---
title: "Simulate_batch_scRNAseq"
author: "Almut Lütge"
date: "26 Februar 2019"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Simulate multibatch testset

Aim: Generate a multibatch testset as described in kBet. 

### Packages
```{r libraries}
suppressPackageStartupMessages({
    library(scater)
    library(MASS)
    library(here)
    library(CellMixS)
    library(scran)
})

set.seed(1234)
```

### Generate synthetic dataset 
Modification of the *create_testset_multibatch* function from the *kbet* package, to control for differences between batches [Buttner2019]. 
```{r sim dataset}

#function to simulate multibatch datasets  
# modified create_testset_multibatch function from kBet package.
# Add parameter "batch_var" to define percentage of mean expression levels, that are multiplied by a gamma distributed random variable with variance 1.
#Add "batch_var" to define batch size differences.

sim_multibatch_data <- function(n.genes = 1000, n.batch = 3, batch_var = 0.1, batch_diff = 0, n_sample = 500) 
{
    mu <- rbeta(n.genes - 1, 2, 5) * 100
    mu <- c(mean(mu), mu)
    b0 <- -1.5
    if(batch_diff == 0){
    samples <- c(n_sample, rep(n_sample, n.batch))
    }else{
      samples <- c(n_sample, rep(n_sample * batch_diff, n.batch))
    }
    mu.batch <- sapply(c(0, floor(n.genes * rep(batch_var, n.batch -1))), 
        function(x, mu) {
            c(rgamma(x, 1), rep(1, n.genes - x)) * mu
        }, mu)
    b2 <- apply(mu.batch, 2, function(x) {
        1/quantile(x, 0.5)
    })
    decay.prob2 <- apply(mu.batch, 2, function(mu, b0) {
        b1 <- 1/quantile(mu, 0.5)
        res <- 1/(1 + exp(-(b0 + b1 * mu)))
    }, b0)
    testset <- sapply(seq_len(n.genes), function(k, sample.size, 
        decay.prob, mu) {
        unlist(sapply(seq_len(n.batch), function(x, sample.size, 
            decay.prob, mu) {
            rnegbin(sample.size[x], mu = mu[k, x], 1) * rbinom(sample.size[x], 
                1, decay.prob[k, x])
        }, sample.size, decay.prob, mu))
    }, samples, decay.prob2, mu.batch)
    result <- list()
    result$data <- testset
    result$batch <- unlist(sapply(seq_len(n.batch), function(x, 
        y) {
        list(rep(x, y[x]))
    }, samples))
    return(result)
}


#generate batch_distributions
batch_var_list <- c(0, 0.2, 0.5)
batch_diff_list <- c(0.5)

any <- lapply(batch_diff_list, function(diff){
  batch_list <- lapply(batch_var_list, sim_multibatch_data, n.genes = 100, n.batch = 3, batch_diff = diff, n_sample = 300)
  names(batch_list) <- c("batch0", "batch20", "batch50")
  batch_list
})

```


### Create  list with SummarizedSingleCell Object 

```{r sce}
create_sce <- function(multibatch){
  #multibatch <- get(multibatch_nam)
    sce <- SingleCellExperiment(
    assays = list(counts = t(multibatch$data)),
    colData = as.data.frame(multibatch$batch)
)
    names(colData(sce)) <- "batch"
    colnames(sce) <-  sprintf("cell_%s",seq(1:ncol(sce)))
    sce <- runTSNE(sce, exprs_values = "counts")
    sce <- runPCA(sce, ncomponents = 30, exprs_values = "counts")
    #sce <- runUMAP(sce, exprs_values = "counts")
    sce
}


sim_list <- names(any[[1]])
sce_list <- lapply(c(1:length(any)), function(diff_list){
  batch_sce <- lapply(any[[diff_list]][sim_list], create_sce)
  names(batch_sce) <- sim_list
  batch_sce
})

names(sce_list) <- c("unbalanced50")


```

#### Plot simulation 

```{r vis}

batch_list <- names(sce_list[[1]])
  
lapply(batch_list, function(name){
    sce_name <- sce_list[["unbalanced50"]][[name]]
    visGroup(sce_name, "batch", dim_red = "TSNE") + ggtitle(paste0("unbalanced50_", name))
})
  
```

#### Integrate data with mnnCorrect 

Add a slot with integrated/corrected reduced dimensions using mnn as example for a data integration method. 
 
```{r mnn}
add_mnn <- function(batch_name){
  sce <- sce_list[["unbalanced50"]][[batch_name]]
  #split sce by batches
  sce_single <- list()
  colData(sce)$batch <- as.factor(colData(sce)$batch)
  for(i in levels(colData(sce)$batch)){
    sce_single[[i]] <- sce[, which(colData(sce)$batch %in% i)]
  }
  mnn_res <- fastMNN(sce_single[[1]], sce_single[[2]], sce_single[[3]], assay.type="counts")
  reducedDim(sce, "MNN") <- mnn_res$corrected
  sce
}
  
#sce_mnn <- lapply(batch_list, add_mnn)
#names(sce_mnn) <- batch_list
sce_list[["unbalanced50"]][["batch20"]] <- add_mnn("batch20")

```


#### Save data and SessionInfo
```{r save and session}
data_path <- here::here(file.path("inst", "extdata"))
sim_50 <- sce_list[["unbalanced50"]]
sim_50[["batch0"]]$batch <- as.factor(sim_50[["batch0"]]$batch)
sim_50[["batch50"]]$batch <- as.factor(sim_50[["batch50"]]$batch)
saveRDS(sim_50, file = file.path(data_path, "sim50.rds"))
sessionInfo()
```

