tfStability | R Documentation |
tfStability aims to statistically evaluate the
stability of ReductiveHourglassTest
, FlatLineTest
,
ReverseHourglassTest
, EarlyConservationTest
, or
LateConservationTest
(all based on TAI
or TDI
computations) against different
data transformations.
The corresponding p-value quantifies the probability that a given TAI or TDI pattern (or any phylotranscriptomics pattern)
does not support the chosen test. A p-value < 0.05 indicates that the corresponding phylotranscriptomics pattern does
indeed support the chosen test.
tfStability(
ExpressionSet,
TestStatistic = "FlatLineTest",
transforms = c("none", "sqrt", "log2", "rank", "squared"),
modules = NULL,
permutations = 1000,
pseudocount = 1
)
ExpressionSet |
a standard PhyloExpressionSet or DivergenceExpressionSet object. |
TestStatistic |
a string defining the type of test statistics to be used to quantify the statistical significance the present phylotranscriptomics pattern. Possible values can be:
|
transforms |
a character vector of any valid function that transforms gene expression levels. |
modules |
a list storing three elements: early, mid, and late. Each element expects a numeric
vector specifying the developmental stages or experiments that correspond to each module.
For example, |
permutations |
a numeric value specifying the number of permutations to be performed for the |
pseudocount |
any valid number to add to the expression matrix prior to log transformations. |
An assessment for the stability of data transforms on the permutation test of choice.
For details, please consult tf
, ReductiveHourglassTest
,
FlatLineTest
, ReverseHourglassTest
,
LateConservationTest
or EarlyConservationTest
a vector object containing the vector elements:
p.value
: the p-value quantifying the statistical significance (depending on the chosen test) of the given phylotranscriptomics pattern under the given data transformation(s).
Jaruwatana Sodai Lotharukpong
Lotharukpong JS et al. (2023) (unpublished)
rhScore
, bootMatrix
, FlatLineTest
,
ReverseHourglassTest
, EarlyConservationTest
,
ReductiveHourglassTest
, PlotSignature
, LateConservationTest
data(PhyloExpressionSetExample)
# perform the reductive hourglass test for a PhyloExpressionSet
# here the prior biological knowledge is that stages 1-2 correspond to module 1 = early,
# stages 3-5 to module 2 = mid (phylotypic module), and stages 6-7 correspond to
# module 3 = late
tfStability(ExpressionSet = PhyloExpressionSetExample,
TestStatistic = "ReductiveHourglassTest",
permutations = 100,
transforms = c("log2", "sqrt", "none"),
modules = list(early = 1:2, mid = 3:5, late = 6:7))
# it is also possible to test the phylotranscriptomic pattern using rlog
# and vst transforms from DESeq2
library(DESeq2)
tfStability(ExpressionSet = PhyloExpressionSetExample,
TestStatistic = "ReductiveHourglassTest",
permutations = 100,
transforms = c("log2", "sqrt", "none", "vst"),
modules = list(early = 1:2, mid = 3:5, late = 6:7))
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