Description Usage Arguments Details Value Examples
This function calculates pvalues (or the related statistics) for
identifying differentially expressed genes (DEGs) from a
TCCclass object.
estimateDE
internally calls a specified method
implemented in other R packages.
1 2 3 4 5 6 7 8 9  estimateDE(tcc, test.method, FDR, paired,
full, reduced, # for DESeq2
design, contrast, # for edgeR, DESeq2, voom
coef, # for edgeR, voom
group, cl, # for baySeq
samplesize, # for baySeq, SAMseq
logged, floor, # for WAD
...
)

tcc 
TCCclass object. 
test.method 
character string specifying a method for identifying
DEGs: one of 
FDR 
numeric value (between 0 and 1) specifying the threshold for determining DEGs. 
paired 
logical. If 
full 
a formula for creating full model described in
DESeq2. The right hand side can involve any column of 
reduced 
a formula for creating reduced model described in DESeq2.
The right hand side can involve any column of 
design 
the argument is used in edgeR, voom (limma) and DESeq2.
For edgeR and voom, it should be the numeric matrix giving the
design matrix for the generalized linear model.
See the 
contrast 
the argument is used in edgeR and DESeq2.
For edgeR, numeric vector specifying a contrast of the linear model
coefficients to be tested equal to zero.
See the 
coef 
integer or character vector indicating which coefficients
of the linear model are to be tested equal to zero.
See the 
group 
numeric or character string identifying the columns in
the 
cl 

samplesize 
integer specifying the sample size for estimating the
prior parameters if 
logged 
logical. If 
floor 
numeric scalar (> 0) specifying the floor value for
taking logarithm. The default is 
... 
further paramenters. 
estimaetDE
function is generally used after performing the
calcNormFactors
function that calculates normalization factors.
estimateDE
constructs a statistical model for differential expression
(DE) analysis with the calculated normalization factors and returns the
pvalues (or the derivatives). The individual functions in other
packages are internally called according to the specified
test.method
parameter.
test.method = "edger"
There are two approaches (i.e., exact test and GLM) to identify DEGs
in edgeR. The two approches are implmented in TCC. As a default,
the exact test approach is used for twogroup data,
and GLM approach is used for multigroup or multifactor data.
However, if design
and the one of coef
or
contrast
are given, the GLM approach will be used for
twogroup data.
If the exact test approach is used,
estimateCommonDisp
,
estimateTagwiseDisp
, and
exactTest
are internally called.
If the GLM approach is used,
estimateGLMCommonDisp
,
estimateGLMTrendedDisp
,
estimateGLMTagwiseDisp
,
glmFit
, and
glmLRT
are internally called.
test.method = "deseq2"
estimateDispersions
, and
nbinomWaldTest
are internally called for
identifying DEGs.
However, if full
and reduced
are given,
the nbinomLRT
will be used.
test.method = "bayseq"
getPriors.NB
and
getLikelihoods
in baySeq are internally
called for identifying DEGs.
If paired = TRUE
,
getPriors
and
getLikelihoods
in baySeq are used.
test.method = "voom"
voom
, lmFit
, and
eBayes
in limma are internally called
for identifying DEGs.
test.method = "wad"
The WAD
implemented in TCC is used for identifying
DEGs. Since WAD
outputs test statistics instead of
pvalues, the tcc$stat$p.value
and
tcc$stat$q.value
are NA
.
Alternatively, the test statistics are stored in
tcc$stat$testStat
field.
A TCCclass
object containing following fields:
stat$p.value 
numeric vector of pvalues. 
stat$q.value 
numeric vector of qvalues calculated
based on the pvalues using the 
stat$testStat 
numeric vector of test statistics if

stat$rank 
gene rank in order of the pvalues or test statistics. 
estimatedDEG 
numeric vector consisting of 0 or 1
depending on whether each gene is classified
as nonDEG or DEG. The threshold for classifying
DEGs or nonDEGs is preliminarily given as the

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  # Analyzing a simulation data for comparing two groups
# (G1 vs. G2) with biological replicates
# The DE analysis is performed by an exact test in edgeR coupled
# with the DEGES/edgeR normalization factors.
# For retrieving the summaries of DE results, we recommend to use
# the getResult function.
data(hypoData)
group < c(1, 1, 1, 2, 2, 2)
tcc < new("TCC", hypoData, group)
tcc < calcNormFactors(tcc, norm.method = "tmm", test.method = "edger",
iteration = 1, FDR = 0.1, floorPDEG = 0.05)
tcc < estimateDE(tcc, test.method = "edger", FDR = 0.1)
head(tcc$stat$p.value)
head(tcc$stat$q.value)
head(tcc$estimatedDEG)
result < getResult(tcc)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.