Description Usage Arguments Value See Also Examples

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ```
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.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
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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