Perform Permutation Tests Under Different Transformations

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.

Usage

tfStability(
  ExpressionSet,
  TestStatistic = "FlatLineTest",
  transforms = c("none", "sqrt", "log2", "rank", "squared"),
  modules = NULL,
  permutations = 1000,
  pseudocount = 1
)

Arguments

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:

TestStatistic = "FlatLineTest" : Statistical test for the deviation from a flat line
TestStatistic = "ReductiveHourglassTest" : Statistical test for the existence of a hourglass shape (high-low-high pattern)
TestStatistic = "ReverseHourglassTest" : Statistical test for the existence of a reverse hourglass pattern (low-high-low pattern)
TestStatistic = "EarlyConservationTest" : Statistical test for the existence of a early conservation pattern (low-high-high pattern)
TestStatistic = "LateConservationTest" : Statistical test for the existence of a late conservation pattern (high-high-low pattern)

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, module = list(early = 1:2, mid = 3:5, late = 6:7) divides a dataset storing seven developmental stages into 3 modules.

permutations

a numeric value specifying the number of permutations to be performed for the FlatLineTest, EarlyConservationTest, LateConservationTest, ReductiveHourglassTest or ReverseHourglassTest.

pseudocount

any valid number to add to the expression matrix prior to log transformations.

Value

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).

Details

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

References

Lotharukpong JS et al. (2023) (unpublished)

Author

Jaruwatana Sodai Lotharukpong

Examples


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))
#> Proceeding with the ReductiveHourglassTest
#> 
#> [ Number of Eigen threads that are employed on your machine: 12 ]
#> 
#> [ Computing age assignment permutations for test statistic ... ]
#> 
[=========================================] 100%   
#> [ Computing variances of permuted transcriptome signatures ... ]
#> 
#> 
#> [ Number of Eigen threads that are employed on your machine: 12 ]
#> 
#> [ Computing age assignment permutations for test statistic ... ]
#> 
[=========================================] 100%   
#> [ Computing variances of permuted transcriptome signatures ... ]
#> 
#> 
#> [ Number of Eigen threads that are employed on your machine: 12 ]
#> 
#> [ Computing age assignment permutations for test statistic ... ]
#> 
[=========================================] 100%   
#> [ Computing variances of permuted transcriptome signatures ... ]
#> 
#>         log2         sqrt         none 
#> 8.884068e-02 2.620711e-06 1.421041e-08 


# it is also possible to test the phylotranscriptomic pattern using rlog
# and vst transforms from DESeq2

library(DESeq2)
#> Loading required package: S4Vectors
#> Warning: package ‘S4Vectors’ was built under R version 4.4.1
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> 
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#>     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#>     pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply,
#>     union, unique, unsplit, which.max, which.min
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#> 
#>     findMatches
#> The following objects are masked from ‘package:base’:
#> 
#>     I, expand.grid, unname
#> Loading required package: IRanges
#> Warning: package ‘IRanges’ was built under R version 4.4.1
#> Loading required package: GenomicRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> Warning: package ‘matrixStats’ was built under R version 4.4.1
#> 
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#> 
#>     rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     anyMissing, rowMedians
tfStability(ExpressionSet = PhyloExpressionSetExample,
                     TestStatistic = "ReductiveHourglassTest",
                     permutations       = 100,
                     transforms = c("log2", "sqrt", "none", "vst"),
                     modules = list(early = 1:2, mid = 3:5, late = 6:7))
#> Proceeding with the ReductiveHourglassTest
#> 
#> [ Number of Eigen threads that are employed on your machine: 12 ]
#> 
#> [ Computing age assignment permutations for test statistic ... ]
#> 
[=========================================] 100%   
#> [ Computing variances of permuted transcriptome signatures ... ]
#> 
#> 
#> [ Number of Eigen threads that are employed on your machine: 12 ]
#> 
#> [ Computing age assignment permutations for test statistic ... ]
#> 
[=========================================] 100%   
#> [ Computing variances of permuted transcriptome signatures ... ]
#> 
#> 
#> [ Number of Eigen threads that are employed on your machine: 12 ]
#> 
#> [ Computing age assignment permutations for test statistic ... ]
#> 
[=========================================] 100%   
#> [ Computing variances of permuted transcriptome signatures ... ]
#> 
#> converting counts to integer mode
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> The phylotranscriptomic pattern may not follow an hourglass pattern (high-low-high).
#> 
#> [ Number of Eigen threads that are employed on your machine: 12 ]
#> 
#> [ Computing age assignment permutations for test statistic ... ]
#> 
[=========================================] 100%   
#> [ Computing variances of permuted transcriptome signatures ... ]
#> 
#>         log2         sqrt         none          vst 
#> 1.218331e-01 1.121363e-07 1.008760e-09 9.057958e-01