simplifyFCS: Simplify FCS

Description Usage Arguments Details Value Note Author(s) See Also Examples

Description

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

Usage

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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

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 ‘noisy data’. simplifyFCS reduce the weight of the long-time scale trending behavior (ms to sec), preserving the structure of the short-time scales.

Value

A vector of the FCS data with reduced length

Note

the step parameter must be between 0 and 1

Author(s)

Adan O. Guerrero

See Also

gcf, var, mean

Examples

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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")

FCSlib documentation built on Nov. 27, 2020, 5:09 p.m.

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