Combine quantified LC/MS nicotine metabolite data with phase survey self-reported data, from specified cohort.

"t_allSamplesMetabolites" for curated nicotine metabolite dataset "t_allSamples_postExclusions" as the master (all variables all samples dataframe, post-QC, after specific exclusions) "clean_df" as the finalized, shared dataset, 18 variables, n=816 (inc. 28 controls)

Data dictionaries (Supplementary File 1) "Clean" variable consensus/logic UML (Supplementary File 2)

1. v6RawData_combined.csv : combined experimental LC-MS/MS calculated and QC'd data in [ng/mL] serum concentrations for: , Cotinine, 3-hyroxy-Cotinine, Nicotine, CO, NO, Nornicotine (in this order)
2. phase2.xlsx : cohort survey results with associated data dictionary for interpretation
3. smoking.xlsx : dataset with relevant smoking-related phenotypes not included in file 2
4. phase1.xlsx : phase I survey results with associated data dictionary for gender, smoking histotry, height, weight
5. shs677_elig.sas7bdat : age at phase 1
6. AlleleActivityScoring.xlsx : CYP2A6 haplotype variant activity score reference table
7. pypgx_cyp2a6_cs_20240524.xlsx : @CarissaSherman's PyPGx output dataset for SHS ph2 cohort

1. A list of all combined results for all 824 samples; t_allSamples_postExclusions, file = "SHSph2_fullDF_05152024CRM.csv"
2. A "clean" dataset with 18 variables for the final 816 participants to use for EWAS/GWAS; clean_df, file = "SHSph2_NMRwSurveyDF_clean05152024CRM.csv"
3. A list of just sampleIDs of final 816 participants; clean_df$idNo, file = "SHSph2_IDs_clean05152024CRM.csv"

• I'm bringing in calculated metabolic data using Kaja's data output from Teddy's script referencing Laura's library, and am assuming reported values pass QC

Calculated metabolite concentration data > Dataframe curation Script > Data Cleaning Script

Raw experimental LC-MS/MS data > metabolite standard curve generation > concentation back-calculations

Data Analysis Script > Figures & Tables Script

1. write in a check that there's not data being overwritten for that sample's concentration variable (ie reruns in the same file), handle this how?
2. generate a NMR assay rerun sample list and connect data back in
3. generate a sample list for phase 3 sample IDs