---
title: 'SummarizedExperiment object'
package: bigPint
bibliography: bigPint.bib
output:
  BiocStyle::html_document:
    toc_float: true
    tidy: TRUE
vignette: >
  \usepackage[utf8]{inputenc}
  %\VignetteIndexEntry{"Data metrics object"}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
  %\VignettePackage{bigPint}
---

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

## SummarizedExperiment object

Researchers who prefer using the `SummarizedExperiment` class can feed their data and associated metrics into the `bigPint` methods using one `SummarizedExperiment` object (instead of both a `data` and `dataMetrics` object). We demonstrate here how to create a `SummarizedExperiment` object. In the remaining articles of the vignette, the example code uses the `data` and `dataMetrics` objects as example input. However, at the bottom of each vignette, we also include the corresponding example code that uses instead the `SummarizedExperiment` object to create the exact same plots.

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## Example: two groups

As was shown in the article [Data object](https://lindsayrutter.github.io/bigPint/articles/data.html), the `data` object called `soybean_ir_sub` contained 5,604 genes and two treatment groups, N and P [@soybeanIR]. We can create a `SummarizedExperiment` object that combines aspects of the `data` object and `dataMetrics` object. We start by reading in the `data` and `dataMetrics` objects (created from `edgeR` in the article [Data metrics object](https://lindsayrutter.github.io/bigPint/articles/dataMetrics.html).

```{r, eval=TRUE, include=TRUE, message=FALSE}
library(bigPint)
library(DelayedArray)
library(SummarizedExperiment)

data(soybean_ir_sub)
data = soybean_ir_sub
data(soybean_ir_sub_metrics)
dataMetrics = soybean_ir_sub_metrics
```

We then convert the `dataMetrics` object from a list of dataframes into one dataframe. 

```{r, eval=TRUE, include=TRUE, message=FALSE}
dMUnlist <- dataMetrics
dMUnlist[-1] <- lapply(dMUnlist[-1], transform, ID = NULL)
dMUnlist <- do.call(cbind, dMUnlist)
names(dMUnlist)[1] <- "ID"
```

Now, we can tranform our `data` object into a `DelayedMatrix` class object and then input both our `data` and `dataMetrics` objects combined into a `SummarizedExperiment` class object. We can verify that the `assay()` and `rowData()` methods work for accessing our `SummarizedExperiment` object.

```{r, eval=TRUE, include=TRUE, message=FALSE}
rownames(data) = data[,1]
data = data[,-1]
data <- DelayedArray(data)
se_soybean_ir_sub <- SummarizedExperiment(assays = data, rowData = dMUnlist)
assay(se_soybean_ir_sub)
rowData(se_soybean_ir_sub)
```

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## Example: three groups

Similarly, as was shown in the data page, the `data` object called `soybean_cn_sub` contained 7,332 genes and three treatment groups, S1, S2, and S3 [@brown2015developmental]. We can create a `SummarizedExperiment` object that combines aspects of the `data` object and `dataMetrics` object. We start by reading in the `data` and `dataMetrics` objects (created from `edgeR` in the article [Data metrics object](https://lindsayrutter.github.io/bigPint/articles/dataMetrics.html).

```{r, eval=TRUE, include=TRUE, message=FALSE}
data(soybean_cn_sub)
data = soybean_cn_sub
data(soybean_cn_sub_metrics)
dataMetrics = soybean_cn_sub_metrics
```

We then convert the `dataMetrics` object from a list of dataframes into one dataframe. 

```{r, eval=TRUE, include=TRUE, message=FALSE}
dMUnlist <- dataMetrics
dMUnlist[-1] <- lapply(dMUnlist[-1], transform, ID = NULL)
dMUnlist <- do.call(cbind, dMUnlist)
names(dMUnlist)[1] <- "ID"
```

Now, we can tranform our `data` object into a `DelayedMatrix` class object and then input both our `data` and `dataMetrics` objects combined into a `SummarizedExperiment` class object. We can verify that the `assay()` and `rowData()` methods work for accessing our `SummarizedExperiment` object.

```{r, eval=TRUE, include=TRUE, message=FALSE}
rownames(data) = data[,1]
data = data[,-1]
data <- DelayedArray(data)
se_soybean_cn_sub <- SummarizedExperiment(assays = data, rowData = dMUnlist)
assay(se_soybean_cn_sub)
rowData(se_soybean_cn_sub)
```

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## References
