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## ----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")
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