R/snpinfo.R

In byandell/CCSanger: Utilities to get and use Sanger data for Collaborative Cross

#' Get SNP and InDel information in window around peak
#'
#' Get SNP and InDel information from SQLite databases within \code{window_Mbp} of \code{peak_Mbp} on \code{chri_id}
#'
#' @param chr_id chromosome identifier
#' @param peak_Mbp position in Mbp of peak
#' @param window_Mbp half-width of \code{window} around \code{peak_Mbp}
#' @param datapath path to Derived Data
#' @param info_type type of info: one of c("snps","indels")
#'
#' @return table of class \code{snpinfo} of top_snps near maximum lod
#'
#' @author Brian S Yandell, \email{brian.yandell@@wisc.edu}
#' @keywords utilities
#'
#' @examples
#' \dontrun{get_snpinfo(chr_id, peak_Mbp, window_Mbp)}
#'
#' @export
#' @importFrom dplyr collect filter src_sqlite tbl
get_snpinfo <- function(chr_id, peak_Mbp, window_Mbp,
                        datapath, info_type = c("snps","indels")) {
  start_val <- peak_Mbp - window_Mbp
  end_val <- peak_Mbp + window_Mbp
  ## identify table in SQLite, and filter to chr and bp interval.
  info_type <- match.arg(info_type)
  file.sqlite <- paste0("ccfounder", info_type, ".sqlite")
  my_db <- dplyr::src_sqlite(file.path(datapath, file.sqlite))
  snpinfo <- dplyr::filter(
    dplyr::tbl(my_db, info_type),
    chr == chr_id,
    pos_Mbp >= start_val,
    pos_Mbp <= end_val)
  ## Collect from database.
  snpinfo <- dplyr::collect(snpinfo, n=1e+6)
  class(snpinfo) <- c("snpinfo", class(snpinfo))
  snpinfo
}
#' Summary of snpinfo object
#'
#' @param object of class \code{near_snps}
#' @param ... other arguments not used
#'
#' @return table summary
#'
#' @author Brian S Yandell, \email{brian.yandell@@wisc.edu}
#' @keywords utilities
#'
#' @examples
#' \dontrun{summary(object)}
#'
#' @method summary snpinfo
#' @rdname snpinfo
#' @export
#' @importFrom dplyr arrange desc group_by mutate summarize ungroup
summary.snpinfo <- function(object, ...) {
  dplyr::arrange(
    dplyr::mutate(
      dplyr::ungroup(
        dplyr::summarize(
          dplyr::group_by(object, sdp),
          pct = round(100 * n() / nrow(snpinfo), 2),
          min_Mbp = min(pos_Mbp),
          max_Mbp = max(pos_Mbp))),
      pattern = sdp_to_pattern(sdp)),
    dplyr::desc(pct))
}