score.cal {MLDA} | R Documentation |
A function to assign each feature a weighted score based on the cut-offs for methylation and unmethylation.The averaged scores for two calsses are calculated. A robust regresion model is constructed using the averaged scores for two classes. The positive and negative outliers of regression models are identified.
score.cal(mlda.obj, cutoff.obj, mlda.data, perm = 200, control = NULL)
mlda.obj |
An object from mlda() |
cutoff.obj |
An object from cutoff() |
mlda.data |
A dataset object frmo mlda.data() |
perm |
The times of sampling in the normal distribution estimated from the standerdised residuals of robust regression model construced by averaged sensitive and resistant scores |
control |
The mean and variance of normal distribution are estimated using controls of which methylations status are not changed in two classes |
Each feature was scored based on the cut-offs of log likelihood ratios for methylation and unmethylation on dye-swapped/duplicate arrays
using a weighted methylation scoring scheme. The features consistently identified as methylated candidates on dye-swapped/duplicate arrays
are scored of 1; similarly unmethylated features were scored of -1. The rest of the feature were assigned a weighted score based on their location
on the plot (see lr.plot
).
Score scheme:
f ( i, j ) = 1 i > a and j > a
f ( i, j ) = -1 i > b and j > b
f ( i, j ) = 0.45 ( i > a and 0 < j < a ) or ( 0 < i < a and j > a )
f ( i, j ) = -0.45 ( i < b and b < j < 0 ) or ( b < i < 0 and j < b )
f ( i, j ) = 0.2 ( 0 < i < a and 0 < j < a ) or ( b < i < 0 and b < j < 0 )
f ( i, j ) = 0.0001 ( 0 < i < a and b < j < 0 ) or ( b < i < 0 and 0 < j < a )
otherwise, f( i, j ) = 0
a: methylation cut-off
b: unmethylation cut-off
A list containing:
sr.obj |
A list including standardised residuals, p value for positive and negative outliers at various thresholds |
score |
A score matrix. Each feature is assigned a score. |
Wei Dai w.dai@imperial.ac.uk
MLDA
Examples are included