voom_model: Sample Eta given a voom observation model
In deruncie/MegaLMM: MegaLMM
View source: R/observation_models.R
voom_model R Documentation
Sample Eta given a voom observation model
Description
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$prec_Y.
Usage
voom_model(observation_model_parameters, MegaLMM_state = list())
Arguments
observation_model_parameters
List of parameters necessary for the data model.
Here, a Matrix of coordinates of NAs in Y
MegaLMM_state
a MegaLMM_state object. Generally, only current_state and data_matrices is used. If
empty, will return a default set of parameters of the appropriate size for model initialization.
Details
When running voom, a fully-specified fixed effect model for the data should be specified.
References
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014).
voom: precision weights unlock linear model analysis tools for RNA-seq read counts.
Genome Biology, 15(2), R29. http://doi.org/10.1186/gb-2004-5-10-r80
deruncie/MegaLMM documentation built on June 10, 2025, 7:26 p.m.
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View source: R/observation_models.R
| voom_model | R Documentation |
Sample Eta given a voom observation model
Description
voom or voomWithQualityWeights (limma) should be run on RNAseq data,
resulting in logCPM (E) and inverseWeights (weights)
for each observation. The observations (E) should be passed as Y, and the weights as
observation_model_parameters$prec_Y.
Usage
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
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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