DAssemble is an R package for ensemble-based differential association analysis of high-throughput omics data. It provides a flexible framework for combining a user-specified core method with one or more complementary enhancer methods, allowing users to integrate statistical evidence across different modeling assumptions rather than relying on a single differential analysis approach. DAssemble aggregates model-specific p-values using the Cauchy Combination Tests (CCT) and reports both ensemble-level and method-specific results.
The package was developed to support robust and interpretable differential analysis across multiple omics domains, including bulk RNA-seq, single-cell RNA-seq, and microbiome data. We submitted DAssemble to Bioconductor to make the method easier to install, document, test, and integrate with established Bioconductor workflows and data structures, thereby supporting reproducible and extensible analysis of high-throughput biological data.
DAssemble implements an ensemble framework for
differential-abundance and differential-expression analysis. Users can
choose a single core model and optionally combine it with enhancer tests
using the Cauchy Combination Test.
The package accepts a MultiAssayExperiment object
directly. When multiple experiments are present, use
assay_name to select the experiment to analyze. For
lower-level workflows, features can also be a data frame
with samples in rows and features in columns, paired with a
metadata data frame with matching sample row names. The
exposure variable supplied through expVar must be
binary.
Install DAssemble from Bioconductor with
BiocManager:
The airway dataset from Bioconductor contains RNA-seq
counts from airway smooth muscle cells treated with dexamethasone. The
example below runs DAssemble with DESeq2 as
the core method and two enhancers.
data("airway", package = "airway")
counts <- SummarizedExperiment::assay(airway, "counts")
keep_genes <- order(rowSums(counts), decreasing = TRUE)[seq_len(500L)]
counts <- counts[keep_genes, , drop = FALSE]
metadata <- as.data.frame(SummarizedExperiment::colData(airway))
metadata <- metadata[colnames(counts), , drop = FALSE]
se <- SummarizedExperiment::SummarizedExperiment(
assays = list(counts = counts)
)
mae <- MultiAssayExperiment::MultiAssayExperiment(
experiments = list(rnaseq = se),
colData = S4Vectors::DataFrame(metadata)
)
res <- DAssemble::DAssemble(
features = mae,
assay_name = "rnaseq",
core_method = "DESeq2",
enhancers = c("WLX", "LR"),
expVar = "dex",
p_adj = "BH",
enhancer_norm = "tmm",
return_components = TRUE,
return_subensembles = TRUE
)
#> using pre-existing size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> -- 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.
#> final dispersion estimates
#> fitting model and testing
#> calcNormFactors has been renamed to normLibSizes
head(res$res)
#> feature metadata pval_core pval_WLX coef_LR pval_LR
#> 260 ENSG00000148175 dex 5.996343e-53 0.02857143 0.01453585 0.9999999
#> 244 ENSG00000101347 dex 1.914071e-42 0.02857143 0.01453585 0.9999999
#> 435 ENSG00000166741 dex 1.301069e-39 0.02857143 0.01453585 0.9999999
#> 16 ENSG00000108821 dex 1.532237e-29 0.02857143 0.01453585 0.9999999
#> 127 ENSG00000135821 dex 8.416394e-27 0.02857143 0.01453585 0.9999999
#> 361 ENSG00000197321 dex 1.659951e-24 0.02857143 0.01453585 0.9999999
#> pval_joint qval
#> 260 1.798903e-52 8.994514e-50
#> 244 5.742212e-42 1.435553e-39
#> 435 3.903207e-39 6.505345e-37
#> 16 4.596711e-29 5.745889e-27
#> 127 2.524918e-26 2.524918e-24
#> 361 4.979854e-24 4.149878e-22
names(res$components)
#> [1] "DESeq2" "WLX" "LR"This analysis combines DESeq2 p-values with Wilcoxon and
logistic-regression enhancer p-values. The combined results are returned
in res$res, and the per-method outputs are available in
res$components.
The GlobalPatterns dataset from phyloseq
contains 16S rRNA profiles from environmental and host-associated
microbiome samples. The example below compares fecal and soil samples
using enhancer-only DAssemble with CLR normalization.
data("GlobalPatterns", package = "phyloseq")
gp <- GlobalPatterns
otu <- as(phyloseq::otu_table(gp), "matrix")
metadata <- as.data.frame(phyloseq::sample_data(gp))
keep <- metadata$SampleType %in% c("Feces", "Soil")
otu <- otu[, keep, drop = FALSE]
keep_taxa <- order(rowSums(otu), decreasing = TRUE)[seq_len(250L)]
features <- as.data.frame(t(otu[keep_taxa, , drop = FALSE]))
metadata <- metadata[keep, , drop = FALSE]
metadata$group <- droplevels(factor(metadata$SampleType))
stopifnot(nlevels(metadata$group) == 2L)
se <- SummarizedExperiment::SummarizedExperiment(
assays = list(counts = t(as.matrix(features)))
)
mae <- MultiAssayExperiment::MultiAssayExperiment(
experiments = list(microbiome = se),
colData = S4Vectors::DataFrame(metadata)
)
res <- DAssemble::DAssemble(
features = mae,
assay_name = "microbiome",
core_method = NULL,
enhancers = c("WLX", "LR", "KS"),
expVar = "group",
enhancer_norm = "clr",
return_components = TRUE,
return_subensembles = TRUE
)
head(res$res)
#> feature metadata pval_WLX coef_LR pval_LR pval_KS pval_joint qval
#> 1 331820 group 0.05714286 1.386108 0.9999888 0.05714286 0.9999664 1
#> 2 158660 group 0.05714286 1.386108 0.9999888 0.05714286 0.9999664 1
#> 3 189047 group 0.05714286 -47.774809 0.9996139 0.05714286 0.9988259 1
#> 4 244304 group 0.62857143 2.092926 0.1901144 0.65714286 0.4469387 1
#> 5 171551 group 0.05714286 -17.489589 0.9973657 0.05714286 0.9913058 1
#> 6 263681 group 0.85714286 1.802663 0.2883916 0.65714286 0.6744160 1
names(res$components)
#> [1] "WLX" "LR" "KS"
head(res$ensembles)
#> $WLX
#> feature Pval adjPval
#> 1 331820 0.05714286 0.4230704
#> 2 158660 0.05714286 0.4230704
#> 3 189047 0.05714286 0.4230704
#> 4 244304 0.62857143 1.0000000
#> 5 171551 0.05714286 0.4230704
#> 6 263681 0.85714286 1.0000000
#> 7 192573 0.05714286 0.4230704
#> 8 322235 0.05714286 0.4230704
#> 9 180658 0.05714286 0.4230704
#> 10 326977 0.05714286 0.4230704
#> 11 259569 0.05714286 0.4230704
#> 12 325407 0.05714286 0.4230704
#> 13 361496 0.05714286 0.4230704
#> 14 316732 0.05714286 0.4230704
#> 15 291090 0.05714286 0.4230704
#> 16 348374 0.05714286 0.4230704
#> 17 196731 0.05714286 0.4230704
#> 18 278873 0.85714286 1.0000000
#> 19 290260 0.05714286 0.4230704
#> 20 361480 0.05714286 0.4230704
#> 21 193761 0.05714286 0.4230704
#> 22 357591 0.40000000 1.0000000
#> 23 127309 1.00000000 1.0000000
#> 24 357795 0.05714286 0.4230704
#> 25 517293 0.85714286 1.0000000
#> 26 223059 0.05714286 0.4230704
#> 27 248140 0.05714286 0.4230704
#> 28 256977 0.05714286 0.4230704
#> 29 287978 0.05714286 0.4230704
#> 30 357470 0.05714286 0.4230704
#> 31 367433 0.05714286 0.4230704
#> 32 187524 0.05714286 0.4230704
#> 33 211720 1.00000000 1.0000000
#> 34 36155 0.05714286 0.4230704
#> 35 518438 0.05714286 0.4230704
#> 36 327476 0.05714286 0.4230704
#> 37 309788 0.05714286 0.4230704
#> 38 470172 0.05714286 0.4230704
#> 39 240687 0.05714286 0.4230704
#> 40 3046 0.05714286 0.4230704
#> 41 470973 0.05714286 0.4230704
#> 42 199487 0.05714286 0.4230704
#> 43 191541 0.05714286 0.4230704
#> 44 573135 0.05714286 0.4230704
#> 45 365676 0.05714286 0.4230704
#> 46 47916 0.05714286 0.4230704
#> 47 549871 0.62857143 1.0000000
#> 48 269833 0.05714286 0.4230704
#> 49 194053 0.05714286 0.4230704
#> 50 220494 0.05714286 0.4230704
#> 51 190464 0.05714286 0.4230704
#> 52 288134 0.05714286 0.4230704
#> 53 194648 0.05714286 0.4230704
#> 54 589024 0.05714286 0.4230704
#> 55 279297 0.05714286 0.4230704
#> 56 306180 0.05714286 0.4230704
#> 57 347862 0.11428571 0.7995643
#> 58 138006 0.05714286 0.4230704
#> 59 212619 0.05714286 0.4230704
#> 60 268540 0.05714286 0.4230704
#> 61 27669 0.05714286 0.4230704
#> 62 293896 0.05714286 0.4230704
#> 63 195173 0.05714286 0.4230704
#> 64 150925 0.05714286 0.4230704
#> 65 249661 0.40000000 1.0000000
#> 66 192127 0.05714286 0.4230704
#> 67 565399 0.05714286 0.4230704
#> 68 111806 0.05714286 0.4230704
#> 69 302160 0.05714286 0.4230704
#> 70 193066 0.05714286 0.4230704
#> 71 578268 0.05714286 0.4230704
#> 72 352304 0.05714286 0.4230704
#> 73 469873 0.05714286 0.4230704
#> 74 183200 0.05714286 0.4230704
#> 75 65683 0.05714286 0.4230704
#> 76 177339 0.05714286 0.4230704
#> 77 193343 0.05714286 0.4230704
#> 78 203708 0.05714286 0.4230704
#> 79 42680 0.40000000 1.0000000
#> 80 328422 0.05714286 0.4230704
#> 81 208283 0.05714286 0.4230704
#> 82 326792 0.11428571 0.7995643
#> 83 261912 0.05714286 0.4230704
#> 84 71074 0.05714286 0.4230704
#> 85 509622 0.05714286 0.4230704
#> 86 175537 0.05714286 0.4230704
#> 87 557785 0.11428571 0.7995643
#> 88 405524 0.05714286 0.4230704
#> 89 546313 0.05714286 0.4230704
#> 90 360298 0.05714286 0.4230704
#> 91 171772 0.22857143 1.0000000
#> 92 154822 0.05714286 0.4230704
#> 93 541301 0.85714286 1.0000000
#> 94 2000 0.05714286 0.4230704
#> 95 469482 0.11428571 0.7995643
#> 96 179460 0.05714286 0.4230704
#> 97 565556 0.05714286 0.4230704
#> 98 109014 0.05714286 0.4230704
#> 99 100954 0.05714286 0.4230704
#> 100 223329 0.05714286 0.4230704
#> 101 181843 0.05714286 0.4230704
#> 102 366612 0.05714286 0.4230704
#> 103 310301 0.05714286 0.4230704
#> 104 367055 0.05714286 0.4230704
#> 105 181095 0.22857143 1.0000000
#> 106 162162 0.05714286 0.4230704
#> 107 299892 0.40000000 1.0000000
#> 108 175497 0.22857143 1.0000000
#> 109 166835 0.05714286 0.4230704
#> 110 150508 0.05714286 0.4230704
#> 111 190108 0.05714286 0.4230704
#> 112 170206 0.05714286 0.4230704
#> 113 313985 0.05714286 0.4230704
#> 114 574458 0.05714286 0.4230704
#> 115 113959 0.05714286 0.4230704
#> 116 366882 1.00000000 1.0000000
#> 117 470114 0.05714286 0.4230704
#> 118 544749 0.05714286 0.4230704
#> 119 247010 0.05714286 0.4230704
#> 120 154102 0.05714286 0.4230704
#> 121 129759 0.05714286 0.4230704
#> 122 113626 0.05714286 0.4230704
#> 123 82283 0.05714286 0.4230704
#> 124 262724 0.05714286 0.4230704
#> 125 273055 0.05714286 0.4230704
#> 126 551424 0.05714286 0.4230704
#> 127 262714 0.05714286 0.4230704
#> 128 111986 0.05714286 0.4230704
#> 129 340507 0.05714286 0.4230704
#> 130 84768 0.05714286 0.4230704
#> 131 250258 0.11428571 0.7995643
#> 132 104780 0.05714286 0.4230704
#> 133 109546 0.22857143 1.0000000
#> 134 544358 0.05714286 0.4230704
#> 135 158370 0.05714286 0.4230704
#> 136 566886 0.05714286 0.4230704
#> 137 320147 0.22857143 1.0000000
#> 138 306921 0.05714286 0.4230704
#> 139 114339 0.05714286 0.4230704
#> 140 243150 0.22857143 1.0000000
#> 141 188646 0.05714286 0.4230704
#> 142 216191 0.05714286 0.4230704
#> 143 191687 0.22857143 1.0000000
#> 144 352632 0.05714286 0.4230704
#> 145 245203 0.05714286 0.4230704
#> 146 204889 0.05714286 0.4230704
#> 147 203722 0.05714286 0.4230704
#> 148 469991 0.05714286 0.4230704
#> 149 265043 0.05714286 0.4230704
#> 150 511008 0.05714286 0.4230704
#> 151 184899 0.05714286 0.4230704
#> 152 302974 0.22857143 1.0000000
#> 153 346780 0.05714286 0.4230704
#> 154 174348 0.05714286 0.4230704
#> 155 278234 0.05714286 0.4230704
#> 156 242675 0.05714286 0.4230704
#> 157 185281 0.05714286 0.4230704
#> 158 167602 0.05714286 0.4230704
#> 159 470812 0.05714286 0.4230704
#> 160 559200 0.05714286 0.4230704
#> 161 367659 0.05714286 0.4230704
#> 162 161227 0.05714286 0.4230704
#> 163 213747 0.05714286 0.4230704
#> 164 277931 0.05714286 0.4230704
#> 165 235270 0.05714286 0.4230704
#> 166 181892 0.11428571 0.7995643
#> 167 527683 0.05714286 0.4230704
#> 168 151018 0.05714286 0.4230704
#> 169 145786 0.62857143 1.0000000
#> 170 113866 0.05714286 0.4230704
#> 171 190872 0.05714286 0.4230704
#> 172 178915 0.05714286 0.4230704
#> 173 181028 0.11428571 0.7995643
#> 174 252410 0.05714286 0.4230704
#> 175 331647 0.05714286 0.4230704
#> 176 356083 0.05714286 0.4230704
#> 177 182980 0.05714286 0.4230704
#> 178 200741 0.05714286 0.4230704
#> 179 244491 0.05714286 0.4230704
#> 180 204044 0.05714286 0.4230704
#> 181 550814 0.11428571 0.7995643
#> 182 191306 0.05714286 0.4230704
#> 183 51024 0.11428571 0.7995643
#> 184 510994 0.05714286 0.4230704
#> 185 308892 0.05714286 0.4230704
#> 186 211351 0.05714286 0.4230704
#> 187 1605 0.05714286 0.4230704
#> 188 250291 0.05714286 0.4230704
#> 189 360268 0.22857143 1.0000000
#> 190 218467 0.05714286 0.4230704
#> 191 286104 0.05714286 0.4230704
#> 192 511844 0.05714286 0.4230704
#> 193 263461 0.22857143 1.0000000
#> 194 21698 0.05714286 0.4230704
#> 195 154230 0.05714286 0.4230704
#> 196 543366 0.05714286 0.4230704
#> 197 549552 0.05714286 0.4230704
#> 198 535437 0.05714286 0.4230704
#> 199 265094 0.05714286 0.4230704
#> 200 159711 0.05714286 0.4230704
#> 201 144381 0.05714286 0.4230704
#> 202 255584 0.22857143 1.0000000
#> 203 215700 0.05714286 0.4230704
#> 204 275891 0.11428571 0.7995643
#> 205 584276 0.05714286 0.4230704
#> 206 368928 0.22857143 1.0000000
#> 207 570888 0.05714286 0.4230704
#> 208 278390 0.05714286 0.4230704
#> 209 363692 0.05714286 0.4230704
#> 210 216862 0.05714286 0.4230704
#> 211 230437 0.40000000 1.0000000
#> 212 212128 0.05714286 0.4230704
#> 213 170339 0.05714286 0.4230704
#> 214 582012 0.05714286 0.4230704
#> 215 93865 0.05714286 0.4230704
#> 216 469946 0.85714286 1.0000000
#> 217 204592 0.05714286 0.4230704
#> 218 252778 0.05714286 0.4230704
#> 219 251499 0.05714286 0.4230704
#> 220 1791 0.05714286 0.4230704
#> 221 70100 0.05714286 0.4230704
#> 222 97561 0.05714286 0.4230704
#> 223 278272 0.05714286 0.4230704
#> 224 103685 1.00000000 1.0000000
#> 225 154268 0.11428571 0.7995643
#> 226 130368 0.05714286 0.4230704
#> 227 76270 0.05714286 0.4230704
#> 228 190723 0.05714286 0.4230704
#> 229 208694 0.05714286 0.4230704
#> 230 193632 0.05714286 0.4230704
#> 231 222729 0.05714286 0.4230704
#> 232 206632 0.05714286 0.4230704
#> 233 203846 0.05714286 0.4230704
#> 234 156101 0.05714286 0.4230704
#> 235 575937 0.05714286 0.4230704
#> 236 349634 0.05714286 0.4230704
#> 237 565565 0.40000000 1.0000000
#> 238 547632 0.11428571 0.7995643
#> 239 553113 0.05714286 0.4230704
#> 240 13034 0.05714286 0.4230704
#> 241 210176 0.05714286 0.4230704
#> 242 308535 0.05714286 0.4230704
#> 243 197817 0.62857143 1.0000000
#> 244 109907 0.05714286 0.4230704
#> 245 113921 0.05714286 0.4230704
#> 246 173810 1.00000000 1.0000000
#> 247 215890 0.05714286 0.4230704
#> 248 326414 0.05714286 0.4230704
#> 249 294022 0.05714286 0.4230704
#> 250 534348 0.05714286 0.4230704
#>
#> $LR
#> feature Pval adjPval
#> 1 331820 0.9999888 1
#> 2 158660 0.9999888 1
#> 3 189047 0.9996139 1
#> 4 244304 0.1901144 1
#> 5 171551 0.9973657 1
#> 6 263681 0.2883916 1
#> 7 192573 0.9996139 1
#> 8 322235 0.9973657 1
#> 9 180658 0.9996139 1
#> 10 326977 0.9999888 1
#> 11 259569 0.9971529 1
#> 12 325407 0.9996139 1
#> 13 361496 0.9996139 1
#> 14 316732 0.9996139 1
#> 15 291090 0.9996139 1
#> 16 348374 0.9974869 1
#> 17 196731 0.9996139 1
#> 18 278873 0.9977790 1
#> 19 290260 0.9996139 1
#> 20 361480 0.9996139 1
#> 21 193761 0.9996139 1
#> 22 357591 0.9974869 1
#> 23 127309 0.2883916 1
#> 24 357795 0.9996139 1
#> 25 517293 0.9977790 1
#> 26 223059 0.9996139 1
#> 27 248140 0.9999888 1
#> 28 256977 0.9995913 1
#> 29 287978 0.9996139 1
#> 30 357470 0.9996139 1
#> 31 367433 0.9996139 1
#> 32 187524 0.9996139 1
#> 33 211720 0.9977790 1
#> 34 36155 0.9979279 1
#> 35 518438 0.9996139 1
#> 36 327476 0.9996139 1
#> 37 309788 0.9995913 1
#> 38 470172 0.9977790 1
#> 39 240687 0.9995913 1
#> 40 3046 0.9995913 1
#> 41 470973 0.9971529 1
#> 42 199487 0.9996139 1
#> 43 191541 0.9996139 1
#> 44 573135 0.9995913 1
#> 45 365676 0.9996139 1
#> 46 47916 0.9979279 1
#> 47 549871 0.9976329 1
#> 48 269833 0.9996139 1
#> 49 194053 0.9996139 1
#> 50 220494 0.9995913 1
#> 51 190464 0.9996139 1
#> 52 288134 0.9996139 1
#> 53 194648 0.9996139 1
#> 54 589024 0.9974869 1
#> 55 279297 0.9979279 1
#> 56 306180 0.9995913 1
#> 57 347862 0.9974869 1
#> 58 138006 0.9996139 1
#> 59 212619 0.9996139 1
#> 60 268540 0.9974869 1
#> 61 27669 0.9995913 1
#> 62 293896 0.9996139 1
#> 63 195173 0.9996139 1
#> 64 150925 0.9979279 1
#> 65 249661 0.9977790 1
#> 66 192127 0.9996139 1
#> 67 565399 0.9995913 1
#> 68 111806 0.9995913 1
#> 69 302160 0.9974869 1
#> 70 193066 0.9996139 1
#> 71 578268 0.9979279 1
#> 72 352304 0.9971529 1
#> 73 469873 0.9976329 1
#> 74 183200 0.9977790 1
#> 75 65683 0.9995913 1
#> 76 177339 0.9996139 1
#> 77 193343 0.9996139 1
#> 78 203708 0.9996139 1
#> 79 42680 0.9976329 1
#> 80 328422 0.9976329 1
#> 81 208283 0.9979279 1
#> 82 326792 0.9974869 1
#> 83 261912 0.9996139 1
#> 84 71074 0.9995913 1
#> 85 509622 0.9995913 1
#> 86 175537 0.9996139 1
#> 87 557785 0.9979279 1
#> 88 405524 0.9976329 1
#> 89 546313 0.9979279 1
#> 90 360298 0.9995913 1
#> 91 171772 0.9976329 1
#> 92 154822 0.9979279 1
#> 93 541301 0.9976329 1
#> 94 2000 0.9974869 1
#> 95 469482 0.9976329 1
#> 96 179460 0.9996139 1
#> 97 565556 0.9979279 1
#> 98 109014 0.9974869 1
#> 99 100954 0.9979279 1
#> 100 223329 0.9995913 1
#> 101 181843 0.9996139 1
#> 102 366612 0.9974869 1
#> 103 310301 0.9996139 1
#> 104 367055 0.9976329 1
#> 105 181095 0.9976329 1
#> 106 162162 0.9996139 1
#> 107 299892 0.9976329 1
#> 108 175497 0.9976329 1
#> 109 166835 0.9979279 1
#> 110 150508 0.9979279 1
#> 111 190108 0.9996139 1
#> 112 170206 0.9974869 1
#> 113 313985 0.9995913 1
#> 114 574458 0.9995913 1
#> 115 113959 0.9979279 1
#> 116 366882 0.9977790 1
#> 117 470114 0.9976329 1
#> 118 544749 0.9979279 1
#> 119 247010 0.9995913 1
#> 120 154102 0.9995913 1
#> 121 129759 0.9995913 1
#> 122 113626 0.9995913 1
#> 123 82283 0.9996139 1
#> 124 262724 0.9996139 1
#> 125 273055 0.9995913 1
#> 126 551424 0.9995913 1
#> 127 262714 0.9995913 1
#> 128 111986 0.9980570 1
#> 129 340507 0.9995913 1
#> 130 84768 0.9995913 1
#> 131 250258 0.9979279 1
#> 132 104780 0.9974869 1
#> 133 109546 0.9976329 1
#> 134 544358 0.9976329 1
#> 135 158370 0.9995913 1
#> 136 566886 0.9995913 1
#> 137 320147 0.9977790 1
#> 138 306921 0.9979279 1
#> 139 114339 0.9979279 1
#> 140 243150 0.9977790 1
#> 141 188646 0.9996139 1
#> 142 216191 0.9995913 1
#> 143 191687 0.9974869 1
#> 144 352632 0.9995913 1
#> 145 245203 0.9995913 1
#> 146 204889 0.9976329 1
#> 147 203722 0.9979279 1
#> 148 469991 0.9996139 1
#> 149 265043 0.9995913 1
#> 150 511008 0.9971529 1
#> 151 184899 0.9979279 1
#> 152 302974 0.9976329 1
#> 153 346780 0.9996139 1
#> 154 174348 0.9996139 1
#> 155 278234 0.9974869 1
#> 156 242675 0.9995913 1
#> 157 185281 0.9996139 1
#> 158 167602 0.9996139 1
#> 159 470812 0.9976329 1
#> 160 559200 0.9995913 1
#> 161 367659 0.9995913 1
#> 162 161227 0.9995913 1
#> 163 213747 0.9974869 1
#> 164 277931 0.9995913 1
#> 165 235270 0.9979279 1
#> 166 181892 0.9974869 1
#> 167 527683 0.9995913 1
#> 168 151018 0.9979279 1
#> 169 145786 0.9979279 1
#> 170 113866 0.9995913 1
#> 171 190872 0.9974869 1
#> 172 178915 0.9974869 1
#> 173 181028 0.9977790 1
#> 174 252410 0.9979279 1
#> 175 331647 0.9979279 1
#> 176 356083 0.9995913 1
#> 177 182980 0.9996139 1
#> 178 200741 0.9995913 1
#> 179 244491 0.9979279 1
#> 180 204044 0.9996139 1
#> 181 550814 0.9977790 1
#> 182 191306 0.9974869 1
#> 183 51024 0.9979279 1
#> 184 510994 0.9981818 1
#> 185 308892 0.9995913 1
#> 186 211351 0.9979279 1
#> 187 1605 0.9995913 1
#> 188 250291 0.9995913 1
#> 189 360268 0.9977790 1
#> 190 218467 0.9995913 1
#> 191 286104 0.9995913 1
#> 192 511844 0.9995913 1
#> 193 263461 0.9974869 1
#> 194 21698 0.9974869 1
#> 195 154230 0.9979279 1
#> 196 543366 0.9995913 1
#> 197 549552 0.9979279 1
#> 198 535437 0.9995913 1
#> 199 265094 0.9979279 1
#> 200 159711 0.9995913 1
#> 201 144381 0.9995913 1
#> 202 255584 0.9979279 1
#> 203 215700 0.9979279 1
#> 204 275891 0.9977790 1
#> 205 584276 0.9995913 1
#> 206 368928 0.9977790 1
#> 207 570888 0.9995913 1
#> 208 278390 0.9995913 1
#> 209 363692 0.9976329 1
#> 210 216862 0.9996139 1
#> 211 230437 0.9977790 1
#> 212 212128 0.9977790 1
#> 213 170339 0.9995913 1
#> 214 582012 0.9979279 1
#> 215 93865 0.9995913 1
#> 216 469946 0.9977790 1
#> 217 204592 0.9979279 1
#> 218 252778 0.9979279 1
#> 219 251499 0.9995913 1
#> 220 1791 0.9979279 1
#> 221 70100 0.9995913 1
#> 222 97561 0.9995913 1
#> 223 278272 0.9995913 1
#> 224 103685 0.9977790 1
#> 225 154268 0.9979279 1
#> 226 130368 0.9995913 1
#> 227 76270 0.9995913 1
#> 228 190723 0.9995913 1
#> 229 208694 0.9995913 1
#> 230 193632 0.9974869 1
#> 231 222729 0.9995913 1
#> 232 206632 0.9979279 1
#> 233 203846 0.9995913 1
#> 234 156101 0.9995913 1
#> 235 575937 0.9979279 1
#> 236 349634 0.9979279 1
#> 237 565565 0.9977790 1
#> 238 547632 0.9979279 1
#> 239 553113 0.9979279 1
#> 240 13034 0.9995913 1
#> 241 210176 0.9979279 1
#> 242 308535 0.9979279 1
#> 243 197817 0.9977790 1
#> 244 109907 0.9995913 1
#> 245 113921 0.9979279 1
#> 246 173810 0.9977790 1
#> 247 215890 0.9977790 1
#> 248 326414 0.9995913 1
#> 249 294022 0.9996139 1
#> 250 534348 0.9979279 1
#>
#> $KS
#> feature Pval adjPval
#> 1 331820 0.05714286 0.4230704
#> 2 158660 0.05714286 0.4230704
#> 3 189047 0.05714286 0.4230704
#> 4 244304 0.65714286 1.0000000
#> 5 171551 0.05714286 0.4230704
#> 6 263681 0.65714286 1.0000000
#> 7 192573 0.05714286 0.4230704
#> 8 322235 0.05714286 0.4230704
#> 9 180658 0.05714286 0.4230704
#> 10 326977 0.05714286 0.4230704
#> 11 259569 0.05714286 0.4230704
#> 12 325407 0.05714286 0.4230704
#> 13 361496 0.05714286 0.4230704
#> 14 316732 0.05714286 0.4230704
#> 15 291090 0.05714286 0.4230704
#> 16 348374 0.05714286 0.4230704
#> 17 196731 0.05714286 0.4230704
#> 18 278873 0.88571429 1.0000000
#> 19 290260 0.05714286 0.4230704
#> 20 361480 0.05714286 0.4230704
#> 21 193761 0.05714286 0.4230704
#> 22 357591 0.22857143 1.0000000
#> 23 127309 1.00000000 1.0000000
#> 24 357795 0.05714286 0.4230704
#> 25 517293 0.65714286 1.0000000
#> 26 223059 0.05714286 0.4230704
#> 27 248140 0.05714286 0.4230704
#> 28 256977 0.05714286 0.4230704
#> 29 287978 0.05714286 0.4230704
#> 30 357470 0.05714286 0.4230704
#> 31 367433 0.05714286 0.4230704
#> 32 187524 0.05714286 0.4230704
#> 33 211720 1.00000000 1.0000000
#> 34 36155 0.05714286 0.4230704
#> 35 518438 0.05714286 0.4230704
#> 36 327476 0.05714286 0.4230704
#> 37 309788 0.05714286 0.4230704
#> 38 470172 0.05714286 0.4230704
#> 39 240687 0.05714286 0.4230704
#> 40 3046 0.05714286 0.4230704
#> 41 470973 0.05714286 0.4230704
#> 42 199487 0.05714286 0.4230704
#> 43 191541 0.05714286 0.4230704
#> 44 573135 0.05714286 0.4230704
#> 45 365676 0.05714286 0.4230704
#> 46 47916 0.05714286 0.4230704
#> 47 549871 0.65714286 1.0000000
#> 48 269833 0.05714286 0.4230704
#> 49 194053 0.05714286 0.4230704
#> 50 220494 0.05714286 0.4230704
#> 51 190464 0.05714286 0.4230704
#> 52 288134 0.05714286 0.4230704
#> 53 194648 0.05714286 0.4230704
#> 54 589024 0.05714286 0.4230704
#> 55 279297 0.05714286 0.4230704
#> 56 306180 0.05714286 0.4230704
#> 57 347862 0.22857143 1.0000000
#> 58 138006 0.05714286 0.4230704
#> 59 212619 0.05714286 0.4230704
#> 60 268540 0.05714286 0.4230704
#> 61 27669 0.05714286 0.4230704
#> 62 293896 0.05714286 0.4230704
#> 63 195173 0.05714286 0.4230704
#> 64 150925 0.05714286 0.4230704
#> 65 249661 0.40000000 1.0000000
#> 66 192127 0.05714286 0.4230704
#> 67 565399 0.05714286 0.4230704
#> 68 111806 0.05714286 0.4230704
#> 69 302160 0.05714286 0.4230704
#> 70 193066 0.05714286 0.4230704
#> 71 578268 0.05714286 0.4230704
#> 72 352304 0.05714286 0.4230704
#> 73 469873 0.05714286 0.4230704
#> 74 183200 0.05714286 0.4230704
#> 75 65683 0.05714286 0.4230704
#> 76 177339 0.05714286 0.4230704
#> 77 193343 0.05714286 0.4230704
#> 78 203708 0.05714286 0.4230704
#> 79 42680 0.65714286 1.0000000
#> 80 328422 0.05714286 0.4230704
#> 81 208283 0.05714286 0.4230704
#> 82 326792 0.22857143 1.0000000
#> 83 261912 0.05714286 0.4230704
#> 84 71074 0.05714286 0.4230704
#> 85 509622 0.05714286 0.4230704
#> 86 175537 0.05714286 0.4230704
#> 87 557785 0.22857143 1.0000000
#> 88 405524 0.05714286 0.4230704
#> 89 546313 0.05714286 0.4230704
#> 90 360298 0.05714286 0.4230704
#> 91 171772 0.40000000 1.0000000
#> 92 154822 0.05714286 0.4230704
#> 93 541301 0.65714286 1.0000000
#> 94 2000 0.05714286 0.4230704
#> 95 469482 0.22857143 1.0000000
#> 96 179460 0.05714286 0.4230704
#> 97 565556 0.05714286 0.4230704
#> 98 109014 0.05714286 0.4230704
#> 99 100954 0.05714286 0.4230704
#> 100 223329 0.05714286 0.4230704
#> 101 181843 0.05714286 0.4230704
#> 102 366612 0.05714286 0.4230704
#> 103 310301 0.05714286 0.4230704
#> 104 367055 0.05714286 0.4230704
#> 105 181095 0.40000000 1.0000000
#> 106 162162 0.05714286 0.4230704
#> 107 299892 0.22857143 1.0000000
#> 108 175497 0.22857143 1.0000000
#> 109 166835 0.05714286 0.4230704
#> 110 150508 0.05714286 0.4230704
#> 111 190108 0.05714286 0.4230704
#> 112 170206 0.05714286 0.4230704
#> 113 313985 0.05714286 0.4230704
#> 114 574458 0.05714286 0.4230704
#> 115 113959 0.05714286 0.4230704
#> 116 366882 0.88571429 1.0000000
#> 117 470114 0.05714286 0.4230704
#> 118 544749 0.05714286 0.4230704
#> 119 247010 0.05714286 0.4230704
#> 120 154102 0.05714286 0.4230704
#> 121 129759 0.05714286 0.4230704
#> 122 113626 0.05714286 0.4230704
#> 123 82283 0.05714286 0.4230704
#> 124 262724 0.05714286 0.4230704
#> 125 273055 0.05714286 0.4230704
#> 126 551424 0.05714286 0.4230704
#> 127 262714 0.05714286 0.4230704
#> 128 111986 0.05714286 0.4230704
#> 129 340507 0.05714286 0.4230704
#> 130 84768 0.05714286 0.4230704
#> 131 250258 0.22857143 1.0000000
#> 132 104780 0.05714286 0.4230704
#> 133 109546 0.22857143 1.0000000
#> 134 544358 0.05714286 0.4230704
#> 135 158370 0.05714286 0.4230704
#> 136 566886 0.05714286 0.4230704
#> 137 320147 0.40000000 1.0000000
#> 138 306921 0.05714286 0.4230704
#> 139 114339 0.05714286 0.4230704
#> 140 243150 0.22857143 1.0000000
#> 141 188646 0.05714286 0.4230704
#> 142 216191 0.05714286 0.4230704
#> 143 191687 0.22857143 1.0000000
#> 144 352632 0.05714286 0.4230704
#> 145 245203 0.05714286 0.4230704
#> 146 204889 0.05714286 0.4230704
#> 147 203722 0.05714286 0.4230704
#> 148 469991 0.05714286 0.4230704
#> 149 265043 0.05714286 0.4230704
#> 150 511008 0.05714286 0.4230704
#> 151 184899 0.05714286 0.4230704
#> 152 302974 0.40000000 1.0000000
#> 153 346780 0.05714286 0.4230704
#> 154 174348 0.05714286 0.4230704
#> 155 278234 0.05714286 0.4230704
#> 156 242675 0.05714286 0.4230704
#> 157 185281 0.05714286 0.4230704
#> 158 167602 0.05714286 0.4230704
#> 159 470812 0.05714286 0.4230704
#> 160 559200 0.05714286 0.4230704
#> 161 367659 0.05714286 0.4230704
#> 162 161227 0.05714286 0.4230704
#> 163 213747 0.05714286 0.4230704
#> 164 277931 0.05714286 0.4230704
#> 165 235270 0.05714286 0.4230704
#> 166 181892 0.22857143 1.0000000
#> 167 527683 0.05714286 0.4230704
#> 168 151018 0.05714286 0.4230704
#> 169 145786 0.40000000 1.0000000
#> 170 113866 0.05714286 0.4230704
#> 171 190872 0.05714286 0.4230704
#> 172 178915 0.05714286 0.4230704
#> 173 181028 0.22857143 1.0000000
#> 174 252410 0.05714286 0.4230704
#> 175 331647 0.05714286 0.4230704
#> 176 356083 0.05714286 0.4230704
#> 177 182980 0.05714286 0.4230704
#> 178 200741 0.05714286 0.4230704
#> 179 244491 0.05714286 0.4230704
#> 180 204044 0.05714286 0.4230704
#> 181 550814 0.22857143 1.0000000
#> 182 191306 0.05714286 0.4230704
#> 183 51024 0.22857143 1.0000000
#> 184 510994 0.05714286 0.4230704
#> 185 308892 0.05714286 0.4230704
#> 186 211351 0.05714286 0.4230704
#> 187 1605 0.05714286 0.4230704
#> 188 250291 0.05714286 0.4230704
#> 189 360268 0.40000000 1.0000000
#> 190 218467 0.05714286 0.4230704
#> 191 286104 0.05714286 0.4230704
#> 192 511844 0.05714286 0.4230704
#> 193 263461 0.22857143 1.0000000
#> 194 21698 0.05714286 0.4230704
#> 195 154230 0.05714286 0.4230704
#> 196 543366 0.05714286 0.4230704
#> 197 549552 0.05714286 0.4230704
#> 198 535437 0.05714286 0.4230704
#> 199 265094 0.05714286 0.4230704
#> 200 159711 0.05714286 0.4230704
#> 201 144381 0.05714286 0.4230704
#> 202 255584 0.40000000 1.0000000
#> 203 215700 0.05714286 0.4230704
#> 204 275891 0.22857143 1.0000000
#> 205 584276 0.05714286 0.4230704
#> 206 368928 0.22857143 1.0000000
#> 207 570888 0.05714286 0.4230704
#> 208 278390 0.05714286 0.4230704
#> 209 363692 0.05714286 0.4230704
#> 210 216862 0.05714286 0.4230704
#> 211 230437 0.40000000 1.0000000
#> 212 212128 0.05714286 0.4230704
#> 213 170339 0.05714286 0.4230704
#> 214 582012 0.05714286 0.4230704
#> 215 93865 0.05714286 0.4230704
#> 216 469946 0.97142857 1.0000000
#> 217 204592 0.05714286 0.4230704
#> 218 252778 0.05714286 0.4230704
#> 219 251499 0.05714286 0.4230704
#> 220 1791 0.05714286 0.4230704
#> 221 70100 0.05714286 0.4230704
#> 222 97561 0.05714286 0.4230704
#> 223 278272 0.05714286 0.4230704
#> 224 103685 0.88571429 1.0000000
#> 225 154268 0.22857143 1.0000000
#> 226 130368 0.05714286 0.4230704
#> 227 76270 0.05714286 0.4230704
#> 228 190723 0.05714286 0.4230704
#> 229 208694 0.05714286 0.4230704
#> 230 193632 0.05714286 0.4230704
#> 231 222729 0.05714286 0.4230704
#> 232 206632 0.05714286 0.4230704
#> 233 203846 0.05714286 0.4230704
#> 234 156101 0.05714286 0.4230704
#> 235 575937 0.05714286 0.4230704
#> 236 349634 0.05714286 0.4230704
#> 237 565565 0.40000000 1.0000000
#> 238 547632 0.22857143 1.0000000
#> 239 553113 0.05714286 0.4230704
#> 240 13034 0.05714286 0.4230704
#> 241 210176 0.05714286 0.4230704
#> 242 308535 0.05714286 0.4230704
#> 243 197817 0.88571429 1.0000000
#> 244 109907 0.05714286 0.4230704
#> 245 113921 0.05714286 0.4230704
#> 246 173810 0.88571429 1.0000000
#> 247 215890 0.05714286 0.4230704
#> 248 326414 0.05714286 0.4230704
#> 249 294022 0.05714286 0.4230704
#> 250 534348 0.05714286 0.4230704
#>
#> $`WLX+LR`
#> feature Pval adjPval
#> 1 331820 0.9999776 1
#> 2 158660 0.9999776 1
#> 3 189047 0.9992225 1
#> 4 244304 0.3470014 1
#> 5 171551 0.9944800 1
#> 6 263681 0.6826497 1
#> 7 192573 0.9992225 1
#> 8 322235 0.9944800 1
#> 9 180658 0.9992225 1
#> 10 326977 0.9999776 1
#> 11 259569 0.9940110 1
#> 12 325407 0.9992225 1
#> 13 361496 0.9992225 1
#> 14 316732 0.9992225 1
#> 15 291090 0.9992225 1
#> 16 348374 0.9947456 1
#> 17 196731 0.9992225 1
#> 18 278873 0.9956216 1
#> 19 290260 0.9992225 1
#> 20 361480 0.9992225 1
#> 21 193761 0.9992225 1
#> 22 357591 0.9949612 1
#> 23 127309 1.0000000 1
#> 24 357795 0.9992225 1
#> 25 517293 0.9956216 1
#> 26 223059 0.9992225 1
#> 27 248140 0.9999776 1
#> 28 256977 0.9991767 1
#> 29 287978 0.9992225 1
#> 30 357470 0.9992225 1
#> 31 367433 0.9992225 1
#> 32 187524 0.9992225 1
#> 33 211720 1.0000000 1
#> 34 36155 0.9957018 1
#> 35 518438 0.9992225 1
#> 36 327476 0.9992225 1
#> 37 309788 0.9991767 1
#> 38 470172 0.9953806 1
#> 39 240687 0.9991767 1
#> 40 3046 0.9991767 1
#> 41 470973 0.9940110 1
#> 42 199487 0.9992225 1
#> 43 191541 0.9992225 1
#> 44 573135 0.9991767 1
#> 45 365676 0.9992225 1
#> 46 47916 0.9957018 1
#> 47 549871 0.9952811 1
#> 48 269833 0.9992225 1
#> 49 194053 0.9992225 1
#> 50 220494 0.9991767 1
#> 51 190464 0.9992225 1
#> 52 288134 0.9992225 1
#> 53 194648 0.9992225 1
#> 54 589024 0.9947456 1
#> 55 279297 0.9957018 1
#> 56 306180 0.9991767 1
#> 57 347862 0.9948662 1
#> 58 138006 0.9992225 1
#> 59 212619 0.9992225 1
#> 60 268540 0.9947456 1
#> 61 27669 0.9991767 1
#> 62 293896 0.9992225 1
#> 63 195173 0.9992225 1
#> 64 150925 0.9957018 1
#> 65 249661 0.9955481 1
#> 66 192127 0.9992225 1
#> 67 565399 0.9991767 1
#> 68 111806 0.9991767 1
#> 69 302160 0.9947456 1
#> 70 193066 0.9992225 1
#> 71 578268 0.9957018 1
#> 72 352304 0.9940110 1
#> 73 469873 0.9950639 1
#> 74 183200 0.9953806 1
#> 75 65683 0.9991767 1
#> 76 177339 0.9992225 1
#> 77 193343 0.9992225 1
#> 78 203708 0.9992225 1
#> 79 42680 0.9952546 1
#> 80 328422 0.9950639 1
#> 81 208283 0.9957018 1
#> 82 326792 0.9948662 1
#> 83 261912 0.9992225 1
#> 84 71074 0.9991767 1
#> 85 509622 0.9991767 1
#> 86 175537 0.9992225 1
#> 87 557785 0.9957829 1
#> 88 405524 0.9950639 1
#> 89 546313 0.9957018 1
#> 90 360298 0.9991767 1
#> 91 171772 0.9952255 1
#> 92 154822 0.9957018 1
#> 93 541301 0.9953381 1
#> 94 2000 0.9947456 1
#> 95 469482 0.9951704 1
#> 96 179460 0.9992225 1
#> 97 565556 0.9957018 1
#> 98 109014 0.9947456 1
#> 99 100954 0.9957018 1
#> 100 223329 0.9991767 1
#> 101 181843 0.9992225 1
#> 102 366612 0.9947456 1
#> 103 310301 0.9992225 1
#> 104 367055 0.9950639 1
#> 105 181095 0.9952255 1
#> 106 162162 0.9992225 1
#> 107 299892 0.9952546 1
#> 108 175497 0.9952255 1
#> 109 166835 0.9957018 1
#> 110 150508 0.9957018 1
#> 111 190108 0.9992225 1
#> 112 170206 0.9947456 1
#> 113 313985 0.9991767 1
#> 114 574458 0.9991767 1
#> 115 113959 0.9957018 1
#> 116 366882 1.0000000 1
#> 117 470114 0.9950639 1
#> 118 544749 0.9957018 1
#> 119 247010 0.9991767 1
#> 120 154102 0.9991767 1
#> 121 129759 0.9991767 1
#> 122 113626 0.9991767 1
#> 123 82283 0.9992225 1
#> 124 262724 0.9992225 1
#> 125 273055 0.9991767 1
#> 126 551424 0.9991767 1
#> 127 262714 0.9991767 1
#> 128 111986 0.9959789 1
#> 129 340507 0.9991767 1
#> 130 84768 0.9991767 1
#> 131 250258 0.9957829 1
#> 132 104780 0.9947456 1
#> 133 109546 0.9952255 1
#> 134 544358 0.9950639 1
#> 135 158370 0.9991767 1
#> 136 566886 0.9991767 1
#> 137 320147 0.9955225 1
#> 138 306921 0.9957018 1
#> 139 114339 0.9957018 1
#> 140 243150 0.9955225 1
#> 141 188646 0.9992225 1
#> 142 216191 0.9991767 1
#> 143 191687 0.9949283 1
#> 144 352632 0.9991767 1
#> 145 245203 0.9991767 1
#> 146 204889 0.9950639 1
#> 147 203722 0.9957018 1
#> 148 469991 0.9992225 1
#> 149 265043 0.9991767 1
#> 150 511008 0.9940110 1
#> 151 184899 0.9957018 1
#> 152 302974 0.9952255 1
#> 153 346780 0.9992225 1
#> 154 174348 0.9992225 1
#> 155 278234 0.9947456 1
#> 156 242675 0.9991767 1
#> 157 185281 0.9992225 1
#> 158 167602 0.9992225 1
#> 159 470812 0.9950639 1
#> 160 559200 0.9991767 1
#> 161 367659 0.9991767 1
#> 162 161227 0.9991767 1
#> 163 213747 0.9947456 1
#> 164 277931 0.9991767 1
#> 165 235270 0.9957018 1
#> 166 181892 0.9948662 1
#> 167 527683 0.9991767 1
#> 168 151018 0.9957018 1
#> 169 145786 0.9958675 1
#> 170 113866 0.9991767 1
#> 171 190872 0.9947456 1
#> 172 178915 0.9947456 1
#> 173 181028 0.9954741 1
#> 174 252410 0.9957018 1
#> 175 331647 0.9957018 1
#> 176 356083 0.9991767 1
#> 177 182980 0.9992225 1
#> 178 200741 0.9991767 1
#> 179 244491 0.9957018 1
#> 180 204044 0.9992225 1
#> 181 550814 0.9954741 1
#> 182 191306 0.9947456 1
#> 183 51024 0.9957829 1
#> 184 510994 0.9962455 1
#> 185 308892 0.9991767 1
#> 186 211351 0.9957018 1
#> 187 1605 0.9991767 1
#> 188 250291 0.9991767 1
#> 189 360268 0.9955225 1
#> 190 218467 0.9991767 1
#> 191 286104 0.9991767 1
#> 192 511844 0.9991767 1
#> 193 263461 0.9949283 1
#> 194 21698 0.9947456 1
#> 195 154230 0.9957018 1
#> 196 543366 0.9991767 1
#> 197 549552 0.9957018 1
#> 198 535437 0.9991767 1
#> 199 265094 0.9957018 1
#> 200 159711 0.9991767 1
#> 201 144381 0.9991767 1
#> 202 255584 0.9958249 1
#> 203 215700 0.9957018 1
#> 204 275891 0.9954741 1
#> 205 584276 0.9991767 1
#> 206 368928 0.9955225 1
#> 207 570888 0.9991767 1
#> 208 278390 0.9991767 1
#> 209 363692 0.9950639 1
#> 210 216862 0.9992225 1
#> 211 230437 0.9955481 1
#> 212 212128 0.9953806 1
#> 213 170339 0.9991767 1
#> 214 582012 0.9957018 1
#> 215 93865 0.9991767 1
#> 216 469946 0.9956216 1
#> 217 204592 0.9957018 1
#> 218 252778 0.9957018 1
#> 219 251499 0.9991767 1
#> 220 1791 0.9957018 1
#> 221 70100 0.9991767 1
#> 222 97561 0.9991767 1
#> 223 278272 0.9991767 1
#> 224 103685 1.0000000 1
#> 225 154268 0.9957829 1
#> 226 130368 0.9991767 1
#> 227 76270 0.9991767 1
#> 228 190723 0.9991767 1
#> 229 208694 0.9991767 1
#> 230 193632 0.9947456 1
#> 231 222729 0.9991767 1
#> 232 206632 0.9957018 1
#> 233 203846 0.9991767 1
#> 234 156101 0.9991767 1
#> 235 575937 0.9957018 1
#> 236 349634 0.9957018 1
#> 237 565565 0.9955481 1
#> 238 547632 0.9957829 1
#> 239 553113 0.9957018 1
#> 240 13034 0.9991767 1
#> 241 210176 0.9957018 1
#> 242 308535 0.9957018 1
#> 243 197817 0.9955714 1
#> 244 109907 0.9991767 1
#> 245 113921 0.9957018 1
#> 246 173810 1.0000000 1
#> 247 215890 0.9953806 1
#> 248 326414 0.9991767 1
#> 249 294022 0.9992225 1
#> 250 534348 0.9957018 1
#>
#> $`WLX+KS`
#> feature Pval adjPval
#> 1 331820 0.05714286 0.4230704
#> 2 158660 0.05714286 0.4230704
#> 3 189047 0.05714286 0.4230704
#> 4 244304 0.64316632 1.0000000
#> 5 171551 0.05714286 0.4230704
#> 6 263681 0.79214898 1.0000000
#> 7 192573 0.05714286 0.4230704
#> 8 322235 0.05714286 0.4230704
#> 9 180658 0.05714286 0.4230704
#> 10 326977 0.05714286 0.4230704
#> 11 259569 0.05714286 0.4230704
#> 12 325407 0.05714286 0.4230704
#> 13 361496 0.05714286 0.4230704
#> 14 316732 0.05714286 0.4230704
#> 15 291090 0.05714286 0.4230704
#> 16 348374 0.05714286 0.4230704
#> 17 196731 0.05714286 0.4230704
#> 18 278873 0.87293060 1.0000000
#> 19 290260 0.05714286 0.4230704
#> 20 361480 0.05714286 0.4230704
#> 21 193761 0.05714286 0.4230704
#> 22 357591 0.29829553 1.0000000
#> 23 127309 1.00000000 1.0000000
#> 24 357795 0.05714286 0.4230704
#> 25 517293 0.79214898 1.0000000
#> 26 223059 0.05714286 0.4230704
#> 27 248140 0.05714286 0.4230704
#> 28 256977 0.05714286 0.4230704
#> 29 287978 0.05714286 0.4230704
#> 30 357470 0.05714286 0.4230704
#> 31 367433 0.05714286 0.4230704
#> 32 187524 0.05714286 0.4230704
#> 33 211720 1.00000000 1.0000000
#> 34 36155 0.05714286 0.4230704
#> 35 518438 0.05714286 0.4230704
#> 36 327476 0.05714286 0.4230704
#> 37 309788 0.05714286 0.4230704
#> 38 470172 0.05714286 0.4230704
#> 39 240687 0.05714286 0.4230704
#> 40 3046 0.05714286 0.4230704
#> 41 470973 0.05714286 0.4230704
#> 42 199487 0.05714286 0.4230704
#> 43 191541 0.05714286 0.4230704
#> 44 573135 0.05714286 0.4230704
#> 45 365676 0.05714286 0.4230704
#> 46 47916 0.05714286 0.4230704
#> 47 549871 0.64316632 1.0000000
#> 48 269833 0.05714286 0.4230704
#> 49 194053 0.05714286 0.4230704
#> 50 220494 0.05714286 0.4230704
#> 51 190464 0.05714286 0.4230704
#> 52 288134 0.05714286 0.4230704
#> 53 194648 0.05714286 0.4230704
#> 54 589024 0.05714286 0.4230704
#> 55 279297 0.05714286 0.4230704
#> 56 306180 0.05714286 0.4230704
#> 57 347862 0.15390114 1.0000000
#> 58 138006 0.05714286 0.4230704
#> 59 212619 0.05714286 0.4230704
#> 60 268540 0.05714286 0.4230704
#> 61 27669 0.05714286 0.4230704
#> 62 293896 0.05714286 0.4230704
#> 63 195173 0.05714286 0.4230704
#> 64 150925 0.05714286 0.4230704
#> 65 249661 0.40000000 1.0000000
#> 66 192127 0.05714286 0.4230704
#> 67 565399 0.05714286 0.4230704
#> 68 111806 0.05714286 0.4230704
#> 69 302160 0.05714286 0.4230704
#> 70 193066 0.05714286 0.4230704
#> 71 578268 0.05714286 0.4230704
#> 72 352304 0.05714286 0.4230704
#> 73 469873 0.05714286 0.4230704
#> 74 183200 0.05714286 0.4230704
#> 75 65683 0.05714286 0.4230704
#> 76 177339 0.05714286 0.4230704
#> 77 193343 0.05714286 0.4230704
#> 78 203708 0.05714286 0.4230704
#> 79 42680 0.53380469 1.0000000
#> 80 328422 0.05714286 0.4230704
#> 81 208283 0.05714286 0.4230704
#> 82 326792 0.15390114 1.0000000
#> 83 261912 0.05714286 0.4230704
#> 84 71074 0.05714286 0.4230704
#> 85 509622 0.05714286 0.4230704
#> 86 175537 0.05714286 0.4230704
#> 87 557785 0.15390114 1.0000000
#> 88 405524 0.05714286 0.4230704
#> 89 546313 0.05714286 0.4230704
#> 90 360298 0.05714286 0.4230704
#> 91 171772 0.29829553 1.0000000
#> 92 154822 0.05714286 0.4230704
#> 93 541301 0.79214898 1.0000000
#> 94 2000 0.05714286 0.4230704
#> 95 469482 0.15390114 1.0000000
#> 96 179460 0.05714286 0.4230704
#> 97 565556 0.05714286 0.4230704
#> 98 109014 0.05714286 0.4230704
#> 99 100954 0.05714286 0.4230704
#> 100 223329 0.05714286 0.4230704
#> 101 181843 0.05714286 0.4230704
#> 102 366612 0.05714286 0.4230704
#> 103 310301 0.05714286 0.4230704
#> 104 367055 0.05714286 0.4230704
#> 105 181095 0.29829553 1.0000000
#> 106 162162 0.05714286 0.4230704
#> 107 299892 0.29829553 1.0000000
#> 108 175497 0.22857143 1.0000000
#> 109 166835 0.05714286 0.4230704
#> 110 150508 0.05714286 0.4230704
#> 111 190108 0.05714286 0.4230704
#> 112 170206 0.05714286 0.4230704
#> 113 313985 0.05714286 0.4230704
#> 114 574458 0.05714286 0.4230704
#> 115 113959 0.05714286 0.4230704
#> 116 366882 1.00000000 1.0000000
#> 117 470114 0.05714286 0.4230704
#> 118 544749 0.05714286 0.4230704
#> 119 247010 0.05714286 0.4230704
#> 120 154102 0.05714286 0.4230704
#> 121 129759 0.05714286 0.4230704
#> 122 113626 0.05714286 0.4230704
#> 123 82283 0.05714286 0.4230704
#> 124 262724 0.05714286 0.4230704
#> 125 273055 0.05714286 0.4230704
#> 126 551424 0.05714286 0.4230704
#> 127 262714 0.05714286 0.4230704
#> 128 111986 0.05714286 0.4230704
#> 129 340507 0.05714286 0.4230704
#> 130 84768 0.05714286 0.4230704
#> 131 250258 0.15390114 1.0000000
#> 132 104780 0.05714286 0.4230704
#> 133 109546 0.22857143 1.0000000
#> 134 544358 0.05714286 0.4230704
#> 135 158370 0.05714286 0.4230704
#> 136 566886 0.05714286 0.4230704
#> 137 320147 0.29829553 1.0000000
#> 138 306921 0.05714286 0.4230704
#> 139 114339 0.05714286 0.4230704
#> 140 243150 0.22857143 1.0000000
#> 141 188646 0.05714286 0.4230704
#> 142 216191 0.05714286 0.4230704
#> 143 191687 0.22857143 1.0000000
#> 144 352632 0.05714286 0.4230704
#> 145 245203 0.05714286 0.4230704
#> 146 204889 0.05714286 0.4230704
#> 147 203722 0.05714286 0.4230704
#> 148 469991 0.05714286 0.4230704
#> 149 265043 0.05714286 0.4230704
#> 150 511008 0.05714286 0.4230704
#> 151 184899 0.05714286 0.4230704
#> 152 302974 0.29829553 1.0000000
#> 153 346780 0.05714286 0.4230704
#> 154 174348 0.05714286 0.4230704
#> 155 278234 0.05714286 0.4230704
#> 156 242675 0.05714286 0.4230704
#> 157 185281 0.05714286 0.4230704
#> 158 167602 0.05714286 0.4230704
#> 159 470812 0.05714286 0.4230704
#> 160 559200 0.05714286 0.4230704
#> 161 367659 0.05714286 0.4230704
#> 162 161227 0.05714286 0.4230704
#> 163 213747 0.05714286 0.4230704
#> 164 277931 0.05714286 0.4230704
#> 165 235270 0.05714286 0.4230704
#> 166 181892 0.15390114 1.0000000
#> 167 527683 0.05714286 0.4230704
#> 168 151018 0.05714286 0.4230704
#> 169 145786 0.51629925 1.0000000
#> 170 113866 0.05714286 0.4230704
#> 171 190872 0.05714286 0.4230704
#> 172 178915 0.05714286 0.4230704
#> 173 181028 0.15390114 1.0000000
#> 174 252410 0.05714286 0.4230704
#> 175 331647 0.05714286 0.4230704
#> 176 356083 0.05714286 0.4230704
#> 177 182980 0.05714286 0.4230704
#> 178 200741 0.05714286 0.4230704
#> 179 244491 0.05714286 0.4230704
#> 180 204044 0.05714286 0.4230704
#> 181 550814 0.15390114 1.0000000
#> 182 191306 0.05714286 0.4230704
#> 183 51024 0.15390114 1.0000000
#> 184 510994 0.05714286 0.4230704
#> 185 308892 0.05714286 0.4230704
#> 186 211351 0.05714286 0.4230704
#> 187 1605 0.05714286 0.4230704
#> 188 250291 0.05714286 0.4230704
#> 189 360268 0.29829553 1.0000000
#> 190 218467 0.05714286 0.4230704
#> 191 286104 0.05714286 0.4230704
#> 192 511844 0.05714286 0.4230704
#> 193 263461 0.22857143 1.0000000
#> 194 21698 0.05714286 0.4230704
#> 195 154230 0.05714286 0.4230704
#> 196 543366 0.05714286 0.4230704
#> 197 549552 0.05714286 0.4230704
#> 198 535437 0.05714286 0.4230704
#> 199 265094 0.05714286 0.4230704
#> 200 159711 0.05714286 0.4230704
#> 201 144381 0.05714286 0.4230704
#> 202 255584 0.29829553 1.0000000
#> 203 215700 0.05714286 0.4230704
#> 204 275891 0.15390114 1.0000000
#> 205 584276 0.05714286 0.4230704
#> 206 368928 0.22857143 1.0000000
#> 207 570888 0.05714286 0.4230704
#> 208 278390 0.05714286 0.4230704
#> 209 363692 0.05714286 0.4230704
#> 210 216862 0.05714286 0.4230704
#> 211 230437 0.40000000 1.0000000
#> 212 212128 0.05714286 0.4230704
#> 213 170339 0.05714286 0.4230704
#> 214 582012 0.05714286 0.4230704
#> 215 93865 0.05714286 0.4230704
#> 216 469946 0.95209035 1.0000000
#> 217 204592 0.05714286 0.4230704
#> 218 252778 0.05714286 0.4230704
#> 219 251499 0.05714286 0.4230704
#> 220 1791 0.05714286 0.4230704
#> 221 70100 0.05714286 0.4230704
#> 222 97561 0.05714286 0.4230704
#> 223 278272 0.05714286 0.4230704
#> 224 103685 1.00000000 1.0000000
#> 225 154268 0.15390114 1.0000000
#> 226 130368 0.05714286 0.4230704
#> 227 76270 0.05714286 0.4230704
#> 228 190723 0.05714286 0.4230704
#> 229 208694 0.05714286 0.4230704
#> 230 193632 0.05714286 0.4230704
#> 231 222729 0.05714286 0.4230704
#> 232 206632 0.05714286 0.4230704
#> 233 203846 0.05714286 0.4230704
#> 234 156101 0.05714286 0.4230704
#> 235 575937 0.05714286 0.4230704
#> 236 349634 0.05714286 0.4230704
#> 237 565565 0.40000000 1.0000000
#> 238 547632 0.15390114 1.0000000
#> 239 553113 0.05714286 0.4230704
#> 240 13034 0.05714286 0.4230704
#> 241 210176 0.05714286 0.4230704
#> 242 308535 0.05714286 0.4230704
#> 243 197817 0.81724014 1.0000000
#> 244 109907 0.05714286 0.4230704
#> 245 113921 0.05714286 0.4230704
#> 246 173810 1.00000000 1.0000000
#> 247 215890 0.05714286 0.4230704
#> 248 326414 0.05714286 0.4230704
#> 249 294022 0.05714286 0.4230704
#> 250 534348 0.05714286 0.4230704
#>
#> $`LR+KS`
#> feature Pval adjPval
#> 1 331820 0.9999776 1
#> 2 158660 0.9999776 1
#> 3 189047 0.9992225 1
#> 4 244304 0.3611660 1
#> 5 171551 0.9944800 1
#> 6 263681 0.4610935 1
#> 7 192573 0.9992225 1
#> 8 322235 0.9944800 1
#> 9 180658 0.9992225 1
#> 10 326977 0.9999776 1
#> 11 259569 0.9940110 1
#> 12 325407 0.9992225 1
#> 13 361496 0.9992225 1
#> 14 316732 0.9992225 1
#> 15 291090 0.9992225 1
#> 16 348374 0.9947456 1
#> 17 196731 0.9992225 1
#> 18 278873 0.9956393 1
#> 19 290260 0.9992225 1
#> 20 361480 0.9992225 1
#> 21 193761 0.9992225 1
#> 22 357591 0.9949283 1
#> 23 127309 1.0000000 1
#> 24 357795 0.9992225 1
#> 25 517293 0.9955748 1
#> 26 223059 0.9992225 1
#> 27 248140 0.9999776 1
#> 28 256977 0.9991767 1
#> 29 287978 0.9992225 1
#> 30 357470 0.9992225 1
#> 31 367433 0.9992225 1
#> 32 187524 0.9992225 1
#> 33 211720 1.0000000 1
#> 34 36155 0.9957018 1
#> 35 518438 0.9992225 1
#> 36 327476 0.9992225 1
#> 37 309788 0.9991767 1
#> 38 470172 0.9953806 1
#> 39 240687 0.9991767 1
#> 40 3046 0.9991767 1
#> 41 470973 0.9940110 1
#> 42 199487 0.9992225 1
#> 43 191541 0.9992225 1
#> 44 573135 0.9991767 1
#> 45 365676 0.9992225 1
#> 46 47916 0.9957018 1
#> 47 549871 0.9952850 1
#> 48 269833 0.9992225 1
#> 49 194053 0.9992225 1
#> 50 220494 0.9991767 1
#> 51 190464 0.9992225 1
#> 52 288134 0.9992225 1
#> 53 194648 0.9992225 1
#> 54 589024 0.9947456 1
#> 55 279297 0.9957018 1
#> 56 306180 0.9991767 1
#> 57 347862 0.9949283 1
#> 58 138006 0.9992225 1
#> 59 212619 0.9992225 1
#> 60 268540 0.9947456 1
#> 61 27669 0.9991767 1
#> 62 293896 0.9992225 1
#> 63 195173 0.9992225 1
#> 64 150925 0.9957018 1
#> 65 249661 0.9955481 1
#> 66 192127 0.9992225 1
#> 67 565399 0.9991767 1
#> 68 111806 0.9991767 1
#> 69 302160 0.9947456 1
#> 70 193066 0.9992225 1
#> 71 578268 0.9957018 1
#> 72 352304 0.9940110 1
#> 73 469873 0.9950639 1
#> 74 183200 0.9953806 1
#> 75 65683 0.9991767 1
#> 76 177339 0.9992225 1
#> 77 193343 0.9992225 1
#> 78 203708 0.9992225 1
#> 79 42680 0.9952850 1
#> 80 328422 0.9950639 1
#> 81 208283 0.9957018 1
#> 82 326792 0.9949283 1
#> 83 261912 0.9992225 1
#> 84 71074 0.9991767 1
#> 85 509622 0.9991767 1
#> 86 175537 0.9992225 1
#> 87 557785 0.9958249 1
#> 88 405524 0.9950639 1
#> 89 546313 0.9957018 1
#> 90 360298 0.9991767 1
#> 91 171772 0.9952546 1
#> 92 154822 0.9957018 1
#> 93 541301 0.9952850 1
#> 94 2000 0.9947456 1
#> 95 469482 0.9952255 1
#> 96 179460 0.9992225 1
#> 97 565556 0.9957018 1
#> 98 109014 0.9947456 1
#> 99 100954 0.9957018 1
#> 100 223329 0.9991767 1
#> 101 181843 0.9992225 1
#> 102 366612 0.9947456 1
#> 103 310301 0.9992225 1
#> 104 367055 0.9950639 1
#> 105 181095 0.9952546 1
#> 106 162162 0.9992225 1
#> 107 299892 0.9952255 1
#> 108 175497 0.9952255 1
#> 109 166835 0.9957018 1
#> 110 150508 0.9957018 1
#> 111 190108 0.9992225 1
#> 112 170206 0.9947456 1
#> 113 313985 0.9991767 1
#> 114 574458 0.9991767 1
#> 115 113959 0.9957018 1
#> 116 366882 0.9956393 1
#> 117 470114 0.9950639 1
#> 118 544749 0.9957018 1
#> 119 247010 0.9991767 1
#> 120 154102 0.9991767 1
#> 121 129759 0.9991767 1
#> 122 113626 0.9991767 1
#> 123 82283 0.9992225 1
#> 124 262724 0.9992225 1
#> 125 273055 0.9991767 1
#> 126 551424 0.9991767 1
#> 127 262714 0.9991767 1
#> 128 111986 0.9959789 1
#> 129 340507 0.9991767 1
#> 130 84768 0.9991767 1
#> 131 250258 0.9958249 1
#> 132 104780 0.9947456 1
#> 133 109546 0.9952255 1
#> 134 544358 0.9950639 1
#> 135 158370 0.9991767 1
#> 136 566886 0.9991767 1
#> 137 320147 0.9955481 1
#> 138 306921 0.9957018 1
#> 139 114339 0.9957018 1
#> 140 243150 0.9955225 1
#> 141 188646 0.9992225 1
#> 142 216191 0.9991767 1
#> 143 191687 0.9949283 1
#> 144 352632 0.9991767 1
#> 145 245203 0.9991767 1
#> 146 204889 0.9950639 1
#> 147 203722 0.9957018 1
#> 148 469991 0.9992225 1
#> 149 265043 0.9991767 1
#> 150 511008 0.9940110 1
#> 151 184899 0.9957018 1
#> 152 302974 0.9952546 1
#> 153 346780 0.9992225 1
#> 154 174348 0.9992225 1
#> 155 278234 0.9947456 1
#> 156 242675 0.9991767 1
#> 157 185281 0.9992225 1
#> 158 167602 0.9992225 1
#> 159 470812 0.9950639 1
#> 160 559200 0.9991767 1
#> 161 367659 0.9991767 1
#> 162 161227 0.9991767 1
#> 163 213747 0.9947456 1
#> 164 277931 0.9991767 1
#> 165 235270 0.9957018 1
#> 166 181892 0.9949283 1
#> 167 527683 0.9991767 1
#> 168 151018 0.9957018 1
#> 169 145786 0.9958472 1
#> 170 113866 0.9991767 1
#> 171 190872 0.9947456 1
#> 172 178915 0.9947456 1
#> 173 181028 0.9955225 1
#> 174 252410 0.9957018 1
#> 175 331647 0.9957018 1
#> 176 356083 0.9991767 1
#> 177 182980 0.9992225 1
#> 178 200741 0.9991767 1
#> 179 244491 0.9957018 1
#> 180 204044 0.9992225 1
#> 181 550814 0.9955225 1
#> 182 191306 0.9947456 1
#> 183 51024 0.9958249 1
#> 184 510994 0.9962455 1
#> 185 308892 0.9991767 1
#> 186 211351 0.9957018 1
#> 187 1605 0.9991767 1
#> 188 250291 0.9991767 1
#> 189 360268 0.9955481 1
#> 190 218467 0.9991767 1
#> 191 286104 0.9991767 1
#> 192 511844 0.9991767 1
#> 193 263461 0.9949283 1
#> 194 21698 0.9947456 1
#> 195 154230 0.9957018 1
#> 196 543366 0.9991767 1
#> 197 549552 0.9957018 1
#> 198 535437 0.9991767 1
#> 199 265094 0.9957018 1
#> 200 159711 0.9991767 1
#> 201 144381 0.9991767 1
#> 202 255584 0.9958472 1
#> 203 215700 0.9957018 1
#> 204 275891 0.9955225 1
#> 205 584276 0.9991767 1
#> 206 368928 0.9955225 1
#> 207 570888 0.9991767 1
#> 208 278390 0.9991767 1
#> 209 363692 0.9950639 1
#> 210 216862 0.9992225 1
#> 211 230437 0.9955481 1
#> 212 212128 0.9953806 1
#> 213 170339 0.9991767 1
#> 214 582012 0.9957018 1
#> 215 93865 0.9991767 1
#> 216 469946 0.9958778 1
#> 217 204592 0.9957018 1
#> 218 252778 0.9957018 1
#> 219 251499 0.9991767 1
#> 220 1791 0.9957018 1
#> 221 70100 0.9991767 1
#> 222 97561 0.9991767 1
#> 223 278272 0.9991767 1
#> 224 103685 0.9956393 1
#> 225 154268 0.9958249 1
#> 226 130368 0.9991767 1
#> 227 76270 0.9991767 1
#> 228 190723 0.9991767 1
#> 229 208694 0.9991767 1
#> 230 193632 0.9947456 1
#> 231 222729 0.9991767 1
#> 232 206632 0.9957018 1
#> 233 203846 0.9991767 1
#> 234 156101 0.9991767 1
#> 235 575937 0.9957018 1
#> 236 349634 0.9957018 1
#> 237 565565 0.9955481 1
#> 238 547632 0.9958249 1
#> 239 553113 0.9957018 1
#> 240 13034 0.9991767 1
#> 241 210176 0.9957018 1
#> 242 308535 0.9957018 1
#> 243 197817 0.9956393 1
#> 244 109907 0.9991767 1
#> 245 113921 0.9957018 1
#> 246 173810 0.9956393 1
#> 247 215890 0.9953806 1
#> 248 326414 0.9991767 1
#> 249 294022 0.9992225 1
#> 250 534348 0.9957018 1Because the core method is NULL, this example combines
only enhancer tests. The $ensembles element reports the CCT
results for the enhancer combinations when
return_subensembles = TRUE.
The single-cell example uses a small
SingleCellExperiment object with two cell groups. The
object is wrapped in a MultiAssayExperiment before being
passed to DAssemble, matching the same input architecture
used above.
set.seed(1)
n_genes <- 120L
n_cells <- 20L
cell_type <- factor(rep(c("control", "treated"), each = n_cells / 2L))
counts <- matrix(
stats::rnbinom(n_genes * n_cells, mu = 20, size = 5),
nrow = n_genes,
dimnames = list(
paste0("gene", seq_len(n_genes)),
paste0("cell", seq_len(n_cells))
)
)
counts[seq_len(12L), cell_type == "treated"] <-
counts[seq_len(12L), cell_type == "treated"] + 15L
metadata <- data.frame(
CellType = cell_type,
row.names = colnames(counts)
)
sce <- SingleCellExperiment::SingleCellExperiment(
assays = list(counts = counts),
colData = S4Vectors::DataFrame(metadata)
)
mae <- MultiAssayExperiment::MultiAssayExperiment(
experiments = list(single_cell = sce),
colData = S4Vectors::DataFrame(metadata)
)
res <- DAssemble::DAssemble(
features = mae,
assay_name = "single_cell",
core_method = "edgeR",
enhancers = c("WLX", "LR", "KS"),
enhancer_norm = "TSS",
expVar = "CellType",
p_adj = "BH",
return_components = TRUE,
return_subensembles = TRUE
)
#> calcNormFactors has been renamed to normLibSizes
head(res$res)
#> feature metadata pval_core pval_WLX coef_LR pval_LR pval_KS
#> 1 gene1 CellType 0.1319973332 0.089209552 -2.205449e-06 1 0.1678213427
#> 2 gene2 CellType 0.0127052773 0.006841456 -2.205449e-06 1 0.0123406006
#> 3 gene3 CellType 0.0610776934 0.089209552 -2.205449e-06 1 0.1678213427
#> 4 gene4 CellType 0.0001603293 0.001050034 -2.205449e-06 1 0.0002165018
#> 5 gene5 CellType 0.0791618751 0.063012839 -2.205449e-06 1 0.0524475524
#> 6 gene6 CellType 0.0032635317 0.008930698 -2.205449e-06 1 0.0123406006
#> pval_joint qval
#> 1 1 1
#> 2 1 1
#> 3 1 1
#> 4 1 1
#> 5 1 1
#> 6 1 1
names(res$components)
#> [1] "edgeR" "WLX" "LR" "KS"sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] BiocStyle_2.41.0
#>
#> loaded via a namespace (and not attached):
#> [1] ade4_1.7-24 tidyselect_1.2.1
#> [3] dplyr_1.2.1 farver_2.1.2
#> [5] Biostrings_2.81.5 S7_0.2.2
#> [7] SingleCellExperiment_1.35.2 fastmap_1.2.0
#> [9] phyloseq_1.57.0 digest_0.6.39
#> [11] lifecycle_1.0.5 cluster_2.1.8.2
#> [13] survival_3.8-9 statmod_1.5.2
#> [15] magrittr_2.0.5 compiler_4.6.1
#> [17] rlang_1.3.0 sass_0.4.10
#> [19] tools_4.6.1 igraph_2.3.3
#> [21] yaml_2.3.12 data.table_1.18.4
#> [23] knitr_1.51 S4Arrays_1.13.0
#> [25] DelayedArray_0.39.3 plyr_1.8.9
#> [27] RColorBrewer_1.1-3 abind_1.4-8
#> [29] BiocParallel_1.47.0 withr_3.0.3
#> [31] BiocGenerics_0.59.10 sys_3.4.3
#> [33] grid_4.6.1 stats4_4.6.1
#> [35] multtest_2.69.0 biomformat_1.41.0
#> [37] edgeR_4.11.4 ggplot2_4.0.3
#> [39] scales_1.4.0 iterators_1.0.14
#> [41] MASS_7.3-66 MultiAssayExperiment_1.39.0
#> [43] SummarizedExperiment_1.43.0 cli_3.6.6
#> [45] vegan_2.7-5 rmarkdown_2.31
#> [47] crayon_1.5.3 generics_0.1.4
#> [49] otel_0.2.0 reshape2_1.4.5
#> [51] ape_5.8-1 cachem_1.1.0
#> [53] stringr_1.6.0 splines_4.6.1
#> [55] parallel_4.6.1 BiocManager_1.30.27
#> [57] XVector_0.53.0 matrixStats_1.5.0
#> [59] vctrs_0.7.3 Matrix_1.7-5
#> [61] jsonlite_2.0.0 IRanges_2.47.2
#> [63] S4Vectors_0.51.5 maketools_1.3.2
#> [65] locfit_1.5-9.12 foreach_1.5.2
#> [67] limma_3.69.2 jquerylib_0.1.4
#> [69] glue_1.8.1 codetools_0.2-20
#> [71] stringi_1.8.7 gtable_0.3.6
#> [73] GenomicRanges_1.65.1 tibble_3.3.1
#> [75] pillar_1.11.1 htmltools_0.5.9
#> [77] Seqinfo_1.3.0 R6_2.6.1
#> [79] evaluate_1.0.5 lattice_0.22-9
#> [81] Biobase_2.73.1 DAssemble_0.99.3
#> [83] bslib_0.11.0 Rcpp_1.1.2
#> [85] permute_0.9-10 SparseArray_1.13.2
#> [87] nlme_3.1-170 mgcv_1.9-4
#> [89] DESeq2_1.53.0 xfun_0.60
#> [91] MatrixGenerics_1.25.0 buildtools_1.0.0
#> [93] pkgconfig_2.0.3