Woelders et al. 2023: Supplementary data and analysis
Creators: name: Woelders, Tom
name: Skene, Debra
name: Hut, Roelof
Publisher: name: figshare
Year: 2023
Subjects: Proteomics and metabolomics
Bioinformatic methods development

description: <strong>Supplementary Datasets and Rscripts – Meta Information:</strong> <br> <em>Instructions for the R scripts to analyse the data presented in the Woelders et al. (2023) paper. </em> <br> <em>Input data:</em> Mel_Cort_Entrained_F.csv, Mel_Cort_Entrained_M.csv - <em>Melatonin and cortisol concentrations per decimal sampling time for females and males</em> Targeted_Entrained_F.csv, Targeted_Entrained_M.csv - <em>Targeted metabolite abundances (unnormalized) per decimal sampling time for females and males</em> Y_Entrained_F.csv, Y_Entrained_M.csv - <em>Circadian time and cartesian coordinates per decimal sampling time for females and males (ie Y-matrix)</em> DLMO_Entrained_F.csv, DLMO_Entrained_M.csv - <em>Estimated DLMO for females and males separately (for the two days in the lab).</em> <br> <em>R scripts *:</em> 1. Figure 1.R <em>Input:</em> Data/Y_Entrained_F.csv, Y_Entrained_M.csv, Mel_Cort_Entrained_F.csv, Mel_Cort_Entrained_M.csv, Targeted_Entrained_F.csv, Targeted_Entrained_M.csv <em>Output:</em> Figures/Figure 1(ABCD).eps <br> 2. PLSR_EntrainedFemales.R <em>Input:</em> Data/Y_Entrained_F.csv, Mel_Cort_Entrained_F.csv, Targeted_Entrained_F.csv <em>Output:</em> Model output/dataSubset**/Data_usedByPLSR_Females.csv <em>(the data as was used by</em> <em>the PLSR algorithm), </em> Model output/dataSubset**/coefficients_S_Females.csv <em>(the coefficients for the S model; mean, sd and significance), </em> Model output/dataSubset**/coefficients_C_Females.csv <em>(the coefficients for the C model; mean, sd and significance), </em> Model output/dataSubset**/PLSR_LOOData_Females.csv <em>(Y matrix and predictions per left out participant; s, c, angle and CT)</em> <br> 3. PLSR_EntrainedMales.R <em>Input:</em> Data/Y_Entrained_M.csv, Mel_Cort_Entrained_M.csv, Targeted_Entrained_M.csv <em>Output:</em> Model output/dataSubset**/Data_usedByPLSR_Males.csv, Model output/dataSubset**/coefficients_S_Males.csv, Model output/dataSubset**/coefficients_C_Males.csv, Model output/dataSubset**/PLSR_LOOData_Males.csv <br> 4. Figure 2(ABC).R <em>Input:</em> Model output/dataSubset**/PLSR_LOOData_Females.csv <em>Output:</em> Figures/dataSubset**/Figure 2(ABC).eps <br> 5. Figure 2(DEF).R <em>Input:</em> Model output/dataSubset**/PLSR_LOOData_Males.csv <em>Output:</em> Figures/dataSubset**/Figure 2(DEF).eps <br> 6. Figure 3(ABC)_Figure S2_S4_Stats_Females.R <em>Input:</em> Model output/dataSubset**/PLSR_LOOData_Females.csv <em>Output:</em> Figures/dataSubset**/Figure 3(ABC).eps, Figure S2.eps, Figure_S4_ROC_Females.png, Statistics/dataSubset**/stats_Females.xlsx <br> 7. Figure 3(DEF)_Figure S3_Stats_Males.R <em>Input:</em> Model output//dataSubset**/PLSR_LOOData_Males.csv <em>Output:</em> Figures/dataSubset**/Figure 3(DEF).eps, Figure S3.eps, Figure_S4_ROC_Males.png, Statistics/dataSubset**/stats_Males.xlsx <br> 8. DLMOStats.R <em>Input:</em> DLMO_Entrained_M.csv and DLMO_Entrained_F.csv <em>Output:</em> Statistics/DLMO_stats.xlsx <br> <em>Model output folder contents (One folder for each subset of the data)</em> <em>For all models, the beta coefficients; mean, sd and significance are produced. For convenience (not used by any script), the full dataset as used by the PLSR procedure (i.e. X and Y matrices). Note that the folders for the one-only subsets (Only Mel or Cort), the single feature is repeated once. This is because the pls regression does not run with an X matrix of only one column. PLSR can handle this artificial colinearity (partly the reason why we are using the method). </em> <br> dataSubset**/Data_usedByPLSR_Females.csv, Data_usedByPLSR_Males.csv dataSubset**/coefficients_C_Females.csv, coefficients_C_Males.csv, coefficients_S_Females.csv, coefficients_S_Males.csv dataSubset**/PLSR_LOOData_Females.csv, PLSR_LOOData_Males.csv Y matrix and predictions per left out participant; s, c, angle and CT. <br> <em>Statistics folder contents (One folder for each subset of the data):</em> <em>These excel documents contain the MdAE values in the first sheet and the F-table of the statistical test used in the second sheet.</em> <br> dataSubset**/stats_Females.xlsx and stats_Males.xsls. <br> <br> <em>*</em> <em>Scripts 4-7 use the outputs from scripts 2 and 3 so it is wise to just run the scripts in order. To run them, simply change the working directory on top of each script accordingly and make sure the required libraries are installed</em> <em>** </em> <em>Scripts 2-7 are run for all combinations of melatonin, cortisol and metabolites included (7). Therefore, the figures and stats that are presented in the manuscript are also created for different subsets of the data to get a feeling of how melatonin, cortisol and metabolites (and any combination of these 3) perform</em>