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 Eric Coissac committed Jul 25, 2018 1 2 3 4 5 6 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.PCRplate.R \name{plot.PCRplate} \alias{plot.PCRplate} \title{Plot PCR plates} \usage{  Eric Coissac committed Mar 13, 2019 7 8 \method{plot}{PCRplate}(x, samples = NULL, col = "cyan2", different = T, ...)  Eric Coissac committed Jul 25, 2018 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 } \arguments{ \item{x}{a \code{\link{metabarcoding.data}} object} \item{samples}{a character vector containing names of problematic samples. Default is \code{NULL}} \item{different}{a boolean indicating whether different tags where used in forward and reverse to identify samples. Default is \code{TRUE}} \item{...}{arguments ot be passed to methods, such as graphical parameters} } \value{ \code{\link{plot.PCRplate}} returns a plot displaying no more than 4 PCR plates, with problematic sample localization } \description{ Plots samples localization in PCR plates, and points out problematic samples if provided. } \examples{ \dontshow{# switch the working directory to the data package directory} \dontshow{setwd(system.file("extdata", package="ROBITools"))} data(termes) # reading the termes_ngsfilt.txt file termes.ngs=import.ngsfilter.data('termes_ngsfilt.txt', platewell="position") # including ngsfilter data into termes data attr(termes, "samples") = termes.ngs[rownames(termes),] #plot PCR plate plan col = rep("green", nrow(termes)) col[grep("r", rownames(termes))] = "red" plot.PCRplate(termes, col=col) #highlighting location of samples with low identification score #low quality taxonomic assignements identification library(plotrix) weighted.hist(termes$motus$best_identity, colSums(termes$reads), breaks = 20, ylab = "Nb reads", xlab = "Ecotag scores", xaxis=F) axis(1, labels = T) lowqual.seq = rownames(termes$motus)[termes$motus$best_identity < 0.7] #identification and localization (in PCR plate) of samples with high proportions of low quality taxonomic assignements termes.freq= normalize(termes, MARGIN=1)\$reads hist(log10(rowSums(termes.freq[,lowqual.seq]) + 1e-05), breaks = 20, xlab = "Prop low quality reads") lowqual.sample = rownames(termes)[log10(rowSums(termes.freq[, lowqual.seq]) + 1e-05) > -0.5] plot.PCRplate(termes, lowqual.sample, col=col) } \seealso{ \code{\link{import.metabarcoding.data}} } \author{ Lucie Zinger } \keyword{DNA} \keyword{metabarcoding}