R/nmr_identify_regions.R

In sipss/AlpsNMR: Automated spectraL Processing System for NMR

#' NMR peak identification (plasma/serum samples)
#'
#' Identify given regions and return a data frame with plausible assignations
#' in human plasma/serum samples.
#'
#' @return a data frame with plausible assignations.
#'
#' @examples
#' # We identify regions from from the corresponding ppm storaged in a vector.
#' ppm_to_assign <- c(
#'     4.060960203, 3.048970634, 2.405935596,
#'     3.24146865, 0.990616851, 1.002075066, 0.955325548
#' )
#' identification <- nmr_identify_regions_blood(ppm_to_assign)
#' @export
#' @family peak detection functions
#' @family peak integration functions
#' @param ppm_to_assign A vector with the ppm regions to assign
#' @param num_proposed_compounds set the number of proposed metabolites sorted by the number times reported in the HMDB: `HMDB_blood`.
#' @param verbose Logical value. Set it to TRUE to print additional information
nmr_identify_regions_blood <- function(ppm_to_assign, num_proposed_compounds = 3, verbose = FALSE) {
    HMDB_blood <- NULL
    utils::data("HMDB_blood", package = "AlpsNMR", envir = environment())
    output_assignation_list <- HMDB_blood[NULL, ]

    for (ppm in ppm_to_assign) {
        lower_ppm_right_edge <- ppm - 0.015
        higher_ppm_left_edge <- ppm + 0.015

        ind <- intersect(
            which(HMDB_blood$Shift_ppm < higher_ppm_left_edge),
            which(HMDB_blood$Shift_ppm > lower_ppm_right_edge)
        )
        assignation_list <- as.data.frame(HMDB_blood[ind, ])
        if (isTRUE(verbose)) {
            message("your peak at ", ppm, " probably corresponds to ", assignation_list[1, 1], ", ", assignation_list[2, 1], " or ", assignation_list[3, 1])
            message("")
        }
        output_assignation_list <- rbind(output_assignation_list, assignation_list[seq_len(num_proposed_compounds), ])
    }
    output_assignation_list$ppm_to_assign <- rep(ppm_to_assign, each = num_proposed_compounds)

    # counts=output_assignation_list %>% dplyr::count(Metabolite) %>% dplyr::arrange(dplyr::desc(n))
    # colnames(counts) <- c("Metabolite", "Counts")
    # output_assignation_list <- merge(output_assignation_list,counts, by = "Metabolite")
    output_assignation_list <- output_assignation_list[order(output_assignation_list$ppm_to_assign, -output_assignation_list$Blood_concentration, -output_assignation_list$n_reported_in_Blood), ]
    return(output_assignation_list)
}


#' The Human Metabolome DataBase multiplet table: blood metabolites normally found in NMR-based metabolomics
#'
#' @name HMDB_blood
#' @docType data
#' @references \url{https://hmdb.ca/}
#' @keywords data
#' @examples
#' data("HMDB_blood")
#' HMDB_blood[HMDB_blood$Metabolite == "1-Methylhistidine", ]
NULL

#' NMR peak identification (urine samples)
#'
#' Identify given regions and return a data frame with plausible assignations
#' in human urine samples. The data frame contains the column "Bouatra_2013" showing if
#' the proposed metabolite was reported in this publication as regular urinary metabolite.
#'
#' @return a data frame with plausible assignations.
#'
#' @examples
#' # We identify regions from from the corresponding ppm storaged in a vector.
#' ppm_to_assign <- c(
#'     4.060960203, 3.048970634, 2.405935596,
#'     3.24146865, 0.990616851, 1.002075066, 0.955325548
#' )
#' identification <- nmr_identify_regions_urine(ppm_to_assign, num_proposed_compounds = 5)
#' @export
#' @family peak detection functions
#' @family peak integration functions
#' @param ppm_to_assign A vector with the ppm regions to assign
#' @param num_proposed_compounds set the number of proposed metabolites sorted by the number times reported in the HMDB: `HMDB_urine`.
#' @param verbose Logical value. Set it to TRUE to print additional information
nmr_identify_regions_urine <- function(ppm_to_assign, num_proposed_compounds = 5, verbose = FALSE) {
    HMDB_urine <- NULL
    utils::data("HMDB_urine", package = "AlpsNMR", envir = environment())
    output_assignation_list <- HMDB_urine[NULL, ]

    for (ppm in ppm_to_assign) {
        lower_ppm_right_edge <- ppm - 0.015
        higher_ppm_left_edge <- ppm + 0.015

        ind <- intersect(
            which(HMDB_urine$Shift_ppm < higher_ppm_left_edge),
            which(HMDB_urine$Shift_ppm > lower_ppm_right_edge)
        )
        assignation_list <- as.data.frame(HMDB_urine[ind, ])
        if (isTRUE(verbose)) {
            message("your peak at ", ppm, " probably corresponds to ", assignation_list[1, 1], ", ", assignation_list[2, 1], ", ", assignation_list[3, 1], ", ", assignation_list[4, 1], " or ", assignation_list[5, 1])
            message("")
        }
        output_assignation_list <- rbind(output_assignation_list, assignation_list[seq_len(num_proposed_compounds), ])
    }
    output_assignation_list$ppm_to_assign <- rep(ppm_to_assign, each = num_proposed_compounds)

    # counts=output_assignation_list %>% dplyr::count(Metabolite) %>% dplyr::arrange(dplyr::desc(n))
    # colnames(counts) <- c("Metabolite", "Counts")
    # output_assignation_list <- merge(output_assignation_list,counts, by = "Metabolite")
    output_assignation_list <- output_assignation_list[order(output_assignation_list$ppm_to_assign, -output_assignation_list$Urine_concentration, -output_assignation_list$n_reported_in_Urine), ]
    return(output_assignation_list)
}

#' The Human Metabolome DataBase multiplet table: urine metabolites normally found in NMR-based metabolomics
#'
#' @name HMDB_urine
#' @docType data
#' @references \url{https://hmdb.ca/}
#' @keywords data
#' @examples
#' data("HMDB_urine")
#' HMDB_urine[HMDB_urine$Metabolite == "1-Methyladenosine", ]
NULL

#' NMR peak identification (cell samples)
#'
#' Identify given regions and return a data frame with plausible assignations
#' in cell samples.
#'
#' @return a data frame with plausible assignations.
#'
#' @examples
#' # We identify regions from from the corresponding ppm storaged in a vector.
#' ppm_to_assign <- c(
#'     4.060960203, 3.048970634, 2.405935596,
#'     3.24146865, 0.990616851, 1.002075066, 0.955325548
#' )
#' identification <- nmr_identify_regions_cell(ppm_to_assign, num_proposed_compounds = 3)
#' @export
#' @family peak detection functions
#' @family peak integration functions
#' @param ppm_to_assign A vector with the ppm regions to assign
#' @param num_proposed_compounds set the number of proposed metabolites in `HMDB_cell`.
#' @param verbose Logical value. Set it to TRUE to print additional information
nmr_identify_regions_cell <- function(ppm_to_assign, num_proposed_compounds = 3, verbose = FALSE) {
    HMDB_cell <- NULL
    utils::data("HMDB_cell", package = "AlpsNMR", envir = environment())
    output_assignation_list <- HMDB_cell[NULL, ]

    for (ppm in ppm_to_assign) {
        lower_ppm_right_edge <- ppm - 0.015
        higher_ppm_left_edge <- ppm + 0.015

        ind <- intersect(
            which(HMDB_cell$Shift_ppm < higher_ppm_left_edge),
            which(HMDB_cell$Shift_ppm > lower_ppm_right_edge)
        )
        assignation_list <- as.data.frame(HMDB_cell[ind, ])
        if (isTRUE(verbose)) {
            message("your peak at ", ppm, " probably corresponds to ", assignation_list[1, 1], ", ", assignation_list[2, 1], ", ", assignation_list[3, 1])
            message("")
        }
        output_assignation_list <- rbind(output_assignation_list, assignation_list[seq_len(num_proposed_compounds), ])
    }
    output_assignation_list$ppm_to_assign <- rep(ppm_to_assign, each = num_proposed_compounds)
    output_assignation_list <- output_assignation_list[order(output_assignation_list$ppm_to_assign), ]
    return(output_assignation_list)
}


#' The Human Metabolome DataBase multiplet table: cell metabolites normally found in NMR-based metabolomics
#'
#' @name HMDB_cell
#' @docType data
#' @references \url{https://hmdb.ca/}
#' @keywords data
#' @examples
#' data("HMDB_cell")
#' HMDB_cell[HMDB_cell$Metabolite == "Acetone", ]
NULL