ABSSeq {ABSSeq} | R Documentation |
This function performs a default analysis by calling, in order, the functions:
normalFactors
,
callParameter
,
callDEs
.
ABSSeq(object, adjmethod = "BH", replaceOutliers = TRUE, useaFold = FALSE, quiet = FALSE, ...)
object |
an |
adjmethod |
defualt is 'BH', method for p-value adjusted, see |
replaceOutliers |
default is TRUE, switch for outlier replacement. |
useaFold |
defualt is FALSE, switch for DE detection through fold-change, see |
quiet |
default is FALSE, whether to print messages at each step |
... |
parameters passed to |
The differential expression analysis models the total counts difference by a Negative binomal distribution
NB(μ,r)
:
an ABSDataSet object with additional elements, which can be retrieved by results
:
Amean and Bmean, mean of log2 normalized reads count for group A and B,
foldChange, shrinked (expression level and gene-specific) log2 of fold-change, B - A,
rawFC, raw log2 of fold-change, B-A (without shrinkage),
lowFC, expression level corrected log2 fold-change,
pvalue, pvalue from NB distribution model,
adj.pvalue, adjuested p-value used p.adjust method.
Wentao Yang
Wentao Yang, Philip Rosenstiel & Hinrich Schulenburg: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- ABSSeq(obj) res <- results(obj,c("Amean","Bmean","foldChange","pvalue","adj.pvalue")) head(res)