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Detrend images.

Source: R/detrend.R
detrending.Rd

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.

Value

The detrended image, an object of class detrended_img.

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.

  • 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.

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

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)
} # }