Sample Eta given a voom observation model

voom or voomWithQualityWeights (limma) should be run on RNAseq data, resulting in logCPM (E) and inverseWeights (weights) for each observation. The observations (E) should be passed as Y, and the weights as observation_model_parameters$prec_Y.

voom_model(observation_model_parameters, MegaLMM_state = list())

Arguments

observation_model_parameters

List of parameters necessary for the data model. Here, a Matrix of coordinates of NAs in Y

MegaLMM_state

a MegaLMM_state object. Generally, only current_state and data_matrices is used. If empty, will return a default set of parameters of the appropriate size for model initialization.

Details

When running voom, a fully-specified fixed effect model for the data should be specified.

References

Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 15(2), R29. http://doi.org/10.1186/gb-2004-5-10-r80