Remove noise from genomic data smoothing and cleaning the observed signal. This function doesn't alter the shape or the values of the signal as much as the traditional method of sliding window average does, providing a great correlation within the original and filtered data (>0.99).
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filterFFT(data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, ...) ## S4 method for signature 'SimpleRleList' filterFFT(data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, mc.cores = 1, ...) ## S4 method for signature 'Rle' filterFFT(data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, ...) ## S4 method for signature 'list' filterFFT(data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, mc.cores = 1, ...) ## S4 method for signature 'numeric' filterFFT(data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, ...)
Coverage or intensities values representing the results of the
NGS of TA experiment. This attribute could be a individual vector
representing a chromosome (
Number of components to select, in percentage respect total length of the sample. Allowed values are numeric (in range 0:1) for manual setting or "auto" for automatic detection. See details.
Plot the Power Spectrum of the Fast Fourier Transform to visually identify the selected components (see details).
This function implements tweaks to a standard fft call to
improve (dramatically) the performance in large genomic data. These
optimizations can be bypassed by setting this parameter to
Other parameters to be passed to
If multiple cores are available, maximum number of them to
use for parallel processing of
Fourier-analysis principal components selection is widely used in signal processing theory for an unbiased cleaning of a signal over the time.
Other procedures, as the traditional sliding window average, can change too much the shape of the results in function of the size of the window, and moreover they don't only smooth the noise without removing it.
With a Fourier Transform of the original signal, the input signal is descomposed in diferent wavelets and described as a combination of them. Long frequencies can be explained as a function of two ore more periodical shorter frequecies. This is the reason why long, unperiodic sequences are usually identified as noise, and therefore is desireable to remove them from the signal we have to process.
This procedure here is applied to genomic data, providing a novel method to obtain perfectly clean values wich allow an efficient detection of the peaks which can be used for a direct nucleosome position recognition.
This function select a certain number of components in the original power
spectrum (the result of the Fast Fourier Transform which can be seen with
showPowerSpec=TRUE) and sets the rest of them to 0 (component knock-out).
The amout of components to keep (given as a percentage of the input lenght)
can be set by the
pcKeepComp. This will select the first components of
the signal, knock-outing the rest. If this value is close to 1, more
components will be selected and then more noise will be allowed in the
output. For an effective filtering which removes the noise keeping almost
all relevant peaks, a value between 0.01 and 0.05 is usually sufficient.
Lower values can cause merging of adjacent minor peaks.
This library also allows the automatic detection of a fitted value for
pcKeepComp. By default, if uses the
pcKeepCompDetect function, which
looks which is the minimum percentage of components than can reproduce
the original signal with a corelation between the filtered and the original
one of 0.99. See the help page of
pcKeepCompDetect for further details and
reference of available parameters.
One of the most powerful features of
nucleR is the efficient
implementation of the FFT to genomic data. This is achived trought few
tweaks that allow an optimum performance of the Fourier Transform. This
includes a by-range filtering, an automatic detection of uncovered regions,
windowed execution of the filter and padding of the data till nearest power
of 2 (this ensures an optimum case for FFT due the high factorization of
components). Internal testing showed up that in specific datasets, these
optimizations lead to a dramatic improvement of many orders of magnitude
(from 3 days to few seconds) while keeping the correlation between the
fft call and our
filterFFT higher than 0.99. So, the use of these
optimizations is highly recomended.
If for some reason you want to apply the function without any kind of
optimizations you can specify the parameter
useOptim=FALSE to bypass them
and get the pure knockout inverse from native FFT call. All other parameters
can be still applyied in this case.
Numeric vector with cleaned/smoothed values
Smith, Steven W. (1999), The Scientist and Engineer's Guide to Digital Signal Processing (Second ed.), San Diego, Calif.: California Technical Publishing, ISBN 0-9660176-3-3 (availabe online: http://www.dspguide.com/pdfbook.htm)
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# Load example data, raw hybridization values for Tiling Array raw_data <- get(data(nucleosome_tiling)) # Filter data fft_data <- filterFFT(raw_data, pcKeepComp=0.01) # See both profiles library(ggplot2) plot_data <- rbind( data.frame(x=seq_along(raw_data), y=raw_data, intensities="raw"), data.frame(x=seq_along(fft_data), y=fft_data, intensities="filtered") ) qplot(x=x, y=y, data=plot_data, geom="line", xlab="position", ylab="intensities") + facet_grid(intensities~.) # The power spectrum shows a visual representation of the components fft_data <- filterFFT(raw_data, pcKeepComp=0.01, showPowerSpec=TRUE)
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