Models:
Joseph Gordon
joseph-gordon/
multiomic_drift
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[f48632]: / zachs_rerun / zc_analysis_deltas.R

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#' @author Emily Yeo

#' @email emily.yeo@colorado.edu

#' @purpose Analysis for the Stanislawski Labm

#' @lab Stanislawski Lab

#' @affiliation University of Colorado Denver - Anschutz Medical, Dept of Biomedical Informatics & Personalized Medicine



###############################

###     Reading R data files    

###############################



# In[1]: Imports ----

rm(list = ls())

source("zc_functions.R") 

library(pacman)

p_load(tools, reticulate, viridis, tidyplots, patchwork, jsonlite, maps, ggvenn, caret, caretEnsemble, 

       readr, plyr, dplyr, tidyr, purrr, tibble, stringr, psych, randomForest, glmnet, xgboost, ggplot2, 

       reshape2, scales, gridExtra, plotly, sf, tidyverse)



###############################

###     Caret Analysis

###############################

# In[2] Load Data sets ----

data_dir <- "drift_fs/csv/all_omic_processed_data/deltas/"

omic_g_ra_outer <- read_csv(paste0(data_dir, "jan20_g_ra_all_omics_deltas_outer.csv"))

omic_g_ra_inner <- read_csv(paste0(data_dir, "jan20_g_ra_all_omics_deltas_inner.csv"))



# In[4] Main Analysis ----



# FIRST JUST WITH OUTER JOINED 

omic_g_ra <- omic_g_ra_outer



### Make BL , 6m and 12m dfs 

BL <- omic_g_ra %>%

  filter(grepl("BL$", SampleID)) %>%

  select(-matches("_12m$|_6m$"))



m6 <- omic_g_ra %>%

  filter(grepl("6m$", SampleID)) %>%

  select(-matches("_BL$|_12m$"))



m12 <- omic_g_ra %>%

  filter(grepl("12m$", SampleID)) %>%

  select(-matches("_BL$|_6m$"))



### Latent variables 

latent_variables_BL_6m <- c(

  "randomized_group", "score_std", "cohort_number", "sex", "race", "age", 

  "Glucose (6m-BL)", "HOMA-IR (6m-BL)", "Insulin (6m-BL)", 

  "HDL (6m-BL)", "LDL (6m-BL)", "Triglyceride lipid (6m-BL)",           

  "outcome_BMI_fnl_BL")



latent_variables_BL_12m <- c(

  "randomized_group", "score_std", "cohort_number", "sex", "race", "age", 

  "Glucose (12m-BL)", "HOMA-IR (12m-BL)", "Insulin (12m-BL)", 

  "HDL (12m-BL)", "LDL (12m-BL)", "Triglyceride lipid (12m-BL)",           

  "BMI (6m-BL)", "BMI (12m-BL)", 

  "Weight (6m-BL)", "Weight (12m-BL)")





### Process DFs 

imputed_BL_6m <- preprocess_data(BL_6m, 

                              latent_variables_BL_6m, 

                              "medianImpute")



imputed_BL_12m <- preprocess_data(BL_12m, 

                                 latent_variables_BL_12m, 

                                 "medianImpute")



# remove outcome_BMI_fnl_BL from the genus and species dataframes

tail(colnames(imputed_6m), 300)



imputed <- remove_columns(imputed_BL_6m, 

                          columns_to_remove = "subject_id", 

                          "SampleID")



set.seed(123)

train_control <- trainControl(method = "cv", number = 5, search = "grid")