---
title: "Import Olink Proteomic Data"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Import Olink Proteomic Data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```


## Import Olink data

Read in the Olink data using the `read_olink` function. Here we will read in the example data provided with the package, as a list object.


```{r setup_and_import}
library(metaboprep)

# example file
olink_file <- system.file("extdata", "olink_v1_example.txt", package="metaboprep")

# import
dat <- read_olink(olink_file, 
                  return_Metaboprep = FALSE )
```


## Quick look to identify the types of data imported

Note that we would advocate that you read in olink data as a list object first, as it will often have additional information that you may wish to inspect prior to creating the `Metaboprep` object.


```{r dat_names}
names(dat)
```


## Create Metaboprep object 

Once imported, we pass the data to the Metaboprep() function to build the `Metaboprep` class object. 

```{r metaboprep_obj}
mydata <- Metaboprep(data      = dat$data, 
                     features  = dat$features, 
                     samples   = dat$samples)
```


## Quick summary of the metaboprep object

```{r summary_before_qc}
summary(mydata)
```


## QC Olink data

Perform the QC steps using the `quality_control` function.

```{r qc}
mydata <- mydata |>
  quality_control(source_layer        = "input", 
                  sample_missingness  = 0.2, 
                  feature_missingness = 0.2, 
                  total_peak_area_sd  = 5, 
                  outlier_udist       = 5, 
                  outlier_treatment   = "leave_be", 
                  winsorize_quantile  = 1.0, 
                  tree_cut_height     = 0.5, 
                  pc_outlier_sd       = 5,
                  feature_selection   = "max_var_exp",
                  features_exclude_but_keep = NULL
                  )

```

## Quick summary of the metaboprep object following QC

```{r summary_after_qc}
summary(mydata)
```