---
title: "Computing Dynamic SWATH Windows"
author: "Witold E. Wolski, Bernd Roschitzki"
date: "`r Sys.Date()`"
package: "`r packageVersion('prozor')`"
vignette: >
  %\VignetteIndexEntry{Computing Dynamic SWATH Windows}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
output: 
  html_document
editor_options: 
  chunk_output_type: console
---


# Introduction

Function optimizes Extraction windows for DIA/SWATH so we have the same number of precursor per window. This optimization is based on spectral library data or non redundant .blib files (Bibliospec).

# Prerequisites

```{r echo=FALSE}
library(prozor)

```

# Constant with method


```{r fig.retina=3}
data("masses")
cdsw <- Cdsw(masses , nbins = 25, digits = 1)
cdsw$plot()

```


```{r, results='markup'}
knitr::kable(cdsw$asTable())
```


```{r}
constError <- cdsw$error()
```

# Classical Method based on `quantile`

Same number of MS1 precursors in each window

```{r}
cdsw$quantile_breaks()
```

```{r fig.retina=3, warning=FALSE}
cdsw$plot()
```

```{r, results='markup'}
knitr::kable(cdsw$asTable())
```


```{r}
quantileError <- cdsw$error()
```

# Adjust windows

Using this method the window start and end is shifted to a mass range with as few MS1 peaks as possible.

```{r, }
knitr::kable(cdsw$optimizeWindows(maxbin = 10, plot = TRUE) )
```

# Dynamic Swath Windows with Constraints.


- Mass range can be specified (*mass\_range*)
- Maximal window size can be specified (*max\_window\_size*). This is because windows should not be to large because of optimal collision energy (personal communication by Bernd R.).
- Minimal window size can be specified (*min\_window\_size*).
- target number of windows can be specified (*nr\_windows*).
- boundaries between windows are placed in regions were no precursors are observed.


```{r fig.retina=3}
cdsw$sampling_breaks(maxwindow = 100,plot = TRUE)
```

```{r fig.retina=3}
cdsw$plot()
```

```{r, results='markup'}
knitr::kable(cdsw$asTable())
```

```{r}
knitr::kable(cdsw$optimizeWindows(maxbin = 10, plot = TRUE) )
```

```{r}
mixedError <- cdsw$error()
```


# Benchmarking of the methods.

We compare the optimal number of MS1 peaks per SWATH window (same in each window) with the numbers obtained by using all of the 3 methods implemented.  

```{r fig.retina=3}
barplot(c(const = constError$score1, quantile = quantileError$score1, mixed = mixedError$score1),ylab = "Manhattan distance")
barplot(c(const = constError$score2, quantile = quantileError$score2, mixed = mixedError$score2),ylab = "Euclidean distance")

```
We can see that Method 3 has a relatively small error although it is able to fulfill constraints such as maximum window size.

# Session info


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