User_test1.Rmd

---
title: "User test file - Simulation for omics biomarker cross over studies"
output:
  word_document:
    toc: yes
    toc_depth: '3'
  html_document:
    theme: spacelab
    mathjax: default
    code_folding: hide
    toc: yes
    toc_depth: 3
    number_sections: yes
    toc_float:
      collapsed: no
      smooth_scroll: no
  pdf_document:
    toc: yes
    toc_depth: '3'
date: "`r format(Sys.time(), '%d %B, %Y')`"
author: Juliane
editor_options:
  chunk_output_type: console
params:
  echo: yes
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  eval = TRUE, echo = params$echo, results = "asis",
  message = FALSE, warning = FALSE, error = TRUE,
  width = 120
)

```


```{r}
library(knitr)
library(openxlsx)
library(limma)
# this should be loaded last
library(tidyverse)

# change ggplot theme
theme_set(theme_bw())
```


```{r}

pheno_data <- data.frame(
  Sample = 1:48, 
  Seq = rep(c("AB","BA"), each = 24),
  Period = rep(c(0, 1, 2, 0, 1, 2), each = 8),
  Treatment = rep(c("BL", "A", "B", "BL", "B", "A"), each = 8),
  Subject = c(rep(1:8, 3), rep(9:16, 3))
  )

pheno_data %>% count(Seq, Treatment, Period)

```

```{r}
#pak::pak("git::https://code.roche.com/PMDA/packages/ComplexSim.git")
library(ComplexSim)
library(parallel)

ngene <- 10
ntime <- pheno_data$Period %>% unique() %>% length()
ntrt <- pheno_data$Seq %>% unique() %>% length()
nrep <- (pheno_data$Subject %>% unique() %>% length()) / ntrt

dat <- wrap_sim_DGEList(
  n = nrep, # subjects per Seq
  m = ntime,
  q = ntrt,
  N_gene = ngene,
  # overall shift?
  global_eff = rep(0, ngene),
  trt_eff = rep(1, ngene),
  trt_slope = rep(.5, ngene),
  inter = rep(.1, ngene),
  sigma = rep(1, ngene),
  mu = rep(10, ngene),
  # tau = NULL,
  # act.cor = NULL,
  # distribution = NULL,
  SingleGroup = FALSE
)



```


```{r}
data1 <- wrap_design(nrep, ntime, ntrt)

library(MASS)
norm_counts <- wrap_simulation(
      data = data1,
      beta0 = 0,
      beta1 = rep(1, ntrt),
      beta2 = rep(.5, ntime),
      lambda = matrix(data = 0.1, nrow = ntrt, ncol = ntime),
      sigma = 1,
      mu = c(0.5, 0.5),
      # tau = tau_genewise,
      # act.cor = act.cor_genewise,
      SingleGroup = FALSE) %>%
      dplyr::mutate(GENE = Exprs) %>%
      rownames_to_column(., var = "SampleID") %>%
      dplyr::select(-eij, -Exprs)

```


```{r}
sim_DGEList <- wrap_sim_DGEList(n=nrep, 
                                m=ntime, 
                                q=ntrt, 
                                N_gene = ngene, 
                                trt_eff = matrix(rep(c(1,2), ngene), ncol=ngene), 
                                SingleGroup=FALSE)
```