md

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+# Example files for MetLinkR analysis
+
+These files were used for benchmarking the metLinkR package in the [original publication](https://pmc.ncbi.nlm.nih.gov/articles/PMC12053952/). See the [github repository](https://github.com/ncats/MetLinkR) for more information, including a vignette.
+
+The "manifest" file in this directory (HarmInputFiles.csv) is properly formatted for MetLinkR to analyze the input metabolomics data sets (the other 5 csvs in the directory). MetLinkR recognizes common names, HMDB IDs, KEGG IDs, LIPID MAPS IDs, PubChem IDs, and ChEBI IDs for human metabolites.