Description Usage Arguments Details Value

Dynamic Noise filtration

1 2 | ```
dynamicNoiseFilter(spectrum.df = NULL, DNF = 2, minPeaks = 5,
minInt = 100)
``` |

`spectrum.df` |
a dataframe or matrix with two columns: 1. Mass/ Mass-to-charge ratio 2. Intensity |

`DNF` |
dynamic noise filter minimum signal to noise threshold (default = 2), calculated as the ratio between the linear model predicted intensity value and the actual intensity. |

`minPeaks` |
minimum number of signal peaks following dynamic noise filtration (default = 5). |

`minInt` |
integer minimum dynamic noise filter |

Dynamic noise filter adapted from the method described in Xu H. and Frietas M. 'A Dynamic Noise Level Algorithm for Spectral Screening of Peptide MS/MS Spectra' 2010 BMC Bioinformatics. The function iteratively calculates linear models starting from the median value of the lower half of all intensities in the spectrum.df. The linear model is used to predict the next peak intensity and ratio is calculated between the predicted and actual intensity value. Assuming that all preceeding intensities included in the linear model are noise, the signal to noise ratio between the predicted and actual values should exceed the minimum signal to noise ratio (default DNF = 2). The function continues until either the DNF value minimum has been exceeded and is also below the maxPeaks or maximum number of peaks value. As the function must necessarily calculate potentially hundreds of linear models the RcppEigen package is used to increase the speed of computation.

a list containing 3 objects: 1. Above.noise The dynamic noise filtered matrix/ dataframe 2. metaData a dataframe with the following column names: 1. Noise.level the noise level determined by the dynamic noise filter function. 2. IntCompSpec Total intensity composite spectrum. 3. TotalIntSNR Sparse ion signal to noise ratio (mean intensity/ stdev intensity) 4. nPeaks number of peaks in composite spectrum 3. aboveMinPeaks Logical are the number of signals above the minimum level

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