inst/doc/input_preparation_workflow.R

In protti: Bottom-Up Proteomics and LiP-MS Quality Control and Data Analysis Tools

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup, message = FALSE, warning = FALSE----------------------------------
library(magrittr)
library(dplyr)
library(tidyr)
library(stringr)

## ----Spectronaut, eval=FALSE--------------------------------------------------
#  # To read in your own data you can use read_protti()
#  spectronaut_data <- read_protti(filename = "mydata/spectronaut.csv")

## ----MaxQuant_peptide, eval=FALSE---------------------------------------------
#  # To read in your own data you can use read_protti()
#  evidence <- read_protti(filename = "yourpath/evidence.txt")
#  
#  evidence_proteotypic <- evidence %>%
#    # adds new column with logicals that are TRUE if the peptide can be assigned
#    # to only one protein and FALSE if it can be assigned to multiple
#    mutate(is_proteotypic = str_detect(
#      string = proteins,
#      pattern = ";",
#      negate = TRUE
#    )) %>%
#    # adds new column with logicals indicating if peptide is coming from a potential contaminant
#    mutate(is_contaminant = ifelse(potential_contaminant == "+", TRUE, FALSE))
#  
#  # Make an annotation data frame and merge it with your data frame to obtain conditions
#  # We are annotating sample 1-3 as controls and samples 4-6 as treated conditions
#  
#  file_name <- c( # make sure that the names are the same name as in your report
#    "sample1",
#    "sample2",
#    "sample3",
#    "sample4",
#    "sample5",
#    "sample6"
#  )
#  
#  condition <- c(
#    "control",
#    "control",
#    "control",
#    "treated",
#    "treated",
#    "treated"
#  )
#  
#  annotation <- data.frame(file_name, condition)
#  
#  # Combine your long data frame with the annotation
#  evidence_annotated <- evidence_proteotypic %>%
#    left_join(y = annotation, by = "file_name")

## ----MaxQuant_protein, eval=FALSE---------------------------------------------
#  # To read in your own data you can use read_protti()
#  protein_groups <- read_protti(filename = "yourpath/proteinGroups.txt") %>%
#    # adds new column with logicals indicating if protein is a potential contaminant,
#    # you can filter these out later on. You should also consider filtering out proteins
#    # that were "only identified by site" and reverse hits, as well as proteins with only
#    # one identified peptide
#    mutate(is_potential_contaminant = ifelse(potential_contaminant == "+", TRUE, FALSE))
#  
#  # Change wide format to long format and create new columns called `r_file_name`and `intensity`
#  protein_groups_long <- protein_groups %>%
#    pivot_longer(
#      cols = starts_with("intensity_"),
#      names_to = "file_name",
#      values_to = "intensity"
#    )
#  
#  # Make an annotation data frame and merge it with your data frame to obtain conditions
#  # We are annotating sample 1-3 as controls and samples 4-6 as treated conditions
#  
#  file_name <- c( # make sure that the names are the same name as in your report
#    "intensity_sample1",
#    "intensity_sample2",
#    "intensity_sample3",
#    "intensity_sample4",
#    "intensity_sample5",
#    "intensity_sample6"
#  )
#  
#  condition <- c(
#    "control",
#    "control",
#    "control",
#    "treated",
#    "treated",
#    "treated"
#  )
#  
#  annotation <- data.frame(file_name, condition)
#  
#  # Combine your long data frame with the annotation
#  protein_groups_annotated <- protein_groups_long %>%
#    left_join(y = annotation, by = "file_name")

## ----Skyline, eval=FALSE------------------------------------------------------
#  # Load data
#  skyline_data <- read_protti(filename = "yourpath/skyline.csv")
#  
#  skyline_data_int <- skyline_data %>%
#    # create a column with precursor information
#    mutate(precursor = paste0(peptide_sequence, "_", charge)) %>%
#    group_by(replicate_name, precursor) %>%
#    # making a new column containing the summed up intensities of all transitions of one precursor
#    mutate(sum_intensity = sum(area)) %>%
#    select(-c(product_mz, area)) %>% # removing the columns we don't need
#    distinct() # removing duplicated rows from the data frame
#  
#  # Add annotation
#  # make sure that the names are the same name as in your report
#  replicate_name <- c(
#    "sample_1",
#    "sample_2",
#    "sample_3",
#    "sample_1",
#    "sample_2",
#    "sample_3"
#  )
#  
#  condition <- c(
#    "control",
#    "control",
#    "control",
#    "treated",
#    "treated",
#    "treated"
#  )
#  
#  annotation <- data.frame(replicate_name, condition)
#  
#  # Combine your long data frame with the annotation
#  skyline_annotated <- skyline_data_int %>%
#    left_join(y = annotation, by = "replicate_name")

## ----Proteome_discoverer_pep, eval=FALSE--------------------------------------
#  # Load data
#  pd_pep_data <- read_protti("yourpath/PDpeptides.csv")
#  
#  # Select relevant columns
#  pd_pep_selected <- pd_pep_data %>%
#    select(
#      sequence,
#      modifications,
#      number_proteins,
#      contaminant,
#      master_protein_accessions,
#      starts_with("abundances_grouped"), # select all columns that start with "abundances_grouped"
#      quan_info
#    )
#  
#  # Filter data frame
#  pd_pep_filtered <- pd_pep_selected %>%
#    filter(contaminant == FALSE) %>% # remove annotated contaminants
#    filter(number_proteins == 1) %>% # select proteotypic peptides
#    filter(quan_info != "No Quan Values") # remove peptides that have no quantification values
#  
#  # Convert into long format
#  pd_pep_long <- pd_pep_filtered %>%
#    pivot_longer(
#      cols = starts_with("abundances"),
#      names_to = "file_name",
#      values_to = "intensity"
#    ) %>%
#    # combine peptide sequence and modifications to make a precursor column
#    mutate(precursor = paste(sequence, modifications))
#  
#  # Make annotation data frame
#  file_name <- c( # make sure that the names are the same name as in your report
#    "abundances_grouped_f1",
#    "abundances_grouped_f2",
#    "abundances_grouped_f3",
#    "abundances_grouped_f4",
#    "abundances_grouped_f5",
#    "abundances_grouped_f6"
#  )
#  
#  condition <- c(
#    "control",
#    "control",
#    "control",
#    "treated",
#    "treated",
#    "treated"
#  )
#  
#  annotation <- data.frame(file_name, condition)
#  
#  # Combine your long data frame with the annotation
#  pd_pep_long_annotated <- pd_pep_long %>%
#    left_join(y = annotation, by = "file_name")

## ----Proteome_discoverer_prot, eval=FALSE-------------------------------------
#  # Load data
#  pd_prot_data <- read_protti("yourpath/PDproteins.csv")
#  
#  # Select relevant columns
#  pd_prot_selected <- pd_prot_data %>%
#    select(
#      accession,
#      description,
#      contaminant,
#      number_peptides,
#      starts_with("abundances_grouped"), # select all columns that start with "abundances_grouped"
#    )
#  
#  # Filter data frame
#  pd_prot_data_filtered <- pd_prot_selected %>%
#    filter(contaminant == FALSE) %>% # remove annotated contaminants
#    filter(number_peptides > 1) # select proteins with more than one identified peptide
#  
#  # Convert into long format
#  pd_prot_long <- pd_prot_data_filtered %>%
#    pivot_longer(
#      cols = starts_with("abundances"),
#      names_to = "file_name",
#      values_to = "intensity"
#    )
#  
#  # Make annotation data frame
#  file_name <- c( # make sure that the names are the same name as in your report
#    "abundances_grouped_f1",
#    "abundances_grouped_f2",
#    "abundances_grouped_f3",
#    "abundances_grouped_f4",
#    "abundances_grouped_f5",
#    "abundances_grouped_f6"
#  )
#  
#  condition <- c(
#    "control",
#    "control",
#    "control",
#    "treated",
#    "treated",
#    "treated"
#  )
#  
#  annotation <- data.frame(file_name, condition)
#  
#  # Combine your long data frame with the annotation
#  pd_prot_long_annotated <- pd_prot_long %>%
#    left_join(y = annotation, by = "file_name")