Define null data simulation function #6
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Qualify namespace in function calls in `R/simulate.null.R`.
Add example to function `simulate.null()`, and update the description of the return value.
Add `num.null` as a parameter to the function `detect.outliers()` to give users control over how many null transcripts to simulate.
Add code to simulate null transcripts in the function `detect.outliers()`, and include the resulting matrix in the list of values to return.
Use `truncnorm::rtruncnorm()` rather than `extraDistr::rtnorm()` in `simulate.null()`. The two functions appear to be equivalent, and doing so avoids adding the package extraDistr as a dependency.
Add code to `simulate.null()` to avoid errors handling data frame input.
Add script `R/add.residual.noise.R` with first draft of function `add.residual.noise()`.
Draft an alternative function definition, tentatively called `simulate.null2()`, for simulating null transcripts. This function combines the computations from the original `simulate.null()` function with those of the new `add.residual.noise()` function, with the benefit being that we ultimately end up with one `num.null` by `ncol(data)` matrix rather than two, thereby relieving memory constraints.
Replace the original definition of the function `simulate.null()` with the new definition. Update documentation and examples.
Use the new definition of the function `simulate.null()` in the main `detect.outliers()` function.
Delete script `R/add.residual.noise.R`, whose functionality has been incorporated into `R/simulate.null.R`.
Add transcript names to `null.data` in function `detect.outliers()`.
Determine optimal distribution of each null transcript in function `detect.outliers()`.
Calculate outlier statistics on null data in function `detect.outliers()`. To optimize memory use, overwrite the vectors used for the observed data rather than create new vectors.
Combine outlier statistics from null transcripts into a matrix, and calculate the rank product for each null transcript.
Add new objects to the list returned by the function `detect.outliers()`.
Don't compute ranks of null data in function `detect.outliers()`. Rather, ranks are computed as each observed transcript is added to the null data.
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This PR adds functionality for simulating null data. It adds the following new script and function:
R/simulate.null.R/simulate.null()Unlike in the original code,
simulate.null()combines the creation of a 'raw' null transcript with the creation of noise and directly adds the two together rather than creating a matrix of 'raw' transcripts, a matrix of noise, and adding the two; this should reduce memory consumption. It accomplishes this by taking as inputs a vector of transcripts, the code indicating the optimal distribution for this transcript, the corresponding vector of (trimmed) residuals, and the code indicating the optimal distribution for those residuals. It creates the 'raw' null transcript, the noise, and returns the sum of the two.This PR also incorporates the creation of simulated data into the main
detect.outliers()function. It adds a parameternum.nullspecifying the number of null transcripts to simulate. It samplesnum.nulltranscripts from the observed data and passes each one tosimulate.null()along with the corresponding residual and the codes indicating the optimal distributions of the transcript and its residual. It combines the null transcripts into a matrix. It also determines the optimal distribution for each null transcript. Finally, it calculates outlier statistics on the null data, and adds quantities to the output list for inspection.