This is a container class for TCC. This class initially contains count data matrix and some information for the analysis of count data. It also provides further fields that are populated during the analysis.
This class is implemented as an R5 reference class.
Functions calling such methods copies the object prior to
calling the method to keep the semantics of functional programming.
This class can be created by the generic
new function with
count data and associated information of experimental design.
The values (defaults to all 1) in the
will be changed after performing the
DEGES field stores information related to our DEGES-based
normalization pipeline after performing the
estimatedDEG fields store results after performing
simulation field stores parameters
used when performing the
This class contains the following fields:
numeric matrix containing count data.
character vector indicating the identifier of the count unit, usually gene.
data frame indicating information about experimental design.
numeric vector containing normalization factors (default to 1).
list for storing results after the execution of the
numeric vector as a placeholder for indicating
either DEGs (flagged as "1") or non-DEGs (as "0") for individual
genes. The values in this field will be populated after
the execution of the
list. This field is only used for analyzing simulation data.
list for storing the information about normalization steps.
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tcc <- simulateReadCounts(Ngene = 10000, PDEG = 0.2, DEG.assign = c(0.8, 0.2), DEG.foldchange = c(4, 4), replicates = c(3, 3)) # Check the TCC class object. tcc # Check the fields of TCC class object. names(tcc) head(tcc$count) # Check the normalization factors. tcc <- calcNormFactors(tcc, norm.method = "tmm", test.method = "edger", iteration = 1, FDR = 0.1, floorPDEG = 0.05) tcc$norm.factors # Check the p-values and q-values. tcc <- estimateDE(tcc, test.method = "edger", FDR = 0.1) tcc # Compare the breakdowns of estimated DEGs with the truth. head(tcc$estimatedDEG) head(tcc$simulation$trueDEG) # M-A plotting. plot(tcc)
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