detrending: Detrend images.

In detrendr: Detrend Images

Detrend images.

Description

Correct images for bleaching (or any other effect that introduces an unwanted trend) by detrending.

Usage

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)

Arguments

img	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]).
swaps	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.
quick	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.
l	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.
purpose	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.
parallel	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.
tau	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.
cutoff	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.
degree	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.

Details

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.

• Robin Hood is a method whereby counts are taken from frames with higher mean intensity and given directly to frames of lower intensity.

• 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.

Value

The detrended image, an object of class detrended_img.

References

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.

Examples

## Not run: 
## 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)

## End(Not run)

detrendr documentation built on March 7, 2023, 7:52 p.m.