Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/estimateGLMCommonDisp.R
Estimates a common negative binomial dispersion parameter for a DGE dataset with a general experimental design.
1 2 3 4 5 6 7 |
y |
object containing read counts, as for |
design |
numeric design matrix, as for |
offset |
numeric vector or matrix of offsets for the log-linear models, as for |
method |
method for estimating the dispersion.
Possible values are |
subset |
maximum number of rows of |
AveLogCPM |
numeric vector giving average log2 counts per million for each gene. |
verbose |
logical, if |
weights |
optional numeric matrix giving observation weights |
... |
other arguments are passed to lower-level functions.
See |
This function calls dispCoxReid
, dispPearson
or dispDeviance
depending on the method
specified.
See dispCoxReid
for details of the three methods and a discussion of their relative performance.
The default method returns a numeric vector of length 1 containing the estimated common dispersion.
The DGEList
method returns the same DGEList
y
as input but with common.dispersion
as an added component.
The output object will also contain a component AveLogCPM
if it was not already present in y
.
Gordon Smyth, Davis McCarthy, Yunshun Chen
McCarthy, DJ, Chen, Y, Smyth, GK (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 40, 4288-4297. https://doi.org/10.1093/nar/gks042
dispCoxReid
, dispPearson
, dispDeviance
estimateGLMTrendedDisp
for trended dispersions or estimateGLMTagwiseDisp
for genewise dispersions in the context of a generalized linear model.
estimateCommonDisp
for the common dispersion or estimateTagwiseDisp
for genewise dispersions in the context of a multiple group experiment (one-way layout).
1 2 3 4 5 6 7 8 9 10 | # True dispersion is 1/size=0.1
y <- matrix(rnbinom(1000,mu=10,size=10),ncol=4)
d <- DGEList(counts=y,group=c(1,1,2,2))
design <- model.matrix(~group, data=d$samples)
d1 <- estimateGLMCommonDisp(d, design, verbose=TRUE)
# Compare with classic CML estimator:
d2 <- estimateCommonDisp(d, verbose=TRUE)
# See example(glmFit) for a different example
|
Loading required package: limma
Disp = 0.08332 , BCV = 0.2886
Disp = 0.08559 , BCV = 0.2926
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