A function to test for autocorrelation of amplification curve data from a quantitative PCR experiment

autocorrelation_test is a function for an autocorrelation analysis from a quantitative PCR experiment. The result of the function is either a correlation coefficient in case the result is significant at a given significance level, or a "n.s." (non-significant) if no correlation could be determined. Noise (negative) amplification curves usually do not exhibit any autocorrelation and will therefore be "n.s.".

autocorrelation_test(y, n = 8, sig.level = 0.01,
  ns_2_numeric = FALSE)

Arguments

y	is the cycle dependent fluorescence amplitude (y-axis).
n	is the number of lagged cycles (default 12).
sig.level	is the significance level for the correlation test., Default: 0.01
ns_2_numeric,	is a logical parameter. If set TRUE, all "n.s." results will be

See also

as.zoo, lag, cor.test

Examples

# Test for autocorrelation in amplification curve data
# Load qpcR for the data
library(qpcR)
# Test for autocorrelation in the testdat data set
res_ac <- sapply(2:ncol(testdat), function(i) {
                    autocorrelation_test(testdat[, i])
                }
         )

# Plot curve data as overview
# Define the colors for the amplification curves
colors <- rainbow(ncol(testdat)-1, alpha=0.3)
# Names of samplesfile:///home/tux/R_malade
samples <- colnames(testdat)[-1]
layout(matrix(c(1,2,1,3), 2, 2, byrow = TRUE))
matplot(testdat[, 1], testdat[, -1], xlab="Cycle", ylab="RFU",
        main="testdat data set", type="l", lty=1, col=colors, lwd=2)
legend("topleft", samples, pch=19, col=colors, ncol=2, bty="n")

# Curves rated by a human after analysis of the overview. 1 = positive,
# 0 = negative
human_rating <- c(1,1,0,0,1,1,0,0,
                  1,1,0,0,1,1,0,0,
                  1,1,0,0,1,1,0,0)

# Convert the n.s. (not significant) to 0 and others to 1.
# Combine the results of the aromatic autocorrelation_test as variable "ac",
# the human rated values as variable "hr" in a new data frame (res_ac_hr).
res_ac_hr <- as.matrix(data.frame(ac=ifelse(res_ac=="n.s.", 0, 1),
                                  hr=human_rating))
res_performeR <- performeR(res_ac_hr[, "ac"], res_ac_hr[, "hr"])

# Add ratings by human and autocorrelation_test to the plot
par(las=2)
plot(1:nrow(res_ac_hr), res_ac_hr[, "hr"], xlab="Sample", ylab="Decisions",
     xaxt="n", yaxt="n", pch=19)
axis(2, at=c(0,1), labels=c("negative", "positive"), las=2)
axis(1, at=1:nrow(res_ac_hr), labels=colnames(testdat)[-1], las=2)
points(1:nrow(res_ac_hr), res_ac_hr[, "ac"], pch=1, cex=2, col="red")
legend("topleft", c("Human", "autocorrelation_test"), pch=c(19,1),
       bty="n", col=c("black","red"))

barplot(as.matrix(res_performeR[, c(1:10,12)]), yaxt="n",
        ylab="", main="Performance of autocorrelation_test")
axis(2, at=c(0,1), labels=c("0", "1"), las=2)