## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----------------------------------------------------------------------------- head(list.files()) ## ----eval = FALSE------------------------------------------------------------- # metdata = read.delim("path_to_data_file.txt", sep = "\t", header = TRUE, # stringsAsFactors = FALSE) ## ----setup, message = FALSE--------------------------------------------------- #loading package library(metabCombiner) data("plasma30") data("plasma20") ## ----------------------------------------------------------------------------- #header names of plasma dataset names(plasma20) ## ----eval = FALSE------------------------------------------------------------- # p20 = metabData(plasma20, mz = "mz", rt = "rt", id = "id", adduct = "adduct", # samples = "CHEAR.20min",...) # # ##all of the following values for id argument give the same result # p20 = metabData(plasma20, ..., id = "identity", ...) #full column name # p20 = metabData(plasma20, ..., id = "^id", ...) #column names starting with id # # #any one of these three keywords # p20 = metabData(plasma20, ..., id = c("compound,identity,name"),...) # # ##all of the following inputs for samples argument give the same result # p20 = metabData(plasma20, samples = c("CHEAR.20min.1, CHEAR.20min.2, # CHEAR.20min.3, CHEAR.20min.4, CHEAR.20min.5") # # p20 = metabData(plasma20, samples = names(plasma20)[6:10], ...) # # #recommended: use a keyword common and exclusive to sample names of interest # p20 = metabData(plasma20, ..., samples = "CHEAR", ...) # p20 = metabData(plasma20, ..., samples = "CH", ...) ## ----eval = FALSE------------------------------------------------------------- # p20 = metabData(plasma20, mz = "mz", rt = "rt", id = "id", adduct = "adduct", # samples = "CHEAR", extra = c("Red", "POOL", "Blank"),...) # # getSamples(p20) #should return column names containing "CHEAR" # getExtra(p20) #should return column containing "Red Cross", "POOL", "Blank" ## ----------------------------------------------------------------------------- head(sort(plasma20$rt), 10) tail(sort(plasma20$rt), 10) ## ----------------------------------------------------------------------------- p20 <- metabData(table = plasma20, mz = "mz", rt = "rt", id = "identity", adduct = "adduct", samples = "CHEAR", extra = c("Red", "POOL"), rtmin = "min", rtmax = 17.25, measure = "median", zero = FALSE, duplicate = c(0.0025, 0.05)) ## ----------------------------------------------------------------------------- p30 <- metabData(table = plasma30, samples = "Red", extra = c("CHEAR", "POOL", "Blank")) getSamples(p30) ##should print out red cross sample names getExtra(p30) ##should print out extra sample names getStats(p30) ##prints a list of dataset statistics print(p30) ##object summary ## ----------------------------------------------------------------------------- p.combined = metabCombiner(xdata = p30, ydata = p20, binGap = 0.0075) ## ----------------------------------------------------------------------------- p.results = combinedTable(p.combined) names(p.results)[1:15] ## ----fig.width= 5, fig.height=4, fig.align='center'--------------------------- p.combined.2 = selectAnchors(p.combined, windx = 0.03,windy = 0.02, tolQ = 0.3, tolmz = 0.003, tolrtq = 0.3, useID = FALSE) a = getAnchors(p.combined.2) plot(a$rtx, a$rty, main = "Fit Template", xlab = "rtx", ylab = "rty") ## ----------------------------------------------------------------------------- set.seed(100) #controls cross validation pseudo-randomness p.combined.3 = fit_gam(p.combined.2, useID = FALSE, k = seq(12,20,2), iterFilter = 2, coef = 2, prop = 0.5, bs = "bs", family = "gaussian", m = c(3,2)) ## ----fig.width= 5, fig.height=4, fig.align='center'--------------------------- plot(p.combined.3, main = "Example metabCombiner Plot", xlab = "P30 RTs", ylab = "P20 RTs", lcol = "blue", pcol = "black", lwd = 3, pch = 19, outlier = "highlight") grid(lty = 2, lwd = 1) ## ----------------------------------------------------------------------------- p.combined.4 = calcScores(p.combined.3, A = 70, B = 15, C = 0.5, usePPM = FALSE, useAdduct = FALSE, groups = NULL) ## ----------------------------------------------------------------------------- scores = evaluateParams(p.combined.3, A = seq(50, 120, 10), B = 5:15, C = seq(0,1,0.1), usePPM = FALSE, minScore = 0.5, penalty = 10) head(scores) ## ----------------------------------------------------------------------------- combined.table = combinedTable(p.combined.4) ##version 1: score-based conflict detection combined.table.2 = labelRows(combined.table, minScore = 0.5, maxRankX = 3, maxRankY = 3, method = "score", delta = 0.2, remove = FALSE, balanced = TRUE) ##version 2: mzrt-based conflict detection combined.table.3 = labelRows(combined.table, minScore = 0.5, maxRankX = 3, maxRankY = 3, method = "mzrt", balanced = TRUE, delta = c(0.003,0.5,0.003,0.2)) ## ----eval = FALSE------------------------------------------------------------- # write2file(combined.table, file = "Combined.Table.Report.txt", sep = "\t")