Using estimated k-mer level affinities returned by kmerFit, this
function tests for differential specificities across conditions for each k-mer
separately. The call to kmerFit must have been made with
contrasts = TRUE. Here, shared specificity is defined as a common ordering of
k-mer affinities across conditions. Therefore, differential specificity is tested by
assessing the statistical significance of deviations from a common trend
of k-mer affinities observed between conditions.
For each condition, the common trend is estimated by fitting either a LOESS curve
or B-spline smoother to the corresponding columns of the "contrastAverage" (x)
and "contrastDifference" (y) assays of the input SummarizedExperiment object.
If useref = TRUE, instead of the "contrastAverage", the
"affinityEstimate" values of the reference condition are used as the x-axis.
The difference between the observed differential affinity and the
estimated trend is taken as the estimate of differential specificity.
When method = "subset" (default), two trends are fit using weighted
orinary linear regression and a B-spline basis. The two trends are meant to capture
any differential specificities that may be smoothed out by fitting a single trend
to the data. The two trends are identified by first splitting the data along
the x-axis into 20 equal-width bins and fitting a
two-component normal mixture to the "contrastDifference" values of k-mers
in each bin. The upper and lower components in each cluster are grouped across
the bins. The two trends are fit separately by weighing each k-mer based
on the likelihood of belonging to either the upper or lower clusters. Finally,
a single trend is selected to be used for testing by taking the trend which
minimizes the mean residual across the top 50% of k-mers.
After estimating k-mer level affinities using probe-set aggregate, this function tests for differential specificity between conditions.
kmerTestSpecificity( se, method = c("subset", "bs", "loess"), span = 0.05, useref = TRUE, ... )
| se | SummarizedExperiment of k-mer results from |
|---|---|
| method | string value specifying method to use for fitting
specificity trend. Must be one of |
| span | span parameter to be passed to |
| useref | logical value whether to fit specificity trend as function of
reference intensities rather than the average of reference and
variant intensities. Must be TRUE if |
| ... | other parameters to pass to |
SummarizedExperiment of k-mer differential specificity results with the following assays:
"contrastAverage": input k-mer average affinities.
"contrastDifference": input k-mer differential affinities.
"contrastVariance": input k-mer differential affinity variances.
"contrastFit": estimated specificity trend.
"contrastResidual": residual from estimated specificity trend (contrastDifference - contrastFit).
"specificityZ": studentized differences (contrastResidual / sqrt(contrastVariance)).
"specificityP": two-sided tail p-values for studentized differences.
"specificityQ": FDR-controlling Benjamini-Hochberg adjusted p-values.