View source: R/fragment_inty.r View source: R/.ipynb_checkpoints/fragment_inty-checkpoint.r
fragment_inty | R Documentation |
fragment_inty performs the intensity fragmentation
fragment_inty makes intensity_fragments based on HL_fragments and assigns all gathered information to the SummarizedExperiment object.
fragment_inty(inp, cores = 1, pen, pen_out)
inp |
SummarizedExperiment: the input data frame with correct format. |
cores |
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 "intensity_fragment" and "intensity_mean_fragment" are added.
fragment_inty makes intensity_fragments and assigns the mean of each fragment.
The function used is:
.score_fun_ave.
The input is the the SummarizedExperiment object.
pen is the penalty for new fragments in the dynamic programming, pen_out is the outlier penalty.
The SummarizedExperiment object:
ID: |
The bin/probe specific ID |
position: |
The bin/probe specific position |
intensity: |
The relative intensity at time point 0 |
probe_TI: |
An internal value to determine which fitting model is applied |
flag: |
Information on which fitting model is applied |
position_segment: |
The position based segment |
delay: |
The delay value of the bin/probe |
half_life: |
The half-life of the bin/probe |
TI_termination_factor: |
String, the factor of TI fragment |
delay_fragment: |
The delay fragment the bin belongs to |
velocity_fragment: |
The velocity value of the respective delay fragment |
intercept: |
The vintercept of fit through the respective delay fragment |
slope: |
The slope of the fit through the respective delay fragment |
HL_fragment: |
The half-life fragment the bin belongs to |
HL_mean_fragment: |
The mean half-life value of the respective half-life fragment |
intensity_fragment: |
The intensity fragment the bin belongs to |
intensity_mean_fragment: |
The mean intensity value of the respective intensity fragment |
data(fragmentation_minimal)
fragment_inty(inp = fragmentation_minimal, cores = 2, pen = 2, pen_out = 1)
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