I am trying to work out the best way to represent weights in the newer
By weights I mean inverse variance (up to some scaling factor) for example as accepted by the
lm() function. Weights are a somewhat simplistic but general way of accounting for the varying noise levels between different observations in many types of data. I'm interested in developing generic approaches for things like visualization and principal components analysis based on this. limma closely follows the "Statistical models in S" book's approach to linear modelling, and so the limma
EList class has support for weights. This has been a quite convenient data type to use, but I'd like to follow modern Bioconductor standards if possible. limma also has an internal
getEAWP function for extracting data including weights from a variety of classes, but this doesn't look like it supports
Are there any other packages with support for weights, and what is their preferred representation?
Would a naming convention for assays be a good way to do this? For example a "log2CPM" assay could have an associated "log2CPMWeights" assay.
There is possibly also a need to clearly distinguish technical variability alone from and technical+biological variability.