estimateDE internally calls a specified method
implemented in other R packages.
estimateDE(tcc, test.method, FDR, paired, full, reduced, # for DESeq, DESeq2 design, contrast, # for edgeR, DESeq2, voom coef, # for edgeR, voom group, cl, # for baySeq samplesize, # for baySeq, SAMseq logged, floor, # for WAD ...
)"edger", "deseq", "deseq2",
"bayseq", "samseq", "voom", and "wad".
See the "Details" field for detail.
The default is "edger" when analyzing the count data with
replicates (i.e., min(table(tcc$group[, 1])) > 1), and
"deseq" (2 group) and "deseq2" (more than 2 group)
when analyzing the count data without replicates
(i.e., min(table(tcc$group[, 1])) == 1).TRUE, the input data are regarded as
(two-group) paired samples. If FALSE, the input data are
regarded as unpaired samples. The default is FALSE.tcc$group
is used as the model frame.
See the fitNbinomGLMs function in DESeq
for details, or nbinomLRT function in DESeq2.tcc$group is
used as the model frame.
See the fitNbinomGLMs function in DESeq
for details, or nbinomLRT function in DESeq2.glmFit function in edgeR or
the lmFit function in limma for details.
For DESeq2, it should be a formula specifying the design of the
experiment. See the DESeqDataSet function
in DESeq2 for details.glmLRT function in edgeR for details.
For DESeq2, the argument is same to contrast which used in
DESeq2 package to retrive the results from Wald test. See the
results function in DESeq2 for details.glmLRT function in edgeR for details.tcc$group for analysis. See the group argument
of topCounts function in baySeq for details.snow object when using multi processors if
test.method = "bayseq" is specified.
See the getPriors.NB function in baySeq
for details.test.method = "bayseq" (defaults to 10000),
and (ii) the number of permutation in samr if
test.method = "samseq" (defaults to 100).TRUE, the input data are regarded as
log2-transformed. If FALSE, the log2-transformation is
performed after the floor setting. The default is
logged = FALSE.
Ignored if test.method is not "wad".floor = 1, indicating that
values less than 1 are replaced by 1. Ignored if
logged = TRUE.
Ignored if test.method is not "wad".TCC-class object containing following fields:
p.adjust function
with default parameter settings."wad" is specified.FDR argument.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
$p$-values (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 two-group data,
and GLM approach is used for multi-group or multi-factor data.
However, if design and the one of coef or
contrast are given, the GLM approach will be used for
two-group 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 = "deseq"
DESeq supports two approach (i.e. an exact test and
GLM approach) for identifying DEGs. As a default,
the exact test is used for two-group data,
and GLM approach is used for multi-group or multi-factor data.
However, if full and reduced are given, the GLM approach
will be used for two-group data.
If the exact test is used,
estimateDispersions and
nbinomTest are internally called.
If the GLM approach is used,
estimateDispersions,
fitNbinomGLMs, and
nbinomGLMTest
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 = "samseq"
SAMseq with
resp.type = "Two class unpaired" arugment
in samr package is called to identify DEGs for two-group data,
resp.type = "Two class paired" for paired two-group data,
and resp.type = "Multiclass" for multi-group data.
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
$p$-values, the tcc$stat$p.value and
tcc$stat$q.value are NA.
Alternatively, the test statistics are stored in
tcc$stat$testStat field.
# 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)
# Analyzing a simulation data for comparing two groups
# (G1 vs. G2) without replicates
# The DE analysis is performed by an negative binomial (NB) test
# in DESeq coupled with the DEGES/DESeq normalization factors.
data(hypoData)
group <- c(1, 2)
tcc <- new("TCC", hypoData[, c(1, 4)], group)
tcc <- calcNormFactors(tcc, norm.method = "deseq", test.method = "deseq",
iteration = 1, FDR = 0.1, floorPDEG = 0.05)
tcc <- estimateDE(tcc, test.method = "deseq", FDR = 0.1)
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