FCSlib (version 1.3.0)

simplifyFCS: Simplify FCS

Description

Reduces the amount of data in a data set without altering its overall structure

Usage

simplifyFCS(g, tau, step = 1)

Arguments

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

Value

A vector of the FCS data with reduced length

Details

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 <U+2018>noisy data<U+2019>. simplifyFCS reduce the weight of the long-time scale trending behavior (ms to sec), preserving the structure of the short-time scales.

See Also

gcf, var, mean

Examples

# NOT RUN {
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")
# }