MRImatch
MRImatch(
data1,
data2,
mz.tol,
rt.tol = 30,
rt.error.type = c("relative", "abs"),
int.tol = 1
)data1
data2
mz.tol
rt.tol
rt.error.type
int.tol
result
#' @title reName #' @description reName #' @author Xiaotao Shen #' shenxt1990@outlook.com #' @param name name #' @return result
reName <- function(name) temp.name <- unique(name) lapply(temp.name, function(x) temp.idx <- which(x == name) if (length(temp.idx) > 1) paste(name[temp.idx], seq_len(length(temp.idx)), sep = "_") ) #' @title getBatchAlignmentInfo #' @description getBatchAlignmentInfo #' @author Xiaotao Shen #' shenxt1990@outlook.com #' @param raw.data raw.data #' @param rough.align.data rough.align.data #' @param accurate.align.data accurate.align.data #' @return result
getBatchAlignmentInfo <- function(raw.data, rough.align.data, accurate.align.data) if (length(raw.data) == 1) return("Only one batch data.") mz.error <- (rough.align.data[[2]]$mz - rough.align.data[[1]]$mz) * 10 ^ 6 / rough.align.data[[2]]$mz mz.error.sd <- sd(abs(mz.error)) rt.error <- rough.align.data[[2]]$rt - rough.align.data[[1]]$rt rt.error.sd <- sd(abs(rt.error)) int1 <- log(apply(rough.align.data[[1]][, -c(seq_len(3))], 1, function(x) mean(x, na.rm = TRUE)) + 1, 10) int2 <- log(apply(rough.align.data[[2]][, -c(seq_len(3))], 1, function(x) mean(x, na.rm = TRUE)) + 1, 10) int.error <- int2 - int1 int.error.sd <- sd(abs(int.error)) parameter.info <- paste( "The standard for m/z, RT and log10intensity errors are", round(mz.error.sd, 2), ",", round(rt.error.sd, 2), ",", 'and', round(int.error.sd, 2), ",respectively. So the tolerances for m/z, RT and log10intensity are", 4 * round(mz.error.sd, 2), ",", 4 * round(rt.error.sd, 2), ",", 'and', 4 * round(int.error.sd, 2), ",respectively." ) peak.info <- paste( "There are ", length(raw.data), " batches. And the peak number are ", paste(unlist(lapply(raw.data, nrow)), collapse = ","), " respectively. ", "After batch alignment, the peak number is ", nrow(accurate.align.data), ".", sep = "" ) return(paste(parameter.info, peak.info))