Low-level functions to fit dispersion estimates
Normal users should instead use estimateDispersions.
These low-level functions are called by estimateDispersions,
but are exported and documented for non-standard usage.
For instance, it is possible to replace fitted values with a custom fit and continue
with the maximum a posteriori dispersion estimation, as demonstrated in the
examples below.
estimateDispersionsGeneEst(
object,
minDisp = 1e-08,
kappa_0 = 1,
dispTol = 1e-06,
maxit = 100,
useCR = TRUE,
weightThreshold = 0.01,
quiet = FALSE,
modelMatrix = NULL,
niter = 1,
linearMu = NULL,
minmu = if (type == "glmGamPoi") 1e-06 else 0.5,
alphaInit = NULL,
type = c("DESeq2", "glmGamPoi")
)
estimateDispersionsFit(
object,
fitType = c("parametric", "local", "mean", "glmGamPoi"),
minDisp = 1e-08,
quiet = FALSE
)
estimateDispersionsMAP(
object,
outlierSD = 2,
dispPriorVar,
minDisp = 1e-08,
kappa_0 = 1,
dispTol = 1e-06,
maxit = 100,
useCR = TRUE,
weightThreshold = 0.01,
modelMatrix = NULL,
type = c("DESeq2", "glmGamPoi"),
quiet = FALSE
)
estimateDispersionsPriorVar(object, minDisp = 1e-08, modelMatrix = NULL)object |
a DESeqDataSet |
minDisp |
small value for the minimum dispersion, to allow for calculations in log scale, one order of magnitude above this value is used as a test for inclusion in mean-dispersion fitting |
kappa_0 |
control parameter used in setting the initial proposal in backtracking search, higher kappa_0 results in larger steps |
dispTol |
control parameter to test for convergence of log dispersion, stop when increase in log posterior is less than dispTol |
maxit |
control parameter: maximum number of iterations to allow for convergence |
useCR |
whether to use Cox-Reid correction |
weightThreshold |
threshold for subsetting the design matrix and GLM weights for calculating the Cox-Reid correction |
quiet |
whether to print messages at each step |
modelMatrix |
for advanced use only, a substitute model matrix for gene-wise and MAP dispersion estimation |
niter |
number of times to iterate between estimation of means and estimation of dispersion |
linearMu |
estimate the expected counts matrix using a linear model, default is NULL, in which case a lienar model is used if the number of groups defined by the model matrix is equal to the number of columns of the model matrix |
minmu |
lower bound on the estimated count for fitting gene-wise dispersion |
alphaInit |
initial guess for the dispersion estimate, alpha |
type |
can either be "DESeq2" or "glmGamPoi". Specifies if the glmGamPoi package is used to calculate the dispersion. This can be significantly faster if there are many replicates with small counts. |
fitType |
either "parametric", "local", "mean", or "glmGamPoi"
for the type of fitting of dispersions to the mean intensity.
See |
outlierSD |
the number of standard deviations of log gene-wise estimates above the prior mean (fitted value), above which dispersion estimates will be labelled outliers. Outliers will keep their original value and not be shrunk using the prior. |
dispPriorVar |
the variance of the normal prior on the log dispersions. If not supplied, this is calculated as the difference between the mean squared residuals of gene-wise estimates to the fitted dispersion and the expected sampling variance of the log dispersion |
a DESeqDataSet with gene-wise, fitted, or final MAP dispersion estimates in the metadata columns of the object.
estimateDispersionsPriorVar is called inside of estimateDispersionsMAP
and stores the dispersion prior variance as an attribute of
dispersionFunction(dds), which can be manually provided to
estimateDispersionsMAP for parallel execution.
dds <- makeExampleDESeqDataSet() dds <- estimateSizeFactors(dds) dds <- estimateDispersionsGeneEst(dds) dds <- estimateDispersionsFit(dds) dds <- estimateDispersionsMAP(dds) plotDispEsts(dds) # after having run estimateDispersionsFit() # the dispersion prior variance over all genes # can be obtained like so: dispPriorVar <- estimateDispersionsPriorVar(dds)
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