Description Usage Arguments Details Value Author(s) References See Also Examples
A similar method is applied to estimate the SCV for each gene based on the method used in DESeq
1 2 3 4 |
object |
a |
method |
There are three ways how the empirical dispersion can be computed:
|
sharingMode |
After the empirical dispersion values have been computed for each
gene, a dispersion-mean relationship is fitted for sharing
information across genes in order to reduce variability of the
dispersion estimates. After that, for each gene, we have two values: the
empirical value (derived only from this gene's data), and the
fitted value (i.e., the dispersion value typical for genes with an
average expression similar to those of this gene). The
|
fitType |
|
locfit_extra_args, lp_extra_args |
(only for |
... |
extra arguments are ignored |
The details regarding which option to choose can be found in the DESeq help page. Generally
speaking, if you have less number of replicates (<=3), set method="pooled"
. Otherwise,
try method="per-condition"
. We revised the code to estimate the variance of the true
signal by using variance sum law rather than calculate the variance directly.
The XBSeqDataSet
cds, with the slots fitInfo
and
dispEst
updated.
Yuanhang Liu
H. I. Chen, Y. Liu, Y. Zou, Z. Lai, D. Sarkar, Y. Huang, et al., "Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads," BMC Genomics, vol. 16 Suppl 7, p. S14, Jun 11 2015.
1 2 3 4 5 6 7 | conditions <- factor(c(rep('C1', 3), rep('C2', 3)))
data(ExampleData)
XB <- XBSeqDataSet(Observed, Background, conditions)
XB <- estimateRealCount(XB)
XB <- estimateSizeFactors(XB)
XB <- estimateSCV(XB, fitType='local')
str(fitInfo(XB))
|
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