For a given data set calculate the persample 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 Fstatistics calculation. The sample coverage adjustments are then used in makeModels for constructing the null and alternative models.
1  sampleDepth(collapsedFull, probs = c(0.5, 1), scalefac = 32, ...)

collapsedFull 
The full coverage data collapsed by sample as produced by collapseFullCoverage. 
probs 
Number(s) between 0 and 1 representing the quantile(s) of interest. For example, 0.5 is the median. 
scalefac 
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.
Leonardo ColladoTorres
Paulson, J. N., Stine, O. C., Bravo, H. C. & Pop, M. Differential abundance analysis for microbial markergene surveys. Nat. Methods (2013). doi:10.1038/nmeth.2658
collapseFullCoverage, makeModels
1 2 3 4 5 6 7  ## Collapse the coverage information
collapsedFull < collapseFullCoverage(list(genomeData$coverage),
verbose=TRUE)
## Calculate library size adjustments
sampleDepths < sampleDepth(collapsedFull, probs=c(0.5, 1), verbose=TRUE)
sampleDepths

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