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

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

## Import SomaLogic data

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


```{r setup}
library(metaboprep)

# example file
filepath <- system.file("extdata", "somalogic_v1_example.adat", package = "metaboprep")

# import
dat <- read_somalogic(filepath,
                      return_Metaboprep = FALSE)

# create the object (down-sampled for speed)
m <- Metaboprep(data     = dat$data[1:9, 1:100], 
                samples  = dat$samples[1:9, ], 
                features = dat$features[1:100, ],
                )

```


## 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$data[1:9, 1:100], 
                     samples  = dat$samples[1:9, ], 
                     features = dat$features[1:100, ]
                     )
```


## 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)
```