entropy is a method for objects of class LowMACA.
It calculates global entropy score of the mutation profile of the alignment
and a test for every position in the consensus comparing the number
of observed mutations against a weigthed random uniform distribution.
an object of class LowMACA
a character string or a numeric positive value representing the desired bandwith
to launch the function density for the uniform distribution.
0 will not launch density (every position is not aggregated to the surrounded ones) ,
'auto' will let the simulation decide according to the Silverman's rule of thumb
and every other number is a user defined bandwidth passed to the function
a number between 0 and 1. Represents the minimum level of conservation to test a mutation
bw overwrites the bandwidth set with
if bw is set to NULL, the method entropy uses the predefined bandwidth of the LowMACA object.
entropy returns an object of class LowMACA
updating the slot
entropy and the slot
entropy becomes a list of 6 elements:
bw the bandwidth used to calculate the null profile
uniform a function to calculate the null profile
absval absolute value of entrpy calculated
log10pval p value of the entropy test in log 10
pvalue p value of the entropy test
conservation_thr the minimum conservation level accepted
alignment is updated in the
df element by adding 6 new columns
mean a numeric vector representing the mean value of the empirical uniform function at every position in the consensus
lTsh a numeric vector representing the limit inferior of the 95% confidence interval of the empirical uniform function at every position in the consensus
uTsh a numeric vector representing the limit superior of the 95% confidence interval of the empirical uniform function at every position in the consensus
profile a numeric vector representing the density of mutations at every position in the sample normalized by the number of position. In case of bandwidth 0, this vector is equal to the number of mutations divided by the total number of mutations
pvalue a numeric vector representing the pvalue of the number of mutations found at every position against the weigthed random uniform distribution of mutations
qvalue a numeric vector representing the corrected pvalues using FDR method. Only positions with a conservation score >= 10% are considered
Stefano de Pretis , Giorgio Melloni
doi:10.1186/gm563 923 Melloni et al.: DOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes. Genome Medicine 2014 6:44
Silverman, B. W. (1986) Density Estimation. London: Chapman and Hall.
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