Calculate adjustments for library size
For a given data set calculate the per-sample coverage adjustments. Hector Corrada's group proposed calculating the sum of the coverage for genes below a given sample quantile. In this function, we calculate the sample quantiles of interest by sample, and then the sum of the coverage for bases below or equal to quantiles of interest. The resulting values are transformed log2(x + scalefac) to avoid very large numbers that could potentially affect the stability of the F-statistics calculation. The sample coverage adjustments are then used in makeModels for constructing the null and alternative models.
The full coverage data collapsed by sample as produced by collapseFullCoverage.
Number(s) between 0 and 1 representing the quantile(s) of interest. For example, 0.5 is the median.
Number added to the sample coverage adjustments before the log2 transformation.
Arguments passed to other methods and/or advanced arguments. Advanced arguments:
A matrix (vector of
length(probs) == 1) with the library size depth
adjustments per sample to be used in makeModels. The number of rows
corresponds to the number of quantiles used for the sample adjustments.
Paulson, J. N., Stine, O. C., Bravo, H. C. & Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nat. Methods (2013). doi:10.1038/nmeth.2658
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