---
title: "Processing and Analyzing RNA-Seq Data with `biotmle`"
author: "[Nima Hejazi](https://nimahejazi.org)"
date: "`r Sys.Date()`"
output:
  BiocStyle::html_document
bibliography: ../inst/REFERENCES.bib
vignette: >
  %\VignetteIndexEntry{Processing and Analyzing RNA-Seq Data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, message=FALSE, warning=FALSE}
library(dplyr)
library(biotmle)
library(biotmleData)
suppressMessages(library(SummarizedExperiment))
"%ni%" = Negate("%in%")
```

## Introduction

Here, we briefly work through how to use the `biotmle` package with data
generated by next-generation sequencing technologies, which, in contrast to
microarray technologies, produce measurements in the form of discrete counts.

---

## Simulation: Data Structure

```{r simNGS}
set.seed(6423709)
n <- 50
g <- 2500
cases_pois <- 50
controls_pois <- 10

ngs_cases <- as.data.frame(matrix(replicate(n, rpois(g, cases_pois)), g))
ngs_controls <- as.data.frame(matrix(replicate(n, rpois(g, controls_pois)), g))

ngs_data <- as.data.frame(cbind(ngs_cases, ngs_controls))
exp_var <- c(rep(1, n), rep(0, n))
batch <- rep(1:2, n)
covar <- rep(1, n * 2)
design <- as.data.frame(cbind(exp_var, batch, covar))

head(ngs_data[, 1:7])
```

---

## Processing: Data Transformation

```{r data_proc}
se <- SummarizedExperiment(assays = list(counts = DataFrame(ngs_data)),
                           colData = DataFrame(design))
se
```

---

## Analysis: Assessing the Effect of Exposure

```{r biomarkertmle, eval=FALSE}
rnaseqTMLEout <- biomarkertmle(se = se,
                               varInt = 1,
                               ngscounts = TRUE,
                               parallel = TRUE,
                               family = "gaussian",
                               g_lib = c("SL.mean", "SL.glm",
                                         "SL.randomForest"),
                               Q_lib = c("SL.mean", "SL.glm",
                                         "SL.randomForest", "SL.nnet")
                              )
head(eif(rnaseqTMLEout)$E[, seq_len(6)])
```

```{r load_biomarkerTMLE_result, echo=FALSE}
data(rnaseqtmleOut)
head(eif(rnaseqTMLEout)$E[, seq_len(6)])
```

```{r modtest_ic}
limmaTMLEout <- modtest_ic(biotmle = rnaseqTMLEout)
head(toptable(limmaTMLEout))
```

---

## Results: Data Visualization

```{r pval_hist_limma_adjp}
plot(x = limmaTMLEout, type = "pvals_adj")
```

```{r pval_hist_limma_rawp}
plot(x = limmaTMLEout, type = "pvals_raw")
```

```{r heatmap_limma_results}
varInt_index <- which(names(colData(se)) %in% "exp_var")
designVar <- as.data.frame(colData(se))[, varInt_index]
design <- as.numeric(designVar == max(designVar))

heatmap_ic(x = limmaTMLEout,
           row.dendrogram = TRUE,
           clustering.method = "hierarchical",
           design = design, FDRcutoff = 1.0, top = 10)
```

```{r volcano_plot_limma_results}
volcano_ic(biotmle = limmaTMLEout)
```

---

## Session Information

```{r sessionInfo, echo=FALSE}
sessionInfo()
```