Mean-Variance_Gamma_Regressions | R Documentation |
fit_gamma_regressions
is a wrapper function that calls both
fit_gamma_imputation
and
fit_gamma_weights
. It returns a list containing
the models for imputation in $imputation
and the weights in $weights
.
fit_gamma_imputation
returns a list named according to the different
conditions and fit_gamma_weights
returns a glm
object containing the
gamma regression for the mean-variance trend.
fit_gamma_regressions(data, design, id_col = "id") fit_gamma_imputation(data, design, id_col = "id") fit_gamma_weights(data, design, id_col = "id")
data |
a |
design |
a design or model matrix as produced by
|
id_col |
a character for the name of the column containing the name of the features in data (e.g., peptides, proteins, etc.). |
fit_gamma_imputation
returns a named
list where the names corresponds to the conditions. Each index contains
a glm
object with the gamma regression for the mean-variance trend.
fit_gamma_weights
returns a glm
object with the gamma regression
for the precision weights. fit_gamma_regressions
returns a list with
the results from fit_gamma_imputation
in $imputation
and the results
from fit_gamma_weights
in $weights
.
fit_gamma_regressions
: Wrapper function that runs both
fit_gamma_imputation
and fit_gamma_weights
fit_gamma_imputation
: Function that generates per
condition mean-variance trends used in the imputation procedure.
Each id in the id_col
gets one mean and variance calculated for each
condition. One model is then fitted per condition.
fit_gamma_weights
: Function to produce the
mean-variance trend used to calculate the precision weights used in
lmFit
. Each id in the id_col
gets one mean and one
variance across all conditions and one model is then fitted for all
mean-variance pairs.
# Generate a design matrix for the data design <- model.matrix(~ 0 + factor(rep(1:2, each = 3))) # Set correct colnames, this is important for fit_gamma_* colnames(design) <- paste0("ng", c(50, 100)) # Normalize and log-transform the data yeast <- psrn(yeast, "identifier") # Fit all gamma regression models for the mean-variance trends all_gamma_models <- fit_gamma_regressions(yeast, design, "identifier") # Fit the gamma regression models for the mean-variance trend used in the # imputation procedure gamma_imputation_models <- fit_gamma_imputation(yeast, design, "identifier") # Fit the gamma regression model for the mean-variance trend used for # estimating the precision weights used in limma gamma_weight_model <- fit_gamma_weights(yeast, design, "identifier")
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