Correct images for bleaching (or any other effect that introduces an unwanted trend) by detrending.
img_detrend_robinhood(img, swaps = "auto", quick = FALSE)
img_detrend_rh(img, swaps = "auto", quick = FALSE)
img_detrend_boxcar(img, l, purpose = c("FCS", "FFS"), parallel = FALSE)
img_detrend_exp(
img,
tau,
cutoff = 0.05,
purpose = c("FCS", "FFS"),
parallel = FALSE
)
img_detrend_polynom(img, degree, purpose = c("FCS", "FFS"), parallel = FALSE)A 4-dimensional array in the style of an
ijtiff_img (indexed by img[y, x, channel, frame])
or a 3-dimensional array which is a single channel of an
ijtiff_img (indexed by img[y, x, frame]).
The number of swaps (giving of one count from rich to poor) to
perform during the Robin Hood detrending. Set this to "auto" (the
default) to use Nolan's algorithm to automatically find a suitable value
for this parameter (recommended). For multi-channel images, it is possible
to have a different swaps for each channel by specifying swaps as a
vector or list.
If FALSE (the default), the swap finding routine is run
several times to get a consensus for the best parameter. If TRUE, the
swap finding routine is run only once.
The length parameter for boxcar detrending. The size of the
sliding window will be 2 * l + 1. This must be a positive integer. Set
this to "auto" to use Nolan's algorithm to automatically find a suitable
value for this parameter (recommended). For multi-channel images, it is
possible to have a different l for each channel by specifying l as a
vector or list.
What type of calculation do you intend to perform on the
detrended image? If it is an FFS (fluorescence fluctuation spectroscopy)
calculation (like number and brightness), choose 'FFS'. If it is an FCS
(fluorescence correlation spectroscopy) calculation (like cross-correlated
number and brightness or autocorrelation), choose 'FCS'. The difference is
that if purpose is 'FFS', the time series is corrected for non-stationary
mean and variance, whereas if purpose is 'FCS', the time series is
corrected for non-stationary mean only. purpose is not required for
Robin Hood detrending.
Would you like to use multiple cores to speed up this
function? If so, set the number of cores here, or to use all available
cores, use parallel = TRUE.
The \(tau\) parameter for exponential filtering detrending.
This must be a positive number. Set this to "auto" to use Nolan's algorithm
to automatically find a suitable value for this parameter (recommended).
For multi-channel images, it is possible to have a different tau for each
channel by specifying tau as a vector or list.
In exponential filtering detrending, for the weighted
average, every point gets a weight. This can slow down the computation
massively. However, many of the weights will be approximately zero. With
cutoff, we say that any point with weight less than or equal to cutoff
times the maximum weight may be ignored; so with cutoff = 0.05, any
weight less than 5\
value of this parameter is sensible and its value should not be set to
anything else without good reason.
The degree of the polynomial to use for the polynomial
detrending. This must be a positive integer. Set this to "auto" to use
Nolan's algorithm to automatically find a suitable value for this parameter
(recommended). For multi-channel images, it is possible to have a different
degree for each channel by specifying degree as a vector or list.
The detrended image, an object of class detrended_img.
There are 4 detrending methods available: Robin Hood, boxcar, exponential filtering and polynomial. Robin Hood is described in Nolan et al., 2018. The others are described in Nolan et al., 2017.
Boxcar detrending with parameter \(l\) is a moving average detrending method using a sliding window of size \(2l + 1\).
Exponential filtering detrending is a moving weighted average method where for parameter \(tau\) the weights are calculated as exp\((- t / tau)\) where \(t\) is the distance from the point of interest.
Polynomial detrending works by fitting a polynomial line to a series of points and then correcting the series to remove the trend detailed by this polynomial fit.
Rory Nolan, Luis A. J. Alvarez, Jonathan Elegheert, Maro Iliopoulou, G. Maria Jakobsdottir, Marina Rodriguez-Muñoz, A. Radu Aricescu, Sergi Padilla-Parra; nandb—number and brightness in R with a novel automatic detrending algorithm, Bioinformatics, https://doi.org/10.1093/bioinformatics/btx434.
if (FALSE) { # \dontrun{
## These examples are not run on CRAN because they take too long.
## You can still try them for yourself.
img <- ijtiff::read_tif(system.file("extdata", "bleached.tif",
package = "detrendr"
))
corrected <- img_detrend_rh(img)
corrected <- img_detrend_boxcar(img, "auto", purpose = "fcs", parallel = 2)
corrected10 <- img_detrend_boxcar(img, 10, purpose = "fcs", parallel = 2)
corrected50 <- img_detrend_boxcar(img, 50, purpose = "fcs", parallel = 2)
corrected <- img_detrend_exp(img, "auto", purpose = "ffs", parallel = 2)
corrected10 <- img_detrend_exp(img, 10, purpose = "ffs", parallel = 2)
corrected50 <- img_detrend_exp(img, 50, purpose = "fcs", parallel = 2)
corrected <- img_detrend_polynom(img, "auto", purpose = "ffs", parallel = 2)
corrected2 <- img_detrend_polynom(img, 2, purpose = "ffs", parallel = 2)
} # }