pcKeepCompDetect {nucleR} | R Documentation

## Auto detection of a fitted `pcKeepComp` param for filterFFT function

### Description

This function tries to obtain the minimum number of components needed in a FFT filter to achieve or get as close as possible to a given correlation value. Usually you don't need to call directly this function, is used in `filterFFT` by default.

### Usage

    pcKeepCompDetect(data, pc.min = 0.01, pc.max = 0.1, max.iter = 20,
      verbose = FALSE, cor.target = 0.98, cor.tol = 0.001, smpl.num = 25,
      smpl.min.size = 2^10, smpl.max.size = 2^14)

### Arguments

`data` | Numeric vector to be filtered
`pc.min, pc.max` | Range of allowed values for `pcKeepComp` (minimum and maximum), in the range 0:1.
`max.iter` | Maximum number of iterations
`verbose` | Extra information (debug)
`cor.target` | Target correlation between the filtered and the original profiles. A value around 0.99 is recommeded for Next Generation Sequencing data and around 0.7 for Tiling Arrays.
`cor.tol` | Tolerance allowed between the obtained correlation an the target one.
`smpl.num` | If `data` is a large vector, some samples from the vector will be used instead the whole dataset. This parameters tells the number of samples to pick.
`smpl.min.size, smpl.max.size` | Minimum and maximum size of the samples. This is used for selection and sub-selection of ranges with meaningful values (i,e, different from 0 and NA). Power of 2 values are recommended, despite non-mandatory.
`...` | Parameters to be pass to `autoPcKeepComp`

### Details

This function predicts a suitable `pcKeepComp` value for `filterFFT` function. This is the recommended amount of components (in percentage) to keep in the `filterFFT` function to obtain a correlation of (or near of) `cor.target`.

The search starts from two given values `pc.min`, `pc.max` and uses linial interpolation to quickly reach a value that gives a corelation between the filtered and the original near `cor.target` within the specified tolerance `cor.tol`.

To allow a quick detection without an exhaustive search, this function uses a subset of the data by randomly sampling those regions with meaningful coverage values (i,e, different from 0 or NA) larger than `smpl.min.size`. If it's not possible to obtain `smpl.max.size` from this region (this could be due to flanking 0's, for example) at least `smpl.min.size` will be used to check correlation. Mean correlation between all sampled regions is used to test the performance of the `pcKeepComp` parameter.

If the number of meaningful bases in `data` is less than `smpl.min.size * (smpl.num/2)` all the `data` vector will be used instead of using sampling.

### Value

Fitted `pcKeepComp` value

### Author(s)

Oscar Flores oflores@mmb.pcb.ub.es, David Rosell david.rosell@irbbarcelona.org

### Examples

    # Load dataset
    data(nucleosome_htseq)
    data <- as.vector(coverage.rpm(nucleosome_htseq)[[1]])

    # Get recommended pcKeepComp value
    pckeepcomp <- pcKeepCompDetect(data, cor.target=0.99)
    print(pckeepcomp)

    # Call filterFFT
    f1 <- filterFFT(data, pcKeepComp=pckeepcomp)

    # Also this can be called directly
    f2 <- filterFFT(data, pcKeepComp="auto", cor.target=0.99)

    # Plot
    library(ggplot2)
    i <- 1:2000
    plot_data <- rbind(
        data.frame(x=i, y=data[i], coverage="original"),
        data.frame(x=i, y=f1[i], coverage="two calls"),
        data.frame(x=i, y=f2[i], coverage="one call")
    )
    qplot(x=x, y=y, color=coverage, data=plot_data, geom="line",
      xlab="position", ylab="coverage")

[Package nucleR version 2.12.1 Index]