Author: Oliver Kardell

# Example Retention Time Distribution

#### Oliver Kardell

# Introduction

This vignette serves as application example for analyzing inter-software differences with flowTraceR. Here, the focus is on retention time (RT). Note that this can be easily changed to other characteristics such as intensity, charge state etc. or can be expanded to other metadata e.g. quantitative and retention time precision.

## Loading R packages

    library(flowTraceR)
    library(magrittr)
    library(dplyr)
    library(tidyr)
    library(stringr)
    library(tibble)
    library(ggplot2)

## Preparation

Use the provided example data or import your own data.

    # Get example data
    diann <- flowTraceR::get_example("RetentionTime")[["DIA-NN"]]
    spectronaut <- flowTraceR::get_example("RetentionTime")[["Spectronaut"]]

## Conversion to standardized format

For converting precursor, modified peptide and proteingroup level at once use `convert_all_levels()`.

    # Convert all levels
    diann_all_converted <- flowTraceR::convert_all_levels(input_df = diann, software = "DIA-NN")
    spectronaut_all_converted <- flowTraceR::convert_all_levels(input_df = spectronaut, software = "Spectronaut")

# RT distribution

## Without flowTraceR

Without conversion to a standardized format no distinction between common and unique precursor identifications can be made and thus no inter-software comparisons for characteristics such as retention time is possible.

    #Example - DIA-NN output

    #Get common entries based on software-specific entries
    diann_common <- dplyr::semi_join(
      diann_all_converted,
      spectronaut_all_converted,
      by = c("Precursor.Id" = "EG.PrecursorId"))

    #Get unique entries based on software-specific entries
    diann_unique <- dplyr::anti_join(
      diann_all_converted,
      spectronaut_all_converted,
      by = c("Precursor.Id" = "EG.PrecursorId"))

    #Connect
    diann_all <- dplyr::bind_rows(
      common = diann_common,
      unique = diann_unique,
      .id = "characteristic")

    #Plot
    #no common entries detected!
    ggplot2::ggplot(diann_all, aes(x = RT, color = characteristic)) +
      geom_density() +
      scale_x_continuous(limits = c(0,115))

## With flowTraceR

    #Example - DIA-NN output

    #Get common entries based on flowTraceR´s entries
    diann_common_traceR <- dplyr::semi_join(
      diann_all_converted,
      spectronaut_all_converted,
      by = c("traceR_precursor"))

    #Get unique entries based on flowTraceR´s entries
    diann_unique_traceR <- dplyr::anti_join(
      diann_all_converted,
      spectronaut_all_converted,
      by = c("traceR_precursor"))

    #Connect
    diann_traceR <- dplyr::bind_rows(
      common = diann_common_traceR,
      unique = diann_unique_traceR,
      .id = "characteristic")

    #Plot
    ggplot2::ggplot(diann_traceR, aes(x = RT, color = characteristic)) +
      geom_density() +
      scale_x_continuous(limits = c(0,115))

# Inter-software comparison

## Common entries

The inter-software comparison with flowTraceR shows high similarity between the RT of common precursor.

    #Spectronaut - common entries
    spectronaut_common_traceR <- dplyr::semi_join(
      spectronaut_all_converted,
      diann_all_converted,
      by = c("traceR_precursor")) %>%
      dplyr::rename(RT = EG.ApexRT)

    #Common combined
    RT_common <- dplyr::bind_rows(
      "DIA-NN" = diann_common_traceR[,"RT"],
      Spectronaut = spectronaut_common_traceR[, "RT"],
      .id = "Software")

    #Plot
    ggplot2::ggplot(RT_common, aes(x = RT, color = Software)) +
      geom_density() +
      scale_x_continuous(limits = c(0,115))

## Unique entries

    #Spectronaut unique entries
    spectronaut_unique_traceR <- dplyr::anti_join(
      spectronaut_all_converted,
      diann_all_converted,
      by = c("traceR_precursor")) %>%
      dplyr::rename(RT = EG.ApexRT)

    #Unique entries combined
    RT_unique <- dplyr::bind_rows(
      "DIA-NN" = diann_unique_traceR[,"RT"],
      Spectronaut = spectronaut_unique_traceR[, "RT"],
      .id = "Software"
    )

    #Plot
    ggplot2::ggplot(RT_unique, aes(x = RT, color = Software)) +
      geom_density() +
      scale_x_continuous(limits = c(0,115))