makeTechTrend {scran} | R Documentation

## Make a technical trend

### Description

Manufacture a mean-variance trend for log-transformed expression values, assuming Poisson or NB-distributed technical noise for count data.

### Usage

    makeTechTrend(means, size.factors=1, tol=1e-6, dispersion=0,
        pseudo.count=1, approx.npts=Inf, x=NULL, BPPARAM=SerialParam())

### Arguments

`means` | A numeric vector of average counts. Note that there are means of the counts, not means of the log-expression values.
`size.factors` | A numeric vector of size factors.
`tol` | A numeric scalar specifying the tolerance for approximating the mean/variance. Lower values result in greater accuracy.
`dispersion` | A numeric scalar specifying the dispersion for the NB distribution. If zero, a Poisson distribution is used.
`pseudo.count` | A numeric scalar specifying the pseudo-count to be added to the scaled counts before log-transformation.
`approx.npts` | An integer scalar specifying the number of interpolation points to use.
`x` | A SingleCellExperiment object from which `size.factors` and `pseudo.count` are extracted, and `means` can be automatically inferred.
`BPPARAM` | A BiocParallelParam object indicating whether and how parallelization should be performed across `means`.

### Details

At each value of `means`, this function will examine the distribution of Poisson/NB-distributed counts with the corresponding mean. All counts are log2-transformed after addition of `pseudo.count`, and the mean and variance is computed for the log-transformed values. Setting `dispersion` to a non-zero value will use a NB distribution instead of the default Poisson.

If `size.factors` is a vector, one count distribution is generated for each of its elements, where the mean is scaled by the corresponding size factor. Counts are then divided by the size factor prior to log-transformation, mimicking the effect of normalization in `normalize`. A composite distribution of log-values is constructed by pooling all of these individual distributions. The mean and variance is then computed for a composite distribution.

Finally, a function is fitted to all of the computed variances, using the means of the log-values as the covariate. Note that the returned function accepts mean log-values as input, not the mean counts that were supplied in `means`. This means that the function is directly usable as a replacement for the `trend` returned by `trendVar`.

If `x` is set, `pseudo.count` is overridden by `metadata(sce)$log.exprs.offset`; `size.factors` is overridden by `sizeFactors(sce)` (or the column sums of `counts(sce)`, if no size factors are present in `x`); and `means` is automatically determined from the range of row averages of `logcounts(sce)` (after undoing the log-transformation).

If `approx.npts` is finite and less than `length(size.factors)`, an approximate approach is used to construct the trend. We define `approx.npts` evenly spaced points (on the log-scale) between the smallest and largest size factors. Each point is treated as a proxy size factor and used to construct a count distribution as previously described. The expected log-count (and expected sum of squares) at each point is computed, and interpolation is used to obtain the corresponding values at the actual size factors. This avoids constructing separate distributions for each element of `size.factors`.

### Value

A function accepting a mean log-expression as input and returning the variance of the log-expression as the output.

### Author(s)

Aaron Lun

### See Also

`trendVar`, `normalize`

### Examples

    means <- 1:100/10
    out <- makeTechTrend(means)
    curve(out(x), xlim=c(0, 5))

[Package scran version 1.14.6 Index]