Description Usage Arguments Details Value Note Author(s) See Also Examples
Reduces the amount of data in a data set without altering its overall structure
1 | simplifyFCS(g, tau, step = 1)
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g |
A vector containing the FCS data analysis |
tau |
A vector that represents the time frame between data acquisitions |
step |
A numeric value that affects the final length of the vector |
The simplifyFCS function performs a log10 weighted binning of the autocorrelation function (acf). It balance the weight of the long-time scale trending behavior of the acf curve, which commonly contain G(tau) points that fluctuate around the zero-correlation regime, hence overweighting fitting with ‘noisy data’. simplifyFCS reduce the weight of the long-time scale trending behavior (ms to sec), preserving the structure of the short-time scales.
A vector of the FCS data with reduced length
the step parameter must be between 0 and 1
Adan O. Guerrero
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | f <- Cy5_100nM$f
acqTime <- 2E-6
f <- as.vector(f)
time <- (1:length(f))*acqTime
cy5 <- data.frame(t = time, f)
g <- fcs(x = cy5$f)
len <- 1:length(g)
tau <-cy5$t[len]
G <- data.frame(tau,g)
sfcs <- simplifyFCS(G$g, G$tau, step = 0.5)
plot(sfcs$g~sfcs$tau, log = "x", type = "l",
xlab = expression(tau(s)),
ylab = expression(G(tau)), main = "Cy5")
# Comparison, original with simplify
plot(G, type = 'l', log = 'x')
lines(sfcs$g~sfcs$tau, col = "red")
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