Nothing
R/utils_import.R
In mpwR: Standardized Comparison of Workflows in Mass Spectrometry-Based Bottom-Up Proteomics
Defines functions
prepare_input
is.integer64
import_mpwR
#import
import_mpwR <- function(input_file, ...) {
data.table::fread(input_file, ...) %>%
tibble::as_tibble()
}
#change integer64
is.integer64 <- function(x) {
inherits(x, "integer64")
}
# Prepare the input data
# Input data will be renamed and default filtering will be applied
# Default filtering- MaxQuant: no potential contaminants and no reverse sequences; DIA-NN: none; Spectronaut: EG.Identified only TRUE; PD: Only High Confidence identifications
prepare_input <- function(input_df,
software = c("MaxQuant", "DIA-NN", "Spectronaut", "PD", "Generic"),
MaxQuant_addon = c("evidence", "peptide", "proteingroup"),
PD_addon = c("psm", "peptide", "protein", "proteingroup"),
diann_addon_pg_qval = 0.01,
diann_addon_prec_qval = 0.01) {
#handle global vars
. <- NULL
#**dependencies
if (software != "MaxQuant" & software != "DIA-NN" & software != "Spectronaut" & software != "PD" & software != "Generic") {
stop("Did you spell the software input wrong? Choose between: MaxQuant, DIA-NN, Spectronaut, PD")
}
##
#****************#
#*Add all columns for identifying file-software match and columns for downstream analysis:
cols_generic <- c("Run_mpwR", "ProteinGroup.IDs_mpwR", "Protein.IDs_mpwR", "Peptide.IDs_mpwR", "Precursor.IDs_mpwR", "Stripped.Sequence_mpwR", "Precursor.Charge_mpwR", "Missed.Cleavage_mpwR", "Retention.time_mpwR", "ProteinGroup_LFQ_mpwR", "Peptide_LFQ_mpwR")
cols_MQ_ev <- c("Raw file", "Proteins", "Modified sequence", "Sequence", "Missed cleavages", "Charge", "Retention time", "Potential contaminant", "Reverse")
cols_MQ_pep <- c("Sequence", "Missed cleavages", "Last amino acid", "Amino acid after", "Potential contaminant", "Reverse") #"Last amino acid" and "amino acid after" to clearly identify data as peptide.txt - currently not used for downstream analysis #requires intensity columns for tidying #requires LFQ intensity for CV LFQ calc
cols_MQ_pg <- c("Protein IDs", "Majority protein IDs", "Peptide counts (all)", "Potential contaminant", "Reverse", "Only identified by site") #"Majority protein IDs" and "Peptide counts (all)" to clearly identify peptideGroups.txt - currently not used for downstream analysis #requires intensity columns for tidying #requires LFQ intensity for CV LFQ calc
cols_DIANN <- c("Run", "Protein.Group", "Protein.Ids", "Modified.Sequence", "Stripped.Sequence", "Precursor.Id", "Precursor.Charge", "RT", "PG.MaxLFQ")
cols_spec <- c("R.FileName", "PG.ProteinGroups", "PEP.StrippedSequence", "EG.ModifiedPeptide", "EG.PrecursorId", "EG.Identified", "EG.ApexRT", "FG.Charge", "PG.Quantity", "PEP.Quantity", "PEP.NrOfMissedCleavages") # currently not used: PEP.IsProteotypic, EG.IsDecoy
cols_PD_psm <- c("Confidence", "Spectrum File", "Protein Accessions", "Annotated Sequence", "Modifications", "Number of Missed Cleavages", "Charge", "RT in min") # currently not used Apex RT in min
cols_PD_pep <- c("Number of Protein Groups", "Number of Proteins", "Number of PSMs", "Confidence", "Sequence", "Modifications", "Number of Missed Cleavages") # also contains(Found in Sample) #currently not used Annotated Sequence #"Number of Protein Groups", "Number of Proteins", "Number of PSMs" to clearly identify peptideGroups.txt - not used for downstream analysis
cols_PD_prot <- c("Proteins Unique Sequence ID", "Accession", "Description") # also contains(Found in Sample) #"Proteins Unique Sequence ID" and "Description" to cleary identify Proteins.txt - not used for downstream analysis
cols_PD_pg <- c("Protein Groups Protein Group ID", "Group Description", "Number of Proteins", "Number of Unique Peptides") # also contains(Found in Sample) #"Protein Groups Protein Group ID","Number of Proteins", "Number of Unique Peptides" to clearly identify ProteinGroups.txt - not used for downstream analysis
#***************
if (software == "MaxQuant") {
#dependencies
if (MaxQuant_addon != "evidence" & MaxQuant_addon != "peptide" & MaxQuant_addon != "proteingroup") {
stop("Did you spell the input for MaxQuant_addon wrong? Choose between: evidence, peptide, proteingroup")
}
#**
if (MaxQuant_addon == "evidence") {
#Column check
if (sum(cols_MQ_ev %in% colnames(input_df)) != length(cols_MQ_ev)) {
message <- paste("For MaxQuant evidence.txt - the following column need to be present: ", cols_MQ_ev, "\n")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
#switch from logical to character, if empty column
input_df$`Potential contaminant` <- as.character(input_df$`Potential contaminant`)
input_df$Reverse <- as.character(input_df$Reverse)
#
#replace_na - if column has no "+" - only NA entries, filtering does not work
input_df <- input_df %>%
tidyr::replace_na(
list(
`Potential contaminant` = "",
Reverse = ""
)
)
#
output_df <- input_df %>%
dplyr::filter(.data$`Potential contaminant` != "+", .data$Reverse != "+") %>%
dplyr::filter(.data$Intensity != 0) %>%
dplyr::mutate("Run_mpwR" = .data$`Raw file`,
"Protein.IDs_mpwR" = .data$Proteins,
"Peptide.IDs_mpwR" = .data$`Modified sequence`,
"Stripped.Sequence_mpwR" = .data$Sequence,
"Missed.Cleavage_mpwR" = .data$`Missed cleavages`,
"Retention.time_mpwR" = .data$`Retention time`,
"Precursor.Charge_mpwR" = .data$Charge) %>%
dplyr::filter(.data$Peptide.IDs_mpwR != "") %>%
tidyr::unite(., col = "Precursor.IDs_mpwR", c("Peptide.IDs_mpwR", "Precursor.Charge_mpwR"), sep = "", remove = FALSE) %>%
dplyr::mutate_if(is.integer64, as.double)
} else if (MaxQuant_addon == "peptide") {
#Column check
if (sum(cols_MQ_pep %in% colnames(input_df)) != length(cols_MQ_pep)) {
message <- paste("For MaxQuant peptides.txt - the following column need to be present: ", cols_MQ_pep, "\n")
stop("Not all required columns present in submitted data.")
}
if (sum(stringr::str_detect(colnames(input_df), "Intensity ")) < 1) {
stop("Not all required columns present in submitted data. No file specific intensity column(s) detected in MaxQuant peptides.txt.")
}
#**
#switch from logical to character, if empty column
input_df$`Potential contaminant` <- as.character(input_df$`Potential contaminant`)
input_df$Reverse <- as.character(input_df$Reverse)
#
#replace_na - if column has no "+" - only NA entries, filtering does not work
input_df <- input_df %>%
tidyr::replace_na(
list(
`Potential contaminant` = "",
Reverse = ""
)
)
output_df <- input_df %>%
dplyr::filter(.data$`Potential contaminant` != "+", .data$Reverse != "+") %>%
dplyr::mutate(
"Stripped.Sequence_mpwR" = .data$Sequence,
"Missed.Cleavage_mpwR" = .data$`Missed cleavages`
)
} else if (MaxQuant_addon == "proteingroup") {
#Column check
if (sum(cols_MQ_pg %in% colnames(input_df)) != length(cols_MQ_pg)) {
message <- paste("For MaxQuant proteinGroups.txt input - the following column need to be present: ", cols_MQ_pg, "\n")
message(message)
stop("Not all required columns present in submitted data.")
}
if (sum(stringr::str_detect(colnames(input_df), "Intensity ")) < 1) {
stop("Not all required columns present in submitted data. No file specific intensity column(s) detected in MaxQuant proteinGroups.txt.")
}
#**
#switch from logical to character, if empty column
input_df$`Potential contaminant` <- as.character(input_df$`Potential contaminant`)
input_df$Reverse <- as.character(input_df$Reverse)
input_df$`Only identified by site` <- as.character(input_df$`Only identified by site`)
#
#replace_na - if column has no "+" - only NA entries, filtering does not work
input_df <- input_df %>%
tidyr::replace_na(
list(
`Potential contaminant` = "",
Reverse = "",
`Only identified by site` = ""
)
)
#
output_df <- input_df %>%
dplyr::filter(.data$`Potential contaminant` != "+", .data$Reverse != "+", .data$`Only identified by site` != "+") %>%
dplyr::mutate("ProteinGroup.IDs_mpwR" = .data$`Protein IDs`) %>%
dplyr::mutate_if(is.integer64, as.double)
}
} else if (software == "DIA-NN") {
#Column check
if (sum(cols_DIANN %in% colnames(input_df)) != length(cols_DIANN)) {
message <- paste("For DIA-NN input - the following column need to be present: ", cols_DIANN, "\n")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
output_df <- input_df %>%
dplyr::filter(.data$PG.Q.Value <= diann_addon_pg_qval) %>%
dplyr::filter(.data$Q.Value <= diann_addon_prec_qval) %>%
dplyr::filter(.data$Precursor.Quantity != 0) %>%
dplyr::filter(!is.na(.data$Protein.Group)) %>%
dplyr::mutate(
"Run_mpwR" = .data$Run,
"Stripped.Sequence_mpwR" = .data$Stripped.Sequence,
"ProteinGroup.IDs_mpwR" = .data$Protein.Group,
"Protein.IDs_mpwR" = .data$Protein.Ids,
"Peptide.IDs_mpwR" = .data$Modified.Sequence,
"Precursor.IDs_mpwR" = .data$Precursor.Id,
"ProteinGroup_LFQ_mpwR" = .data$PG.MaxLFQ,
"Precursor.Charge_mpwR" = .data$Precursor.Charge,
"Retention.time_mpwR" = .data$RT)
} else if (software == "Spectronaut") {
#Column check
if (sum(cols_spec %in% colnames(input_df)) != length(cols_spec)) {
message <- paste("For Spectronaut input - the following column need to be present: ", cols_spec, "\n")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
output_df <- input_df %>%
dplyr::filter(.data$EG.Identified == "TRUE") %>%
dplyr::mutate("Run_mpwR" = .data$R.FileName,
"ProteinGroup.IDs_mpwR" = .data$PG.ProteinGroups,
"Peptide.IDs_mpwR" = .data$EG.ModifiedPeptide,
"Precursor.IDs_mpwR" = .data$EG.PrecursorId,
"Stripped.Sequence_mpwR" = .data$PEP.StrippedSequence,
"Precursor.Charge_mpwR" = .data$FG.Charge,
"Missed.Cleavage_mpwR" = .data$PEP.NrOfMissedCleavages,
"Retention.time_mpwR" = .data$EG.ApexRT,
"ProteinGroup_LFQ_mpwR" = .data$PG.Quantity, #not not necessarily LFQ quantity - depends on analysis settings in spectronaut
"Peptide_LFQ_mpwR" = .data$PEP.Quantity) %>% #not necessarily LFQ quantity - depends on analysis settings in spectronaut
dplyr::mutate_if(is.integer64, as.double)
} else if (software == "PD") {
#dependencies
if (PD_addon != "psm" & PD_addon != "protein" & PD_addon != "peptide" & PD_addon != "proteingroup") {
stop("Did you spell the input for PD_addon wrong? Choose between: psm, evidence, peptide, proteingroup")
}
#**
if (PD_addon == "psm") {
#Column check
if (sum(cols_PD_psm %in% colnames(input_df)) != length(cols_PD_psm)) {
message <- paste("For PD PSMs.txt - the following column need to be present: ", cols_PD_psm, "\n")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
output_df <- input_df %>%
dplyr::filter(.data$Confidence == "High") %>% #other: "Medium" - "Low"
dplyr::mutate("Run_mpwR" = .data$`Spectrum File`,
"ProteinGroup.IDs_mpwR" = .data$`Protein Accessions`,
"Peptide.IDs_mpwR" = .data$`Annotated Sequence`,
"Details_Mods_mpwR" = .data$Modifications,
"Missed.Cleavage_mpwR" = .data$`Number of Missed Cleavages`,
"Precursor.Charge_mpwR" = .data$Charge,
"Retention.time_mpwR" = .data$`RT in min`) %>%
tidyr::unite(., col = "Precursor.IDs_mpwR", c("Peptide.IDs_mpwR", "Precursor.Charge_mpwR"), sep = "", remove = FALSE) %>%
dplyr::mutate_if(is.integer64, as.double)
#If contaminant column is present
if (sum(stringr::str_detect(colnames(input_df), pattern = "Contaminant")) > 0) {
output_df <- output_df %>%
filter(.data$Contaminant == FALSE)
}
} else if (PD_addon == "peptide") {
#Column check
if (sum(cols_PD_pep %in% colnames(input_df)) != length(cols_PD_pep) | sum(stringr::str_detect(colnames(input_df), pattern = "Found in Sample")) == 0) {
message <- paste("For PD PeptideGroups.txt - the following column need to be present: ", cols_PD_pep, "\n")
message("For PD PeptideGroups.txt - the following column(s) need to be present: Found in Sample")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
output_df <- input_df %>%
dplyr::filter(.data$Confidence == "High") %>% #other: "Medium" - "Low"
dplyr::mutate(
"Stripped.Sequence_mpwR" = .data$`Sequence`,
"Details_Mods_mpwR" = .data$Modifications,
"Missed.Cleavage_mpwR" = .data$`Number of Missed Cleavages`
) %>%
tidyr::pivot_longer(cols = contains("Found in Sample"), names_to = "Run_mpwR", values_to = "Found.in.Sample_values_mpwR") %>% #tidy #Run needed for ID_Report - generate_level_count
dplyr::filter(.data$Found.in.Sample_values_mpwR == "High") %>% #"Not Found" - "Peak Found" - "Medium" - "Low"
dplyr::mutate_if(is.integer64, as.double)
#If contaminant column is present
if (sum(stringr::str_detect(colnames(input_df), pattern = "Contaminant")) > 0) {
output_df <- output_df %>%
filter(.data$Contaminant == FALSE)
}
} else if (PD_addon == "protein") {
#Column check
if (sum(cols_PD_prot %in% colnames(input_df)) != length(cols_PD_prot) | sum(stringr::str_detect(colnames(input_df), pattern = "Found in Sample")) == 0) {
message <- paste("For PD Proteins.txt - the following column need to be present: ", cols_PD_prot, "\n")
message("For PD Proteins.txt - the following column(s) need to be present: Found in Sample")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
output_df <- input_df %>%
dplyr::filter(.data$`Protein FDR Confidence Combined` == "High") %>% #other: "Medium" - "Low"
dplyr::mutate(
"Protein.IDs_mpwR" = .data$Accession) %>%
tidyr::pivot_longer(cols = contains("Found in Sample"), names_to = "Run_mpwR", values_to = "Found.in.Sample_values_mpwR") %>% #tidy #Run needed for ID_Report - generate_level_count
dplyr::filter(.data$Found.in.Sample_values_mpwR == "High") %>% #"Not Found" - "Peak Found" - "Medium" - "Low"
dplyr::mutate_if(is.integer64, as.double)
#If contaminant column is present
if (sum(stringr::str_detect(colnames(input_df), pattern = "Contaminant")) > 0) {
output_df <- output_df %>%
filter(.data$Contaminant == FALSE)
}
} else if (PD_addon == "proteingroup") {
#Column check####
if (sum(cols_PD_pg %in% colnames(input_df)) != length(cols_PD_pg) | sum(stringr::str_detect(colnames(input_df), pattern = "Found in Sample")) == 0) {
message <- paste("For PD ProteinGroups.txt - the following column need to be present: ", cols_PD_pg, "\n")
message("For PD ProteinGroups.txt - the following column(s) need to be present: Found in Sample")
message(message)
stop("Not all required columns present in submitted data.")
}
##############
output_df <- input_df %>%
dplyr::rename(
"ProteinGroup.IDs_mpwR" = "Protein Groups Protein Group ID") %>% #, #only number! - PD Manual page 306
tidyr::pivot_longer(cols = contains("Found in Sample"), names_to = "Run_mpwR", values_to = "Found.in.Sample_values_mpwR") %>% #tidy #Run needed for ID_Report - generate_level_count
dplyr::filter(.data$Found.in.Sample_values_mpwR == "High") %>% #"Not Found" - "Peak Found" - "Medium" - "Low"
dplyr::mutate_if(is.integer64, as.double)
}
#If contaminant column is present
if (sum(stringr::str_detect(colnames(input_df), pattern = "Contaminant")) > 0) {
output_df <- output_df %>%
filter(.data$Contaminant == FALSE)
}
} else if (software == "Generic") {
#Column check
if (sum(cols_generic %in% colnames(input_df)) != length(cols_generic)) {
message <- paste("For Generic input - the following column need to be present: ", cols_generic, "\n")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
output_df <- input_df %>%
dplyr::mutate_if(is.integer64, as.double)
}
return(output_df)
}
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Defines functions prepare_input is.integer64 import_mpwR
#import
import_mpwR <- function(input_file, ...) {
data.table::fread(input_file, ...) %>%
tibble::as_tibble()
}
#change integer64
is.integer64 <- function(x) {
inherits(x, "integer64")
}
# Prepare the input data
# Input data will be renamed and default filtering will be applied
# Default filtering- MaxQuant: no potential contaminants and no reverse sequences; DIA-NN: none; Spectronaut: EG.Identified only TRUE; PD: Only High Confidence identifications
prepare_input <- function(input_df,
software = c("MaxQuant", "DIA-NN", "Spectronaut", "PD", "Generic"),
MaxQuant_addon = c("evidence", "peptide", "proteingroup"),
PD_addon = c("psm", "peptide", "protein", "proteingroup"),
diann_addon_pg_qval = 0.01,
diann_addon_prec_qval = 0.01) {
#handle global vars
. <- NULL
#**dependencies
if (software != "MaxQuant" & software != "DIA-NN" & software != "Spectronaut" & software != "PD" & software != "Generic") {
stop("Did you spell the software input wrong? Choose between: MaxQuant, DIA-NN, Spectronaut, PD")
}
##
#****************#
#*Add all columns for identifying file-software match and columns for downstream analysis:
cols_generic <- c("Run_mpwR", "ProteinGroup.IDs_mpwR", "Protein.IDs_mpwR", "Peptide.IDs_mpwR", "Precursor.IDs_mpwR", "Stripped.Sequence_mpwR", "Precursor.Charge_mpwR", "Missed.Cleavage_mpwR", "Retention.time_mpwR", "ProteinGroup_LFQ_mpwR", "Peptide_LFQ_mpwR")
cols_MQ_ev <- c("Raw file", "Proteins", "Modified sequence", "Sequence", "Missed cleavages", "Charge", "Retention time", "Potential contaminant", "Reverse")
cols_MQ_pep <- c("Sequence", "Missed cleavages", "Last amino acid", "Amino acid after", "Potential contaminant", "Reverse") #"Last amino acid" and "amino acid after" to clearly identify data as peptide.txt - currently not used for downstream analysis #requires intensity columns for tidying #requires LFQ intensity for CV LFQ calc
cols_MQ_pg <- c("Protein IDs", "Majority protein IDs", "Peptide counts (all)", "Potential contaminant", "Reverse", "Only identified by site") #"Majority protein IDs" and "Peptide counts (all)" to clearly identify peptideGroups.txt - currently not used for downstream analysis #requires intensity columns for tidying #requires LFQ intensity for CV LFQ calc
cols_DIANN <- c("Run", "Protein.Group", "Protein.Ids", "Modified.Sequence", "Stripped.Sequence", "Precursor.Id", "Precursor.Charge", "RT", "PG.MaxLFQ")
cols_spec <- c("R.FileName", "PG.ProteinGroups", "PEP.StrippedSequence", "EG.ModifiedPeptide", "EG.PrecursorId", "EG.Identified", "EG.ApexRT", "FG.Charge", "PG.Quantity", "PEP.Quantity", "PEP.NrOfMissedCleavages") # currently not used: PEP.IsProteotypic, EG.IsDecoy
cols_PD_psm <- c("Confidence", "Spectrum File", "Protein Accessions", "Annotated Sequence", "Modifications", "Number of Missed Cleavages", "Charge", "RT in min") # currently not used Apex RT in min
cols_PD_pep <- c("Number of Protein Groups", "Number of Proteins", "Number of PSMs", "Confidence", "Sequence", "Modifications", "Number of Missed Cleavages") # also contains(Found in Sample) #currently not used Annotated Sequence #"Number of Protein Groups", "Number of Proteins", "Number of PSMs" to clearly identify peptideGroups.txt - not used for downstream analysis
cols_PD_prot <- c("Proteins Unique Sequence ID", "Accession", "Description") # also contains(Found in Sample) #"Proteins Unique Sequence ID" and "Description" to cleary identify Proteins.txt - not used for downstream analysis
cols_PD_pg <- c("Protein Groups Protein Group ID", "Group Description", "Number of Proteins", "Number of Unique Peptides") # also contains(Found in Sample) #"Protein Groups Protein Group ID","Number of Proteins", "Number of Unique Peptides" to clearly identify ProteinGroups.txt - not used for downstream analysis
#***************
if (software == "MaxQuant") {
#dependencies
if (MaxQuant_addon != "evidence" & MaxQuant_addon != "peptide" & MaxQuant_addon != "proteingroup") {
stop("Did you spell the input for MaxQuant_addon wrong? Choose between: evidence, peptide, proteingroup")
}
#**
if (MaxQuant_addon == "evidence") {
#Column check
if (sum(cols_MQ_ev %in% colnames(input_df)) != length(cols_MQ_ev)) {
message <- paste("For MaxQuant evidence.txt - the following column need to be present: ", cols_MQ_ev, "\n")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
#switch from logical to character, if empty column
input_df$`Potential contaminant` <- as.character(input_df$`Potential contaminant`)
input_df$Reverse <- as.character(input_df$Reverse)
#
#replace_na - if column has no "+" - only NA entries, filtering does not work
input_df <- input_df %>%
tidyr::replace_na(
list(
`Potential contaminant` = "",
Reverse = ""
)
)
#
output_df <- input_df %>%
dplyr::filter(.data$`Potential contaminant` != "+", .data$Reverse != "+") %>%
dplyr::filter(.data$Intensity != 0) %>%
dplyr::mutate("Run_mpwR" = .data$`Raw file`,
"Protein.IDs_mpwR" = .data$Proteins,
"Peptide.IDs_mpwR" = .data$`Modified sequence`,
"Stripped.Sequence_mpwR" = .data$Sequence,
"Missed.Cleavage_mpwR" = .data$`Missed cleavages`,
"Retention.time_mpwR" = .data$`Retention time`,
"Precursor.Charge_mpwR" = .data$Charge) %>%
dplyr::filter(.data$Peptide.IDs_mpwR != "") %>%
tidyr::unite(., col = "Precursor.IDs_mpwR", c("Peptide.IDs_mpwR", "Precursor.Charge_mpwR"), sep = "", remove = FALSE) %>%
dplyr::mutate_if(is.integer64, as.double)
} else if (MaxQuant_addon == "peptide") {
#Column check
if (sum(cols_MQ_pep %in% colnames(input_df)) != length(cols_MQ_pep)) {
message <- paste("For MaxQuant peptides.txt - the following column need to be present: ", cols_MQ_pep, "\n")
stop("Not all required columns present in submitted data.")
}
if (sum(stringr::str_detect(colnames(input_df), "Intensity ")) < 1) {
stop("Not all required columns present in submitted data. No file specific intensity column(s) detected in MaxQuant peptides.txt.")
}
#**
#switch from logical to character, if empty column
input_df$`Potential contaminant` <- as.character(input_df$`Potential contaminant`)
input_df$Reverse <- as.character(input_df$Reverse)
#
#replace_na - if column has no "+" - only NA entries, filtering does not work
input_df <- input_df %>%
tidyr::replace_na(
list(
`Potential contaminant` = "",
Reverse = ""
)
)
output_df <- input_df %>%
dplyr::filter(.data$`Potential contaminant` != "+", .data$Reverse != "+") %>%
dplyr::mutate(
"Stripped.Sequence_mpwR" = .data$Sequence,
"Missed.Cleavage_mpwR" = .data$`Missed cleavages`
)
} else if (MaxQuant_addon == "proteingroup") {
#Column check
if (sum(cols_MQ_pg %in% colnames(input_df)) != length(cols_MQ_pg)) {
message <- paste("For MaxQuant proteinGroups.txt input - the following column need to be present: ", cols_MQ_pg, "\n")
message(message)
stop("Not all required columns present in submitted data.")
}
if (sum(stringr::str_detect(colnames(input_df), "Intensity ")) < 1) {
stop("Not all required columns present in submitted data. No file specific intensity column(s) detected in MaxQuant proteinGroups.txt.")
}
#**
#switch from logical to character, if empty column
input_df$`Potential contaminant` <- as.character(input_df$`Potential contaminant`)
input_df$Reverse <- as.character(input_df$Reverse)
input_df$`Only identified by site` <- as.character(input_df$`Only identified by site`)
#
#replace_na - if column has no "+" - only NA entries, filtering does not work
input_df <- input_df %>%
tidyr::replace_na(
list(
`Potential contaminant` = "",
Reverse = "",
`Only identified by site` = ""
)
)
#
output_df <- input_df %>%
dplyr::filter(.data$`Potential contaminant` != "+", .data$Reverse != "+", .data$`Only identified by site` != "+") %>%
dplyr::mutate("ProteinGroup.IDs_mpwR" = .data$`Protein IDs`) %>%
dplyr::mutate_if(is.integer64, as.double)
}
} else if (software == "DIA-NN") {
#Column check
if (sum(cols_DIANN %in% colnames(input_df)) != length(cols_DIANN)) {
message <- paste("For DIA-NN input - the following column need to be present: ", cols_DIANN, "\n")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
output_df <- input_df %>%
dplyr::filter(.data$PG.Q.Value <= diann_addon_pg_qval) %>%
dplyr::filter(.data$Q.Value <= diann_addon_prec_qval) %>%
dplyr::filter(.data$Precursor.Quantity != 0) %>%
dplyr::filter(!is.na(.data$Protein.Group)) %>%
dplyr::mutate(
"Run_mpwR" = .data$Run,
"Stripped.Sequence_mpwR" = .data$Stripped.Sequence,
"ProteinGroup.IDs_mpwR" = .data$Protein.Group,
"Protein.IDs_mpwR" = .data$Protein.Ids,
"Peptide.IDs_mpwR" = .data$Modified.Sequence,
"Precursor.IDs_mpwR" = .data$Precursor.Id,
"ProteinGroup_LFQ_mpwR" = .data$PG.MaxLFQ,
"Precursor.Charge_mpwR" = .data$Precursor.Charge,
"Retention.time_mpwR" = .data$RT)
} else if (software == "Spectronaut") {
#Column check
if (sum(cols_spec %in% colnames(input_df)) != length(cols_spec)) {
message <- paste("For Spectronaut input - the following column need to be present: ", cols_spec, "\n")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
output_df <- input_df %>%
dplyr::filter(.data$EG.Identified == "TRUE") %>%
dplyr::mutate("Run_mpwR" = .data$R.FileName,
"ProteinGroup.IDs_mpwR" = .data$PG.ProteinGroups,
"Peptide.IDs_mpwR" = .data$EG.ModifiedPeptide,
"Precursor.IDs_mpwR" = .data$EG.PrecursorId,
"Stripped.Sequence_mpwR" = .data$PEP.StrippedSequence,
"Precursor.Charge_mpwR" = .data$FG.Charge,
"Missed.Cleavage_mpwR" = .data$PEP.NrOfMissedCleavages,
"Retention.time_mpwR" = .data$EG.ApexRT,
"ProteinGroup_LFQ_mpwR" = .data$PG.Quantity, #not not necessarily LFQ quantity - depends on analysis settings in spectronaut
"Peptide_LFQ_mpwR" = .data$PEP.Quantity) %>% #not necessarily LFQ quantity - depends on analysis settings in spectronaut
dplyr::mutate_if(is.integer64, as.double)
} else if (software == "PD") {
#dependencies
if (PD_addon != "psm" & PD_addon != "protein" & PD_addon != "peptide" & PD_addon != "proteingroup") {
stop("Did you spell the input for PD_addon wrong? Choose between: psm, evidence, peptide, proteingroup")
}
#**
if (PD_addon == "psm") {
#Column check
if (sum(cols_PD_psm %in% colnames(input_df)) != length(cols_PD_psm)) {
message <- paste("For PD PSMs.txt - the following column need to be present: ", cols_PD_psm, "\n")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
output_df <- input_df %>%
dplyr::filter(.data$Confidence == "High") %>% #other: "Medium" - "Low"
dplyr::mutate("Run_mpwR" = .data$`Spectrum File`,
"ProteinGroup.IDs_mpwR" = .data$`Protein Accessions`,
"Peptide.IDs_mpwR" = .data$`Annotated Sequence`,
"Details_Mods_mpwR" = .data$Modifications,
"Missed.Cleavage_mpwR" = .data$`Number of Missed Cleavages`,
"Precursor.Charge_mpwR" = .data$Charge,
"Retention.time_mpwR" = .data$`RT in min`) %>%
tidyr::unite(., col = "Precursor.IDs_mpwR", c("Peptide.IDs_mpwR", "Precursor.Charge_mpwR"), sep = "", remove = FALSE) %>%
dplyr::mutate_if(is.integer64, as.double)
#If contaminant column is present
if (sum(stringr::str_detect(colnames(input_df), pattern = "Contaminant")) > 0) {
output_df <- output_df %>%
filter(.data$Contaminant == FALSE)
}
} else if (PD_addon == "peptide") {
#Column check
if (sum(cols_PD_pep %in% colnames(input_df)) != length(cols_PD_pep) | sum(stringr::str_detect(colnames(input_df), pattern = "Found in Sample")) == 0) {
message <- paste("For PD PeptideGroups.txt - the following column need to be present: ", cols_PD_pep, "\n")
message("For PD PeptideGroups.txt - the following column(s) need to be present: Found in Sample")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
output_df <- input_df %>%
dplyr::filter(.data$Confidence == "High") %>% #other: "Medium" - "Low"
dplyr::mutate(
"Stripped.Sequence_mpwR" = .data$`Sequence`,
"Details_Mods_mpwR" = .data$Modifications,
"Missed.Cleavage_mpwR" = .data$`Number of Missed Cleavages`
) %>%
tidyr::pivot_longer(cols = contains("Found in Sample"), names_to = "Run_mpwR", values_to = "Found.in.Sample_values_mpwR") %>% #tidy #Run needed for ID_Report - generate_level_count
dplyr::filter(.data$Found.in.Sample_values_mpwR == "High") %>% #"Not Found" - "Peak Found" - "Medium" - "Low"
dplyr::mutate_if(is.integer64, as.double)
#If contaminant column is present
if (sum(stringr::str_detect(colnames(input_df), pattern = "Contaminant")) > 0) {
output_df <- output_df %>%
filter(.data$Contaminant == FALSE)
}
} else if (PD_addon == "protein") {
#Column check
if (sum(cols_PD_prot %in% colnames(input_df)) != length(cols_PD_prot) | sum(stringr::str_detect(colnames(input_df), pattern = "Found in Sample")) == 0) {
message <- paste("For PD Proteins.txt - the following column need to be present: ", cols_PD_prot, "\n")
message("For PD Proteins.txt - the following column(s) need to be present: Found in Sample")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
output_df <- input_df %>%
dplyr::filter(.data$`Protein FDR Confidence Combined` == "High") %>% #other: "Medium" - "Low"
dplyr::mutate(
"Protein.IDs_mpwR" = .data$Accession) %>%
tidyr::pivot_longer(cols = contains("Found in Sample"), names_to = "Run_mpwR", values_to = "Found.in.Sample_values_mpwR") %>% #tidy #Run needed for ID_Report - generate_level_count
dplyr::filter(.data$Found.in.Sample_values_mpwR == "High") %>% #"Not Found" - "Peak Found" - "Medium" - "Low"
dplyr::mutate_if(is.integer64, as.double)
#If contaminant column is present
if (sum(stringr::str_detect(colnames(input_df), pattern = "Contaminant")) > 0) {
output_df <- output_df %>%
filter(.data$Contaminant == FALSE)
}
} else if (PD_addon == "proteingroup") {
#Column check####
if (sum(cols_PD_pg %in% colnames(input_df)) != length(cols_PD_pg) | sum(stringr::str_detect(colnames(input_df), pattern = "Found in Sample")) == 0) {
message <- paste("For PD ProteinGroups.txt - the following column need to be present: ", cols_PD_pg, "\n")
message("For PD ProteinGroups.txt - the following column(s) need to be present: Found in Sample")
message(message)
stop("Not all required columns present in submitted data.")
}
##############
output_df <- input_df %>%
dplyr::rename(
"ProteinGroup.IDs_mpwR" = "Protein Groups Protein Group ID") %>% #, #only number! - PD Manual page 306
tidyr::pivot_longer(cols = contains("Found in Sample"), names_to = "Run_mpwR", values_to = "Found.in.Sample_values_mpwR") %>% #tidy #Run needed for ID_Report - generate_level_count
dplyr::filter(.data$Found.in.Sample_values_mpwR == "High") %>% #"Not Found" - "Peak Found" - "Medium" - "Low"
dplyr::mutate_if(is.integer64, as.double)
}
#If contaminant column is present
if (sum(stringr::str_detect(colnames(input_df), pattern = "Contaminant")) > 0) {
output_df <- output_df %>%
filter(.data$Contaminant == FALSE)
}
} else if (software == "Generic") {
#Column check
if (sum(cols_generic %in% colnames(input_df)) != length(cols_generic)) {
message <- paste("For Generic input - the following column need to be present: ", cols_generic, "\n")
message(message)
stop("Not all required columns present in submitted data.")
}
#**
output_df <- input_df %>%
dplyr::mutate_if(is.integer64, as.double)
}
return(output_df)
}
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