R/ReportTables.R

In BioAlvaro/MQviewer: Quality Control of Protemics Data

#' Report Tables with summary data
#'
#' @param MQCombined The directory to the "combined" folder where the
#'  MaxQuant results are stored.
#' @param log_base The logarithmic scale for the intensity. Default is 2.
#'
#' @param long_names If TRUE, samples having long names will be considered, and
#'  the name will be split by sep_names. By default = FALSE.
#'
#' @param sep_names If long_names is TRUE, sep_names has to be selected. Samples
#'  names will be split. By default is NULL.
#'
#' @param intensity_type The type of intensity. Values: 'Intensity' or 'LFQ'.
#'
#'
#' @return A list with four tables are generated:
#'  - Protein Information
#'  - Intensity Information
#'  - Peptide Charge Information
#'  - Peptide hydrophobicity Information
#' @export
#'
#' @examples
#' MQPathCombined <- system.file('extdata/combined/', package = 'MQmetrics')
#' MQCombined <- make_MQCombined(MQPathCombined)
#' ReportTables(MQCombined)
ReportTables <- function(MQCombined,
                        long_names = FALSE,
                        sep_names = NULL,
                        log_base = 2,
                        intensity_type = 'Intensity'){

    sd <- median <- Experiment <- Charge <- variable <- NULL
    `Missed cleavages` <- value <- `freq` <- samples <- NULL


    MQPathCombined <- MQCombined$MQPathCombined

    MaxQuant_version <- MQCombined$parameters$Value[
        MQCombined$parameters$Parameter == 'Version']


    #### Table 1, Summary ####

    #Read the Protein Groups without removing the contamintants To plot it.
    proteinGroups <- read_delim(file.path(MQPathCombined,
                                        "txt/proteinGroups.txt"),
                                "\t",
                                escape_double = FALSE,
                                trim_ws = TRUE,
                                guess_max = 100000)

    if (intensity_type == 'Intensity') {


        protein_table <- proteinGroups %>%
            select(contains(c('Intensity ',
                            'Reverse',
                            'Potential',
                            'Only identified by site')
            )
            ) %>%
            select(-contains('LFQ'))

        #Remove Intensity from name
        colnames(protein_table) <- gsub("Intensity.",
                                        "",
                                        colnames(protein_table)
        )

        title <- 'Proteins Identified based on Intensity'

    }

    if (intensity_type == 'LFQ'){

        #Remove LFQ Intensity from name

        protein_table <- proteinGroups %>%
            select(contains(c('LFQ ',
                            'Reverse',
                            'Potential',
                            'Only identified by site')
            ))

        colnames(protein_table) <- gsub("LFQ intensity.", "",
                                        colnames(protein_table))

        title <- 'Proteins Identified based on LFQ intensity'

        #Error if LFQ Intensity not found.

        if (length(protein_table) == 0) {
            print('LFQ intensities not found,
                changing automatically to Intensity.')

            protein_table <- proteinGroups %>%
                select(contains(c('Intensity ',
                                'Reverse',
                                'Potential',
                                'Only identified by site'))) %>%
                select(-contains('LFQ'))
            #Remove Intensity from name
            colnames(protein_table) <- gsub("Intensity.", "",
                                            colnames(protein_table))

            title <- 'Proteins Identified based on Intensity'

        }
        }


    table_summary <- data.frame(
        "Proteins Identified" = nrow(protein_table)-colSums(protein_table==0),
        "Missing values" = colSums(protein_table==0),
        "Potential contaminants" = colSums(protein_table[grep('+',
                                    protein_table$`Potential contaminant`),]>0),
        "Reverse" = colSums(protein_table[grep('+',
                                            protein_table$Reverse),] >0),
        #"Reverse" = length(which(protein_table$Reverse == '+')),
        "Only identified by site"= colSums(
            protein_table[grep('+',
                                protein_table$`Only identified by site`),] >0),
        check.names = FALSE) #To have spaces .

    table_summary <- table_summary[!(row.names(table_summary) %in%
                                    c("Reverse",
                                    "Potential contaminant",
                                    "Only identified by site")),]

    table_summary$Experiment <- rownames(table_summary)

    rownames(table_summary) <- NULL

    table_summary <- table_summary[,c(6,1,2,3,4,5)]


    # Add column peptides identified, and peptide/protein ratio.

    summary <- read_delim(file.path(MQPathCombined,
                                    "txt/summary.txt"),
                                    "\t",
                                    escape_double = FALSE,
                                    trim_ws = TRUE,
                                    na = c("NA", "NaN", "", " "))

    #Detect MaxQuant Version to read column names accordingly.

    if (MaxQuant_version == '1.6.17.0') {

        df <- summary %>% select(contains(c('Experiment',
                                            'Peptide Sequences Identified')))


    } else{
        df <- summary %>% select(contains(c('Experiment',
                                            'Peptide sequences identified')))
        colnames(df)[colnames(df) == "Peptide sequences identified"] <- "Peptide Sequences Identified"

    }




    table_summary <- merge(table_summary, df, by = 'Experiment')

    table_summary$`Peptides/Proteins` <- format(round(
        table_summary$`Peptide Sequences Identified` /
            table_summary$`Proteins Identified`, 1), nsmall = 1)


    combined_row <- c('Combined Samples',
                    nrow(proteinGroups), #total proteins
                    NA,  # NA combined,
                    length(which(proteinGroups$`Potential contaminant` == '+')),
                    length(which(proteinGroups$Reverse == '+')),
                    length(which(proteinGroups$`Only identified by site` == '+')
                        ),
                    summary$`Peptide Sequences Identified`[nrow(summary)],
                    format(
                        round(
                            summary$`Peptide Sequences Identified`[
                        nrow(summary)]/nrow(proteinGroups), 1
                        ),
                        nsmall = 1)
                    )


    table_summary <- rbind( combined_row, table_summary)

    #### Table 2 Log10 intensities ####


    int_info <- protein_table %>% select(-contains(c('Reverse',
                                                    'Potential contaminant',
                                                    'Only identified by site'))
    )

    int_info[int_info == 0] <- NA

    #log intensities

    if(log_base == 2){
        dynamic_table <- do.call(data.frame,
                                list(mean = format(log2(apply(int_info,
                                                2,
                                                mean,
                                                na.rm=TRUE)),
                                        digits = 4),

                            sd = format(log2(apply(int_info,
                                                2,
                                                sd,
                                                na.rm=TRUE)),
                                        digits = 4),

                            median = format(log2(apply(int_info,
                                                    2,
                                                    median,
                                                    na.rm=TRUE)),
                                            digits =4),
                            min = format(log2(apply(int_info,
                                                2,
                                                min,
                                                na.rm=TRUE)),
                                        digits = 4),
                            max = format(log2(apply(int_info,
                                                2,
                                                max,na.rm=TRUE)),
                                        digits = 4),
                            n = apply(int_info, 2, length)-
                                colSums(is.na(int_info))))
    }

    if(log_base == 10){
        dynamic_table <- do.call(data.frame,
                                list(mean = format(log10(apply(int_info,
                                                            2,
                                                            mean,
                                                            na.rm=TRUE)),
                                                digits = 4),
                                    sd = format(log10(apply(int_info, 2,
                                                            sd,
                                                            na.rm=TRUE)),
                                                digits = 4),
                                    median = format(log10(apply(int_info, 2,
                                                            median,
                                                            na.rm=TRUE)),
                                                digits =4),
                                    min = format(log10(apply(int_info, 2,
                                                            min,na.rm=TRUE)),
                                                digits = 4),
                                    max = format(log10(apply(int_info, 2, max,
                                                            na.rm=TRUE)),
                                                digits = 4),
                                    n = apply(int_info,
                                            2,
                                            length)-colSums(is.na(int_info))
                                    )
        )
    }


    dynamic_table$Experiment  <- rownames(dynamic_table)

    rownames(dynamic_table) <- NULL
2
    dynamic_table <- dynamic_table[,c(7,1,2,3,4,5,6)]

    #### Table 3 Charge ####

    charge_percentage <- PlotCharge(MQCombined, tabular_output =  TRUE)

    #### Table 4, GRAVY ####

    GRAVY <- PlotHydrophobicity(MQCombined, tabular_output = TRUE)

    #### Table  5, Cleavages ####

    missed_summary <- PlotProteaseSpecificity(MQCombined, tabular_output = TRUE)

    #### Table 6 Completeness or PlotCoverageAll ####

    df_bin_stat <- PlotProteinOverlap(MQCombined, tabular_output = TRUE)

    #### Combination all tables ####
    out <- list()

    out$proteins <- table_summary
    out$intensities <- dynamic_table
    out$charge <- charge_percentage
    out$GRAVY <- GRAVY
    out$cleavages <- missed_summary
    out$overlap <- df_bin_stat




    if (long_names == TRUE) {
        out$proteins$Experiment <- gsub(sep_names,
                                        ' ',
                                        out$proteins$Experiment)
        out$intensities$Experiment <- gsub(sep_names,
                                            ' ',
                                            out$intensities$Experiment)
        out$charge$Experiment <- gsub(sep_names,
                                        ' ',
                                        out$charge$Experiment)
        out$GRAVY$Experiment <- gsub(sep_names,
                                        ' ',
                                        out$GRAVY$Experiment)
        out$cleavages$Experiment <- gsub(sep_names,
                                            ' ',
                                            out$cleavages$Experiment)
    }

    return(out)
}