View source: R/fragment_HL.r View source: R/.ipynb_checkpoints/fragment_HL-checkpoint.r
fragment_HL | R Documentation |
fragment_HL performs the half_life fragmentation
fragment_HL makes HL_fragments based on delay_fragments and assigns all gathered information to the SummarizedExperiment object.
fragment_HL(inp, cores = 1, pen, pen_out)
inp |
SummarizedExperiment: the input data frame with correct format. |
cores |
integer: the number of assigned cores for the task. |
pen |
numeric: an internal parameter for the dynamic programming. Higher values result in fewer fragments. Default is the auto generated value. |
pen_out |
numeric: an internal parameter for the dynamic programming. Higher values result in fewer allowed outliers. Default is the auto generated value. |
The columns "HL_fragment" and "HL_mean_fragment" are added.
fragment_HL makes half-life_fragments and assigns the mean of each fragment.
The function used is:
.score_fun_ave.
The input the SummarizedExperiment object.
pen is the penalty for new fragments in the dynamic programming, pen_out is the outlier penalty.
The SummarizedExperiment object:
The bin/probe specific ID
The bin/probe specific position
The relative intensity at time point 0
An internal value to determine which fitting model is applied
Information on which fitting model is applied
The position based segment
The delay value of the bin/probe
The half-life of the bin/probe
String, the factor of TI fragment
The delay fragment the bin belongs to
The velocity value of the respective delay fragment
The vintercept of fit through the respective delay fragment
The slope of the fit through the respective delay fragment
The half-life fragment the bin belongs to
The mean half-life value of the respective half-life fragment
data(fragmentation_minimal)
fragment_HL(inp = fragmentation_minimal, cores = 2, pen = 2, pen_out = 1)
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