Introduction
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.
Overview
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.
The main DAssemble() function also supports:
- multiple adjustment covariates through
coVars
- repeated-measures / longitudinal designs through
random_effects
- open-ended method-specific controls through
method_args
These arguments are available only for methods whose wrappers can express the
corresponding model directly. For unsupported methods, DAssemble stops with a
clear error rather than fitting a mis-specified model.
Main API
The primary entry point is:
DAssemble(
features,
metadata = NULL,
core_method = NULL,
enhancers = NULL,
expVar = "group",
coVars = NULL,
random_effects = NULL,
method_args = NULL,
assay_name = NULL,
p_adj = "BY",
enhancer_norm = "TSS",
return_components = TRUE,
return_subensembles = FALSE
)
method_args is a named control list used to forward additional arguments to
underlying methods. When an argument overlaps with a DAssemble wrapper default,
the value in method_args takes precedence. The usual pattern is:
method_args$core for arguments shared by the selected core methodmethod_args$enhancer for arguments shared by the selected enhancersmethod_args$<MethodName> for method-specific overrides
For the LR enhancer, the wrapper additionally recognizes
method_args$LR$separation_method. The default is "augment",
which follows the MaAsLin3-style augmented logistic fit. For
cross-sectional presence/absence logistic analyses, users can instead request
"firth" to use bias-reduced logistic regression through brglm2.
For the Maaslin2 core, the wrapper also recognizes
method_args$Maaslin2$median_comparison = TRUE. When requested, DAssemble
uses raw fit$results, applies the internal median_comparison_tweedie()
adjustment there, and only then reduces the output back to the standardized
exposure-specific result table.
Wrappers with multi-stage internals can also accept subcall-specific entries.
For example, method_args$DESeq2 may contain items such as DESeq,
results, or estimateSizeFactors, while method_args$edgeR may contain
items such as glmFit or glmLRT.
res <- DAssemble(
features = features,
metadata = metadata,
enhancers = "LR",
expVar = "group",
method_args = list(
LR = list(separation_method = "firth")
)
)
For example, MaAsLin3’s prevalence median comparison can be enabled with:
res <- DAssemble(
features = features,
metadata = metadata,
core_method = "Maaslin3",
expVar = "group",
method_args = list(
Maaslin3 = list(median_comparison_prevalence = TRUE)
)
)
Supported methods
Core methods currently available in DAssemble() include:
DESeq2edgeRlimmaVOOMmetagenomeSeqMASTdearseqALDEx2LinDALOCOMMaaslin2Maaslin3TweedieverseRobseqANCOMBC2
Enhancers currently available are:
Covariate and longitudinal support
The current implementation supports multiple covariates (coVars) for the
following methods:
When random_effects is supplied, the following methods are currently treated
as longitudinal-compatible:
All other methods should be treated as cross-sectional only in the current
package implementation.
For the LR enhancer, the default fitting path uses MaAsLin3-style
augmentation in both cross-sectional and longitudinal settings. The
alternative method_args$LR$separation_method = "firth" is available only
for the non-longitudinal model.
Maaslin3 is used through its standard package pathway in DAssemble() and
is not forced into a prevalence-only mode.
Installation
Install DAssemble from Bioconductor with BiocManager:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("DAssemble")
Bulk RNA-seq Example
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",
coVars = "cell",
p_adj = "BH",
enhancer_norm = "tmm",
method_args = list(
DESeq2 = list(
DESeq = list(fitType = "local")
),
LR = list(
control = glm.control(maxit = 100)
)
),
return_components = TRUE,
return_subensembles = TRUE
)
#> using pre-existing size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
#> calcNormFactors has been renamed to normLibSizes
head(res$res)
#> feature metadata pval_core pval_WLX coef_LR pval_LR pval_joint
#> 1 ENSG00000115414 dex 9.388328e-01 1.00000000 NA NA 1
#> 2 ENSG00000011465 dex 1.523315e-03 0.48571429 NA NA 1
#> 3 ENSG00000198804 dex 8.842089e-01 0.88571429 NA NA 1
#> 4 ENSG00000156508 dex 4.568638e-04 0.11428571 NA NA 1
#> 5 ENSG00000164692 dex 1.759305e-08 0.02857143 NA NA 1
#> 6 ENSG00000116260 dex 2.836045e-01 0.68571429 NA NA 1
#> qval
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 5 1
#> 6 1
names(res$components)
#> [1] "DESeq2" "WLX" "LR"
This analysis adjusts for cell, combines DESeq2 p-values with
Wilcoxon and logistic-regression enhancer p-values, and forwards example
control arguments through method_args. The combined results are returned in
res$res, and the per-method outputs are available in res$components.
Longitudinal HMP2 Example
The Human Microbiome Project 2 adult example included with the package shows a
longitudinal microbiome analysis with multiple covariates. The feature table and
metadata are stored in inst/extdata and can be accessed after installation
with system.file(). To keep the vignette build lightweight, the evaluated
example below uses the most abundant taxa and runs the Dysbiosis_UC scenario.
hmp2_file <- system.file(
"extdata",
"hmp2_adult.RData",
package = "DAssemble",
mustWork = TRUE
)
load(hmp2_file)
stopifnot(
exists("adult_taxa"),
exists("adult_meta"),
identical(rownames(adult_taxa), rownames(adult_meta))
)
binary_factor <- function(x) {
factor(as.integer(x), levels = c(0, 1))
}
adult_meta$diagnosis_CD <- binary_factor(adult_meta$diagnosis == "CD")
adult_meta$diagnosis_UC <- binary_factor(adult_meta$diagnosis == "UC")
adult_meta$dysbiosis_CD <- binary_factor(
adult_meta$dysbiosis_state == "dysbiosis_CD"
)
adult_meta$dysbiosis_UC <- binary_factor(
adult_meta$dysbiosis_state == "dysbiosis_UC"
)
adult_meta$dysbiosis_nonIBD <- binary_factor(
adult_meta$dysbiosis_state == "dysbiosis_nonIBD"
)
adult_meta$abx_used <- binary_factor(adult_meta$Antibiotics == "Yes")
adult_meta$participant_id <- factor(adult_meta$`Participant ID`)
disease <- "UC"
other_disease <- "CD"
hmp2_taxa <- adult_taxa[
,
order(colSums(adult_taxa), decreasing = TRUE)[seq_len(20L)],
drop = FALSE
]
res <- DAssemble::DAssemble(
features = hmp2_taxa,
metadata = adult_meta,
expVar = paste0("dysbiosis_", disease),
coVars = c(
"diagnosis_CD",
"diagnosis_UC",
paste0("dysbiosis_", other_disease),
"dysbiosis_nonIBD",
"abx_used",
"reads_filtered"
),
random_effects = "participant_id",
core_method = "Maaslin2",
enhancers = "LR",
method_args = list(
Maaslin2 = list(median_comparison = TRUE)
),
enhancer_norm = "TSS",
p_adj = "BH",
return_components = TRUE
)
#> [1] "Creating output feature tables folder"
#> [1] "Creating output fits folder"
#> 2026-09-11 17:16:55.166811 INFO::Writing function arguments to log file
#> 2026-09-11 17:16:55.190603 INFO::Verifying options selected are valid
#> 2026-09-11 17:16:55.233288 INFO::Determining format of input files
#> 2026-09-11 17:16:55.234532 INFO::Input format is data samples as rows and metadata samples as rows
#> 2026-09-11 17:16:55.246139 INFO::Formula for random effects: expr ~ (1 | participant_id)
#> 2026-09-11 17:16:55.247225 INFO::Formula for fixed effects: expr ~ dysbiosis_UC + diagnosis_CD + diagnosis_UC + dysbiosis_CD + dysbiosis_nonIBD + abx_used + reads_filtered
#> 2026-09-11 17:16:55.248096 INFO::Factor detected for categorial metadata 'dysbiosis_UC'. Provide a reference argument or manually set factor ordering to change reference level.
#> 2026-09-11 17:16:55.248873 INFO::Factor detected for categorial metadata 'diagnosis_CD'. Provide a reference argument or manually set factor ordering to change reference level.
#> 2026-09-11 17:16:55.249631 INFO::Factor detected for categorial metadata 'diagnosis_UC'. Provide a reference argument or manually set factor ordering to change reference level.
#> 2026-09-11 17:16:55.250409 INFO::Factor detected for categorial metadata 'dysbiosis_CD'. Provide a reference argument or manually set factor ordering to change reference level.
#> 2026-09-11 17:16:55.251167 INFO::Factor detected for categorial metadata 'dysbiosis_nonIBD'. Provide a reference argument or manually set factor ordering to change reference level.
#> 2026-09-11 17:16:55.251912 INFO::Factor detected for categorial metadata 'abx_used'. Provide a reference argument or manually set factor ordering to change reference level.
#> 2026-09-11 17:16:55.252628 INFO::Filter data based on min abundance and min prevalence
#> 2026-09-11 17:16:55.253358 INFO::Total samples in data: 1078
#> 2026-09-11 17:16:55.254077 INFO::Min samples required with min abundance for a feature not to be filtered: 107.800000
#> 2026-09-11 17:16:55.256078 INFO::Total filtered features: 0
#> 2026-09-11 17:16:55.256957 INFO::Filtered feature names from abundance and prevalence filtering:
#> 2026-09-11 17:16:55.259163 INFO::Total filtered features with variance filtering: 0
#> 2026-09-11 17:16:55.259969 INFO::Filtered feature names from variance filtering:
#> 2026-09-11 17:16:55.260681 INFO::Running selected normalization method: TSS
#> 2026-09-11 17:16:55.3455 INFO::Applying z-score to standardize continuous metadata
#> 2026-09-11 17:16:55.361342 INFO::Running selected transform method: LOG
#> 2026-09-11 17:16:55.466223 INFO::Running selected analysis method: LM
#> 2026-09-11 17:16:55.708297 INFO::Fitting model to feature number 1, UNCLASSIFIED
#> 2026-09-11 17:16:55.859632 INFO::Fitting model to feature number 2, k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Phocaeicola.s__Phocaeicola_vulgatus.t__SGB1814
#> 2026-09-11 17:16:55.945127 INFO::Fitting model to feature number 3, k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides.s__Bacteroides_uniformis.t__SGB1836
#> 2026-09-11 17:16:56.031382 INFO::Fitting model to feature number 4, k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Phocaeicola.s__Phocaeicola_dorei.t__SGB1815
#> 2026-09-11 17:16:56.166279 INFO::Fitting model to feature number 5, k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides.s__Bacteroides_stercoris.t__SGB1830
#> 2026-09-11 17:16:56.249758 INFO::Fitting model to feature number 6, k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella.s__Prevotella_copri_clade_A.t__SGB1626
#> 2026-09-11 17:16:56.330991 INFO::Fitting model to feature number 7, k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides.s__Bacteroides_ovatus.t__SGB1871
#> 2026-09-11 17:16:56.412035 INFO::Fitting model to feature number 8, k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__Faecalibacterium.s__Faecalibacterium_prausnitzii.t__SGB15342
#> 2026-09-11 17:16:56.493517 INFO::Fitting model to feature number 9, k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Lachnospiraceae_unclassified.s__Eubacterium_rectale.t__SGB4933
#> 2026-09-11 17:16:56.57662 INFO::Fitting model to feature number 10, k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__Alistipes.s__Alistipes_putredinis.t__SGB2318
#> 2026-09-11 17:16:56.739041 INFO::Fitting model to feature number 11, k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Tannerellaceae.g__Parabacteroides.s__Parabacteroides_distasonis.t__SGB1934
#> 2026-09-11 17:16:56.821417 INFO::Fitting model to feature number 12, k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Roseburia.s__Roseburia_intestinalis.t__SGB4951
#> 2026-09-11 17:16:56.901933 INFO::Fitting model to feature number 13, k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__Faecalibacterium.s__Faecalibacterium_prausnitzii.t__SGB15316
#> 2026-09-11 17:16:56.982848 INFO::Fitting model to feature number 14, k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides.s__Bacteroides_fragilis.t__SGB1855
#> 2026-09-11 17:16:57.063402 INFO::Fitting model to feature number 15, k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Roseburia.s__Roseburia_faecis.t__SGB4925
#> 2026-09-11 17:16:57.143605 INFO::Fitting model to feature number 16, k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides.s__Bacteroides_caccae.t__SGB1877
#> 2026-09-11 17:16:57.93805 INFO::Fitting model to feature number 17, k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Phocaeicola.s__Phocaeicola_massiliensis.t__SGB1812
#> 2026-09-11 17:16:58.015346 INFO::Fitting model to feature number 18, k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__Faecalibacterium.s__Faecalibacterium_prausnitzii.t__SGB15332
#> 2026-09-11 17:16:58.095177 INFO::Fitting model to feature number 19, k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides.s__Bacteroides_thetaiotaomicron.t__SGB1861
#> 2026-09-11 17:16:58.176102 INFO::Fitting model to feature number 20, k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__Faecalibacterium.s__Faecalibacterium_prausnitzii.t__SGB15318
#> 2026-09-11 17:16:58.265853 INFO::Counting total values for each feature
#> 2026-09-11 17:16:58.273463 INFO::Writing filtered data to file /tmp/RtmpdXfiOn/m2_22150867/features/filtered_data.tsv
#> 2026-09-11 17:16:58.291984 INFO::Writing filtered, normalized data to file /tmp/RtmpdXfiOn/m2_22150867/features/filtered_data_norm.tsv
#> 2026-09-11 17:16:58.309506 INFO::Writing filtered, normalized, transformed data to file /tmp/RtmpdXfiOn/m2_22150867/features/filtered_data_norm_transformed.tsv
#> 2026-09-11 17:16:58.334965 INFO::Writing residuals to file /tmp/RtmpdXfiOn/m2_22150867/fits/residuals.rds
#> 2026-09-11 17:16:58.34768 INFO::Writing fitted values to file /tmp/RtmpdXfiOn/m2_22150867/fits/fitted.rds
#> 2026-09-11 17:16:58.359239 INFO::Writing extracted random effects to file /tmp/RtmpdXfiOn/m2_22150867/fits/ranef.rds
#> 2026-09-11 17:16:58.360817 INFO::Writing all results to file (ordered by increasing q-values): /tmp/RtmpdXfiOn/m2_22150867/all_results.tsv
#> 2026-09-11 17:16:58.363275 INFO::Writing the significant results (those which are less than or equal to the threshold of 1.000000 ) to file (ordered by increasing q-values): /tmp/RtmpdXfiOn/m2_22150867/significant_results.tsv
head(res$res)
#> feature
#> 16 UNCLASSIFIED
#> 15 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_massiliensis|t__SGB1812
#> 3 k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Lachnospiraceae|g__Lachnospiraceae_unclassified|s__Eubacterium_rectale|t__SGB4933
#> 1 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_vulgatus|t__SGB1814
#> 2 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_uniformis|t__SGB1836
#> 6 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_ovatus|t__SGB1871
#> metadata coef_core pval_core coef_LR pval_LR pval_joint
#> 16 dysbiosis_UC 1.9440197 3.972770e-10 -0.03729951 9.906647e-01 7.945542e-10
#> 15 dysbiosis_UC 2.6487670 7.491369e-07 -0.17424170 8.790036e-01 1.498283e-06
#> 3 dysbiosis_UC -2.0488947 1.497239e-02 -3.28080747 5.571692e-05 1.110210e-04
#> 1 dysbiosis_UC -1.8508152 7.242177e-03 -2.47057587 1.099657e-03 1.909417e-03
#> 2 dysbiosis_UC -1.3281813 5.360154e-02 -4.05438628 8.180096e-04 1.611646e-03
#> 6 dysbiosis_UC -0.3504128 5.561235e-01 -5.59457718 8.667992e-04 1.734427e-03
#> qval
#> 16 1.589108e-08
#> 15 1.498283e-05
#> 3 7.401400e-04
#> 1 5.750931e-03
#> 2 5.750931e-03
#> 6 5.750931e-03
head(res$components$Maaslin2)
#> feature
#> 1 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_vulgatus|t__SGB1814
#> 2 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_uniformis|t__SGB1836
#> 3 k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Lachnospiraceae|g__Lachnospiraceae_unclassified|s__Eubacterium_rectale|t__SGB4933
#> 4 k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium|s__Faecalibacterium_prausnitzii|t__SGB15318
#> 5 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_caccae|t__SGB1877
#> 6 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_ovatus|t__SGB1871
#> metadata coef_core pval_core
#> 1 dysbiosis_UC -1.8508152 0.007242177
#> 2 dysbiosis_UC -1.3281813 0.053601544
#> 3 dysbiosis_UC -2.0488947 0.014972390
#> 4 dysbiosis_UC -0.7591049 0.261306203
#> 5 dysbiosis_UC -0.2689939 0.636064366
#> 6 dysbiosis_UC -0.3504128 0.556123515
head(res$components$LR)
#> feature
#> UNCLASSIFIED UNCLASSIFIED
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_vulgatus|t__SGB1814 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_vulgatus|t__SGB1814
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_uniformis|t__SGB1836 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_uniformis|t__SGB1836
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_dorei|t__SGB1815 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_dorei|t__SGB1815
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_stercoris|t__SGB1830 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_stercoris|t__SGB1830
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella|s__Prevotella_copri_clade_A|t__SGB1626 k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella|s__Prevotella_copri_clade_A|t__SGB1626
#> metadata
#> UNCLASSIFIED dysbiosis_UC
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_vulgatus|t__SGB1814 dysbiosis_UC
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_uniformis|t__SGB1836 dysbiosis_UC
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_dorei|t__SGB1815 dysbiosis_UC
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_stercoris|t__SGB1830 dysbiosis_UC
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella|s__Prevotella_copri_clade_A|t__SGB1626 dysbiosis_UC
#> coef_LR
#> UNCLASSIFIED -0.03729951
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_vulgatus|t__SGB1814 -2.47057587
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_uniformis|t__SGB1836 -4.05438628
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_dorei|t__SGB1815 -1.29992480
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_stercoris|t__SGB1830 -3.80748387
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella|s__Prevotella_copri_clade_A|t__SGB1626 -1.14473113
#> pval_LR
#> UNCLASSIFIED 0.9906647216
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_vulgatus|t__SGB1814 0.0010996566
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_uniformis|t__SGB1836 0.0008180096
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Phocaeicola|s__Phocaeicola_dorei|t__SGB1815 0.0693521116
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides_stercoris|t__SGB1830 0.0297879923
#> k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella|s__Prevotella_copri_clade_A|t__SGB1626 0.3859268822
The same example is also available as an executable script:
source(system.file(
"extdata",
"hmp2_adult_example.R",
package = "DAssemble",
mustWork = TRUE
))
Microbiome Example
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
#> 28 256977 group 0.05714286 5.615105 0.009103585 0.05714286 0.02074046
#> 37 309788 group 0.05714286 5.615105 0.009103585 0.05714286 0.02074046
#> 39 240687 group 0.05714286 5.615105 0.009103585 0.05714286 0.02074046
#> 40 3046 group 0.05714286 5.615105 0.009103585 0.05714286 0.02074046
#> 44 573135 group 0.05714286 5.615105 0.009103585 0.05714286 0.02074046
#> 50 220494 group 0.05714286 5.615105 0.009103585 0.05714286 0.02074046
#> qval
#> 28 0.4518957
#> 37 0.4518957
#> 39 0.4518957
#> 40 0.4518957
#> 44 0.4518957
#> 50 0.4518957
names(res$components)
#> [1] "WLX" "LR" "KS"
head(res$ensembles)
#> $WLX
#> feature Pval adjPval
#> 1 256977 0.05714286 0.4230704
#> 2 309788 0.05714286 0.4230704
#> 3 240687 0.05714286 0.4230704
#> 4 3046 0.05714286 0.4230704
#> 5 573135 0.05714286 0.4230704
#> 6 220494 0.05714286 0.4230704
#> 7 306180 0.05714286 0.4230704
#> 8 27669 0.05714286 0.4230704
#> 9 565399 0.05714286 0.4230704
#> 10 111806 0.05714286 0.4230704
#> 11 65683 0.05714286 0.4230704
#> 12 71074 0.05714286 0.4230704
#> 13 509622 0.05714286 0.4230704
#> 14 360298 0.05714286 0.4230704
#> 15 223329 0.05714286 0.4230704
#> 16 313985 0.05714286 0.4230704
#> 17 574458 0.05714286 0.4230704
#> 18 247010 0.05714286 0.4230704
#> 19 154102 0.05714286 0.4230704
#> 20 129759 0.05714286 0.4230704
#> 21 113626 0.05714286 0.4230704
#> 22 273055 0.05714286 0.4230704
#> 23 551424 0.05714286 0.4230704
#> 24 262714 0.05714286 0.4230704
#> 25 340507 0.05714286 0.4230704
#> 26 84768 0.05714286 0.4230704
#> 27 158370 0.05714286 0.4230704
#> 28 566886 0.05714286 0.4230704
#> 29 216191 0.05714286 0.4230704
#> 30 352632 0.05714286 0.4230704
#> 31 245203 0.05714286 0.4230704
#> 32 265043 0.05714286 0.4230704
#> 33 242675 0.05714286 0.4230704
#> 34 559200 0.05714286 0.4230704
#> 35 367659 0.05714286 0.4230704
#> 36 161227 0.05714286 0.4230704
#> 37 277931 0.05714286 0.4230704
#> 38 527683 0.05714286 0.4230704
#> 39 113866 0.05714286 0.4230704
#> 40 356083 0.05714286 0.4230704
#> 41 200741 0.05714286 0.4230704
#> 42 308892 0.05714286 0.4230704
#> 43 1605 0.05714286 0.4230704
#> 44 250291 0.05714286 0.4230704
#> 45 218467 0.05714286 0.4230704
#> 46 286104 0.05714286 0.4230704
#> 47 511844 0.05714286 0.4230704
#> 48 543366 0.05714286 0.4230704
#> 49 535437 0.05714286 0.4230704
#> 50 159711 0.05714286 0.4230704
#> 51 144381 0.05714286 0.4230704
#> 52 584276 0.05714286 0.4230704
#> 53 570888 0.05714286 0.4230704
#> 54 278390 0.05714286 0.4230704
#> 55 170339 0.05714286 0.4230704
#> 56 93865 0.05714286 0.4230704
#> 57 251499 0.05714286 0.4230704
#> 58 70100 0.05714286 0.4230704
#> 59 97561 0.05714286 0.4230704
#> 60 278272 0.05714286 0.4230704
#> 61 130368 0.05714286 0.4230704
#> 62 76270 0.05714286 0.4230704
#> 63 190723 0.05714286 0.4230704
#> 64 208694 0.05714286 0.4230704
#> 65 222729 0.05714286 0.4230704
#> 66 203846 0.05714286 0.4230704
#> 67 156101 0.05714286 0.4230704
#> 68 13034 0.05714286 0.4230704
#> 69 109907 0.05714286 0.4230704
#> 70 326414 0.05714286 0.4230704
#> 71 36155 0.05714286 0.4230704
#> 72 47916 0.05714286 0.4230704
#> 73 279297 0.05714286 0.4230704
#> 74 150925 0.05714286 0.4230704
#> 75 578268 0.05714286 0.4230704
#> 76 208283 0.05714286 0.4230704
#> 77 546313 0.05714286 0.4230704
#> 78 154822 0.05714286 0.4230704
#> 79 565556 0.05714286 0.4230704
#> 80 100954 0.05714286 0.4230704
#> 81 166835 0.05714286 0.4230704
#> 82 150508 0.05714286 0.4230704
#> 83 113959 0.05714286 0.4230704
#> 84 544749 0.05714286 0.4230704
#> 85 306921 0.05714286 0.4230704
#> 86 114339 0.05714286 0.4230704
#> 87 203722 0.05714286 0.4230704
#> 88 184899 0.05714286 0.4230704
#> 89 235270 0.05714286 0.4230704
#> 90 151018 0.05714286 0.4230704
#> 91 252410 0.05714286 0.4230704
#> 92 331647 0.05714286 0.4230704
#> 93 244491 0.05714286 0.4230704
#> 94 211351 0.05714286 0.4230704
#> 95 154230 0.05714286 0.4230704
#> 96 549552 0.05714286 0.4230704
#> 97 265094 0.05714286 0.4230704
#> 98 215700 0.05714286 0.4230704
#> 99 582012 0.05714286 0.4230704
#> 100 204592 0.05714286 0.4230704
#> 101 252778 0.05714286 0.4230704
#> 102 1791 0.05714286 0.4230704
#> 103 206632 0.05714286 0.4230704
#> 104 575937 0.05714286 0.4230704
#> 105 349634 0.05714286 0.4230704
#> 106 553113 0.05714286 0.4230704
#> 107 210176 0.05714286 0.4230704
#> 108 308535 0.05714286 0.4230704
#> 109 113921 0.05714286 0.4230704
#> 110 534348 0.05714286 0.4230704
#> 111 189047 0.05714286 0.4230704
#> 112 192573 0.05714286 0.4230704
#> 113 180658 0.05714286 0.4230704
#> 114 325407 0.05714286 0.4230704
#> 115 361496 0.05714286 0.4230704
#> 116 316732 0.05714286 0.4230704
#> 117 291090 0.05714286 0.4230704
#> 118 196731 0.05714286 0.4230704
#> 119 290260 0.05714286 0.4230704
#> 120 361480 0.05714286 0.4230704
#> 121 193761 0.05714286 0.4230704
#> 122 357795 0.05714286 0.4230704
#> 123 223059 0.05714286 0.4230704
#> 124 287978 0.05714286 0.4230704
#> 125 357470 0.05714286 0.4230704
#> 126 367433 0.05714286 0.4230704
#> 127 187524 0.05714286 0.4230704
#> 128 518438 0.05714286 0.4230704
#> 129 327476 0.05714286 0.4230704
#> 130 199487 0.05714286 0.4230704
#> 131 191541 0.05714286 0.4230704
#> 132 365676 0.05714286 0.4230704
#> 133 269833 0.05714286 0.4230704
#> 134 194053 0.05714286 0.4230704
#> 135 190464 0.05714286 0.4230704
#> 136 288134 0.05714286 0.4230704
#> 137 194648 0.05714286 0.4230704
#> 138 138006 0.05714286 0.4230704
#> 139 212619 0.05714286 0.4230704
#> 140 293896 0.05714286 0.4230704
#> 141 195173 0.05714286 0.4230704
#> 142 192127 0.05714286 0.4230704
#> 143 193066 0.05714286 0.4230704
#> 144 177339 0.05714286 0.4230704
#> 145 193343 0.05714286 0.4230704
#> 146 203708 0.05714286 0.4230704
#> 147 261912 0.05714286 0.4230704
#> 148 175537 0.05714286 0.4230704
#> 149 557785 0.11428571 0.7995643
#> 150 179460 0.05714286 0.4230704
#> 151 181843 0.05714286 0.4230704
#> 152 310301 0.05714286 0.4230704
#> 153 162162 0.05714286 0.4230704
#> 154 190108 0.05714286 0.4230704
#> 155 82283 0.05714286 0.4230704
#> 156 262724 0.05714286 0.4230704
#> 157 111986 0.05714286 0.4230704
#> 158 250258 0.11428571 0.7995643
#> 159 188646 0.05714286 0.4230704
#> 160 469991 0.05714286 0.4230704
#> 161 346780 0.05714286 0.4230704
#> 162 174348 0.05714286 0.4230704
#> 163 185281 0.05714286 0.4230704
#> 164 167602 0.05714286 0.4230704
#> 165 145786 0.62857143 1.0000000
#> 166 182980 0.05714286 0.4230704
#> 167 204044 0.05714286 0.4230704
#> 168 51024 0.11428571 0.7995643
#> 169 255584 0.22857143 1.0000000
#> 170 216862 0.05714286 0.4230704
#> 171 154268 0.11428571 0.7995643
#> 172 547632 0.11428571 0.7995643
#> 173 294022 0.05714286 0.4230704
#> 174 259569 0.05714286 0.4230704
#> 175 348374 0.05714286 0.4230704
#> 176 470973 0.05714286 0.4230704
#> 177 589024 0.05714286 0.4230704
#> 178 268540 0.05714286 0.4230704
#> 179 302160 0.05714286 0.4230704
#> 180 352304 0.05714286 0.4230704
#> 181 2000 0.05714286 0.4230704
#> 182 109014 0.05714286 0.4230704
#> 183 366612 0.05714286 0.4230704
#> 184 170206 0.05714286 0.4230704
#> 185 104780 0.05714286 0.4230704
#> 186 511008 0.05714286 0.4230704
#> 187 278234 0.05714286 0.4230704
#> 188 213747 0.05714286 0.4230704
#> 189 190872 0.05714286 0.4230704
#> 190 178915 0.05714286 0.4230704
#> 191 191306 0.05714286 0.4230704
#> 192 510994 0.05714286 0.4230704
#> 193 21698 0.05714286 0.4230704
#> 194 193632 0.05714286 0.4230704
#> 195 469873 0.05714286 0.4230704
#> 196 328422 0.05714286 0.4230704
#> 197 405524 0.05714286 0.4230704
#> 198 367055 0.05714286 0.4230704
#> 199 470114 0.05714286 0.4230704
#> 200 544358 0.05714286 0.4230704
#> 201 204889 0.05714286 0.4230704
#> 202 470812 0.05714286 0.4230704
#> 203 363692 0.05714286 0.4230704
#> 204 171551 0.05714286 0.4230704
#> 205 322235 0.05714286 0.4230704
#> 206 331820 0.05714286 0.4230704
#> 207 158660 0.05714286 0.4230704
#> 208 244304 0.62857143 1.0000000
#> 209 263681 0.85714286 1.0000000
#> 210 326977 0.05714286 0.4230704
#> 211 278873 0.85714286 1.0000000
#> 212 357591 0.40000000 1.0000000
#> 213 127309 1.00000000 1.0000000
#> 214 517293 0.85714286 1.0000000
#> 215 248140 0.05714286 0.4230704
#> 216 211720 1.00000000 1.0000000
#> 217 470172 0.05714286 0.4230704
#> 218 549871 0.62857143 1.0000000
#> 219 347862 0.11428571 0.7995643
#> 220 249661 0.40000000 1.0000000
#> 221 183200 0.05714286 0.4230704
#> 222 42680 0.40000000 1.0000000
#> 223 326792 0.11428571 0.7995643
#> 224 171772 0.22857143 1.0000000
#> 225 541301 0.85714286 1.0000000
#> 226 469482 0.11428571 0.7995643
#> 227 181095 0.22857143 1.0000000
#> 228 299892 0.40000000 1.0000000
#> 229 175497 0.22857143 1.0000000
#> 230 366882 1.00000000 1.0000000
#> 231 109546 0.22857143 1.0000000
#> 232 320147 0.22857143 1.0000000
#> 233 243150 0.22857143 1.0000000
#> 234 191687 0.22857143 1.0000000
#> 235 302974 0.22857143 1.0000000
#> 236 181892 0.11428571 0.7995643
#> 237 181028 0.11428571 0.7995643
#> 238 550814 0.11428571 0.7995643
#> 239 360268 0.22857143 1.0000000
#> 240 263461 0.22857143 1.0000000
#> 241 275891 0.11428571 0.7995643
#> 242 368928 0.22857143 1.0000000
#> 243 230437 0.40000000 1.0000000
#> 244 212128 0.05714286 0.4230704
#> 245 469946 0.85714286 1.0000000
#> 246 103685 1.00000000 1.0000000
#> 247 565565 0.40000000 1.0000000
#> 248 197817 0.62857143 1.0000000
#> 249 173810 1.00000000 1.0000000
#> 250 215890 0.05714286 0.4230704
#>
#> $LR
#> feature Pval adjPval
#> 1 256977 0.009103585 0.1946615
#> 2 309788 0.009103585 0.1946615
#> 3 240687 0.009103585 0.1946615
#> 4 3046 0.009103585 0.1946615
#> 5 573135 0.009103585 0.1946615
#> 6 220494 0.009103585 0.1946615
#> 7 306180 0.009103585 0.1946615
#> 8 27669 0.009103585 0.1946615
#> 9 565399 0.009103585 0.1946615
#> 10 111806 0.009103585 0.1946615
#> 11 65683 0.009103585 0.1946615
#> 12 71074 0.009103585 0.1946615
#> 13 509622 0.009103585 0.1946615
#> 14 360298 0.009103585 0.1946615
#> 15 223329 0.009103585 0.1946615
#> 16 313985 0.009103585 0.1946615
#> 17 574458 0.009103585 0.1946615
#> 18 247010 0.009103585 0.1946615
#> 19 154102 0.009103585 0.1946615
#> 20 129759 0.009103585 0.1946615
#> 21 113626 0.009103585 0.1946615
#> 22 273055 0.009103585 0.1946615
#> 23 551424 0.009103585 0.1946615
#> 24 262714 0.009103585 0.1946615
#> 25 340507 0.009103585 0.1946615
#> 26 84768 0.009103585 0.1946615
#> 27 158370 0.009103585 0.1946615
#> 28 566886 0.009103585 0.1946615
#> 29 216191 0.009103585 0.1946615
#> 30 352632 0.009103585 0.1946615
#> 31 245203 0.009103585 0.1946615
#> 32 265043 0.009103585 0.1946615
#> 33 242675 0.009103585 0.1946615
#> 34 559200 0.009103585 0.1946615
#> 35 367659 0.009103585 0.1946615
#> 36 161227 0.009103585 0.1946615
#> 37 277931 0.009103585 0.1946615
#> 38 527683 0.009103585 0.1946615
#> 39 113866 0.009103585 0.1946615
#> 40 356083 0.009103585 0.1946615
#> 41 200741 0.009103585 0.1946615
#> 42 308892 0.009103585 0.1946615
#> 43 1605 0.009103585 0.1946615
#> 44 250291 0.009103585 0.1946615
#> 45 218467 0.009103585 0.1946615
#> 46 286104 0.009103585 0.1946615
#> 47 511844 0.009103585 0.1946615
#> 48 543366 0.009103585 0.1946615
#> 49 535437 0.009103585 0.1946615
#> 50 159711 0.009103585 0.1946615
#> 51 144381 0.009103585 0.1946615
#> 52 584276 0.009103585 0.1946615
#> 53 570888 0.009103585 0.1946615
#> 54 278390 0.009103585 0.1946615
#> 55 170339 0.009103585 0.1946615
#> 56 93865 0.009103585 0.1946615
#> 57 251499 0.009103585 0.1946615
#> 58 70100 0.009103585 0.1946615
#> 59 97561 0.009103585 0.1946615
#> 60 278272 0.009103585 0.1946615
#> 61 130368 0.009103585 0.1946615
#> 62 76270 0.009103585 0.1946615
#> 63 190723 0.009103585 0.1946615
#> 64 208694 0.009103585 0.1946615
#> 65 222729 0.009103585 0.1946615
#> 66 203846 0.009103585 0.1946615
#> 67 156101 0.009103585 0.1946615
#> 68 13034 0.009103585 0.1946615
#> 69 109907 0.009103585 0.1946615
#> 70 326414 0.009103585 0.1946615
#> 71 36155 0.022058541 0.2821997
#> 72 47916 0.022058541 0.2821997
#> 73 279297 0.022058541 0.2821997
#> 74 150925 0.022058541 0.2821997
#> 75 578268 0.022058541 0.2821997
#> 76 208283 0.022058541 0.2821997
#> 77 546313 0.022058541 0.2821997
#> 78 154822 0.022058541 0.2821997
#> 79 565556 0.022058541 0.2821997
#> 80 100954 0.022058541 0.2821997
#> 81 166835 0.022058541 0.2821997
#> 82 150508 0.022058541 0.2821997
#> 83 113959 0.022058541 0.2821997
#> 84 544749 0.022058541 0.2821997
#> 85 306921 0.022058541 0.2821997
#> 86 114339 0.022058541 0.2821997
#> 87 203722 0.022058541 0.2821997
#> 88 184899 0.022058541 0.2821997
#> 89 235270 0.022058541 0.2821997
#> 90 151018 0.022058541 0.2821997
#> 91 252410 0.022058541 0.2821997
#> 92 331647 0.022058541 0.2821997
#> 93 244491 0.022058541 0.2821997
#> 94 211351 0.022058541 0.2821997
#> 95 154230 0.022058541 0.2821997
#> 96 549552 0.022058541 0.2821997
#> 97 265094 0.022058541 0.2821997
#> 98 215700 0.022058541 0.2821997
#> 99 582012 0.022058541 0.2821997
#> 100 204592 0.022058541 0.2821997
#> 101 252778 0.022058541 0.2821997
#> 102 1791 0.022058541 0.2821997
#> 103 206632 0.022058541 0.2821997
#> 104 575937 0.022058541 0.2821997
#> 105 349634 0.022058541 0.2821997
#> 106 553113 0.022058541 0.2821997
#> 107 210176 0.022058541 0.2821997
#> 108 308535 0.022058541 0.2821997
#> 109 113921 0.022058541 0.2821997
#> 110 534348 0.022058541 0.2821997
#> 111 189047 0.187112927 1.0000000
#> 112 192573 0.187112927 1.0000000
#> 113 180658 0.187112927 1.0000000
#> 114 325407 0.187112927 1.0000000
#> 115 361496 0.187112927 1.0000000
#> 116 316732 0.187112927 1.0000000
#> 117 291090 0.187112927 1.0000000
#> 118 196731 0.187112927 1.0000000
#> 119 290260 0.187112927 1.0000000
#> 120 361480 0.187112927 1.0000000
#> 121 193761 0.187112927 1.0000000
#> 122 357795 0.187112927 1.0000000
#> 123 223059 0.187112927 1.0000000
#> 124 287978 0.187112927 1.0000000
#> 125 357470 0.187112927 1.0000000
#> 126 367433 0.187112927 1.0000000
#> 127 187524 0.187112927 1.0000000
#> 128 518438 0.187112927 1.0000000
#> 129 327476 0.187112927 1.0000000
#> 130 199487 0.187112927 1.0000000
#> 131 191541 0.187112927 1.0000000
#> 132 365676 0.187112927 1.0000000
#> 133 269833 0.187112927 1.0000000
#> 134 194053 0.187112927 1.0000000
#> 135 190464 0.187112927 1.0000000
#> 136 288134 0.187112927 1.0000000
#> 137 194648 0.187112927 1.0000000
#> 138 138006 0.187112927 1.0000000
#> 139 212619 0.187112927 1.0000000
#> 140 293896 0.187112927 1.0000000
#> 141 195173 0.187112927 1.0000000
#> 142 192127 0.187112927 1.0000000
#> 143 193066 0.187112927 1.0000000
#> 144 177339 0.187112927 1.0000000
#> 145 193343 0.187112927 1.0000000
#> 146 203708 0.187112927 1.0000000
#> 147 261912 0.187112927 1.0000000
#> 148 175537 0.187112927 1.0000000
#> 149 557785 0.022058541 0.2821997
#> 150 179460 0.187112927 1.0000000
#> 151 181843 0.187112927 1.0000000
#> 152 310301 0.187112927 1.0000000
#> 153 162162 0.187112927 1.0000000
#> 154 190108 0.187112927 1.0000000
#> 155 82283 0.187112927 1.0000000
#> 156 262724 0.187112927 1.0000000
#> 157 111986 0.093494567 1.0000000
#> 158 250258 0.022058541 0.2821997
#> 159 188646 0.187112927 1.0000000
#> 160 469991 0.187112927 1.0000000
#> 161 346780 0.187112927 1.0000000
#> 162 174348 0.187112927 1.0000000
#> 163 185281 0.187112927 1.0000000
#> 164 167602 0.187112927 1.0000000
#> 165 145786 0.022058541 0.2821997
#> 166 182980 0.187112927 1.0000000
#> 167 204044 0.187112927 1.0000000
#> 168 51024 0.022058541 0.2821997
#> 169 255584 0.022058541 0.2821997
#> 170 216862 0.187112927 1.0000000
#> 171 154268 0.022058541 0.2821997
#> 172 547632 0.022058541 0.2821997
#> 173 294022 0.187112927 1.0000000
#> 174 259569 0.471878302 1.0000000
#> 175 348374 0.408906218 1.0000000
#> 176 470973 0.471878302 1.0000000
#> 177 589024 0.408906218 1.0000000
#> 178 268540 0.408906218 1.0000000
#> 179 302160 0.408906218 1.0000000
#> 180 352304 0.471878302 1.0000000
#> 181 2000 0.408906218 1.0000000
#> 182 109014 0.408906218 1.0000000
#> 183 366612 0.408906218 1.0000000
#> 184 170206 0.408906218 1.0000000
#> 185 104780 0.408906218 1.0000000
#> 186 511008 0.471878302 1.0000000
#> 187 278234 0.408906218 1.0000000
#> 188 213747 0.408906218 1.0000000
#> 189 190872 0.408906218 1.0000000
#> 190 178915 0.408906218 1.0000000
#> 191 191306 0.408906218 1.0000000
#> 192 510994 0.471878302 1.0000000
#> 193 21698 0.408906218 1.0000000
#> 194 193632 0.408906218 1.0000000
#> 195 469873 0.696765470 1.0000000
#> 196 328422 0.696765470 1.0000000
#> 197 405524 0.696765470 1.0000000
#> 198 367055 0.696765470 1.0000000
#> 199 470114 0.696765470 1.0000000
#> 200 544358 0.696765470 1.0000000
#> 201 204889 0.696765470 1.0000000
#> 202 470812 0.696765470 1.0000000
#> 203 363692 0.696765470 1.0000000
#> 204 171551 0.915051949 1.0000000
#> 205 322235 0.915051949 1.0000000
#> 206 331820 NA NA
#> 207 158660 NA NA
#> 208 244304 0.165274665 1.0000000
#> 209 263681 0.241191857 1.0000000
#> 210 326977 NA NA
#> 211 278873 0.950713866 1.0000000
#> 212 357591 0.408906218 1.0000000
#> 213 127309 0.241191857 1.0000000
#> 214 517293 0.950713866 1.0000000
#> 215 248140 NA NA
#> 216 211720 0.950713866 1.0000000
#> 217 470172 0.950713866 1.0000000
#> 218 549871 0.696765470 1.0000000
#> 219 347862 0.408906218 1.0000000
#> 220 249661 0.950713866 1.0000000
#> 221 183200 0.950713866 1.0000000
#> 222 42680 0.696765470 1.0000000
#> 223 326792 0.408906218 1.0000000
#> 224 171772 0.696765470 1.0000000
#> 225 541301 0.696765470 1.0000000
#> 226 469482 0.696765470 1.0000000
#> 227 181095 0.696765470 1.0000000
#> 228 299892 0.696765470 1.0000000
#> 229 175497 0.696765470 1.0000000
#> 230 366882 0.950713866 1.0000000
#> 231 109546 0.696765470 1.0000000
#> 232 320147 0.950713866 1.0000000
#> 233 243150 0.950713866 1.0000000
#> 234 191687 0.408906218 1.0000000
#> 235 302974 0.696765470 1.0000000
#> 236 181892 0.408906218 1.0000000
#> 237 181028 0.950713866 1.0000000
#> 238 550814 0.950713866 1.0000000
#> 239 360268 0.950713866 1.0000000
#> 240 263461 0.408906218 1.0000000
#> 241 275891 0.950713866 1.0000000
#> 242 368928 0.950713866 1.0000000
#> 243 230437 0.950713866 1.0000000
#> 244 212128 0.950713866 1.0000000
#> 245 469946 0.950713866 1.0000000
#> 246 103685 0.950713866 1.0000000
#> 247 565565 0.950713866 1.0000000
#> 248 197817 0.950713866 1.0000000
#> 249 173810 0.950713866 1.0000000
#> 250 215890 0.950713866 1.0000000
#>
#> $KS
#> feature Pval adjPval
#> 1 256977 0.05714286 0.4230704
#> 2 309788 0.05714286 0.4230704
#> 3 240687 0.05714286 0.4230704
#> 4 3046 0.05714286 0.4230704
#> 5 573135 0.05714286 0.4230704
#> 6 220494 0.05714286 0.4230704
#> 7 306180 0.05714286 0.4230704
#> 8 27669 0.05714286 0.4230704
#> 9 565399 0.05714286 0.4230704
#> 10 111806 0.05714286 0.4230704
#> 11 65683 0.05714286 0.4230704
#> 12 71074 0.05714286 0.4230704
#> 13 509622 0.05714286 0.4230704
#> 14 360298 0.05714286 0.4230704
#> 15 223329 0.05714286 0.4230704
#> 16 313985 0.05714286 0.4230704
#> 17 574458 0.05714286 0.4230704
#> 18 247010 0.05714286 0.4230704
#> 19 154102 0.05714286 0.4230704
#> 20 129759 0.05714286 0.4230704
#> 21 113626 0.05714286 0.4230704
#> 22 273055 0.05714286 0.4230704
#> 23 551424 0.05714286 0.4230704
#> 24 262714 0.05714286 0.4230704
#> 25 340507 0.05714286 0.4230704
#> 26 84768 0.05714286 0.4230704
#> 27 158370 0.05714286 0.4230704
#> 28 566886 0.05714286 0.4230704
#> 29 216191 0.05714286 0.4230704
#> 30 352632 0.05714286 0.4230704
#> 31 245203 0.05714286 0.4230704
#> 32 265043 0.05714286 0.4230704
#> 33 242675 0.05714286 0.4230704
#> 34 559200 0.05714286 0.4230704
#> 35 367659 0.05714286 0.4230704
#> 36 161227 0.05714286 0.4230704
#> 37 277931 0.05714286 0.4230704
#> 38 527683 0.05714286 0.4230704
#> 39 113866 0.05714286 0.4230704
#> 40 356083 0.05714286 0.4230704
#> 41 200741 0.05714286 0.4230704
#> 42 308892 0.05714286 0.4230704
#> 43 1605 0.05714286 0.4230704
#> 44 250291 0.05714286 0.4230704
#> 45 218467 0.05714286 0.4230704
#> 46 286104 0.05714286 0.4230704
#> 47 511844 0.05714286 0.4230704
#> 48 543366 0.05714286 0.4230704
#> 49 535437 0.05714286 0.4230704
#> 50 159711 0.05714286 0.4230704
#> 51 144381 0.05714286 0.4230704
#> 52 584276 0.05714286 0.4230704
#> 53 570888 0.05714286 0.4230704
#> 54 278390 0.05714286 0.4230704
#> 55 170339 0.05714286 0.4230704
#> 56 93865 0.05714286 0.4230704
#> 57 251499 0.05714286 0.4230704
#> 58 70100 0.05714286 0.4230704
#> 59 97561 0.05714286 0.4230704
#> 60 278272 0.05714286 0.4230704
#> 61 130368 0.05714286 0.4230704
#> 62 76270 0.05714286 0.4230704
#> 63 190723 0.05714286 0.4230704
#> 64 208694 0.05714286 0.4230704
#> 65 222729 0.05714286 0.4230704
#> 66 203846 0.05714286 0.4230704
#> 67 156101 0.05714286 0.4230704
#> 68 13034 0.05714286 0.4230704
#> 69 109907 0.05714286 0.4230704
#> 70 326414 0.05714286 0.4230704
#> 71 36155 0.05714286 0.4230704
#> 72 47916 0.05714286 0.4230704
#> 73 279297 0.05714286 0.4230704
#> 74 150925 0.05714286 0.4230704
#> 75 578268 0.05714286 0.4230704
#> 76 208283 0.05714286 0.4230704
#> 77 546313 0.05714286 0.4230704
#> 78 154822 0.05714286 0.4230704
#> 79 565556 0.05714286 0.4230704
#> 80 100954 0.05714286 0.4230704
#> 81 166835 0.05714286 0.4230704
#> 82 150508 0.05714286 0.4230704
#> 83 113959 0.05714286 0.4230704
#> 84 544749 0.05714286 0.4230704
#> 85 306921 0.05714286 0.4230704
#> 86 114339 0.05714286 0.4230704
#> 87 203722 0.05714286 0.4230704
#> 88 184899 0.05714286 0.4230704
#> 89 235270 0.05714286 0.4230704
#> 90 151018 0.05714286 0.4230704
#> 91 252410 0.05714286 0.4230704
#> 92 331647 0.05714286 0.4230704
#> 93 244491 0.05714286 0.4230704
#> 94 211351 0.05714286 0.4230704
#> 95 154230 0.05714286 0.4230704
#> 96 549552 0.05714286 0.4230704
#> 97 265094 0.05714286 0.4230704
#> 98 215700 0.05714286 0.4230704
#> 99 582012 0.05714286 0.4230704
#> 100 204592 0.05714286 0.4230704
#> 101 252778 0.05714286 0.4230704
#> 102 1791 0.05714286 0.4230704
#> 103 206632 0.05714286 0.4230704
#> 104 575937 0.05714286 0.4230704
#> 105 349634 0.05714286 0.4230704
#> 106 553113 0.05714286 0.4230704
#> 107 210176 0.05714286 0.4230704
#> 108 308535 0.05714286 0.4230704
#> 109 113921 0.05714286 0.4230704
#> 110 534348 0.05714286 0.4230704
#> 111 189047 0.05714286 0.4230704
#> 112 192573 0.05714286 0.4230704
#> 113 180658 0.05714286 0.4230704
#> 114 325407 0.05714286 0.4230704
#> 115 361496 0.05714286 0.4230704
#> 116 316732 0.05714286 0.4230704
#> 117 291090 0.05714286 0.4230704
#> 118 196731 0.05714286 0.4230704
#> 119 290260 0.05714286 0.4230704
#> 120 361480 0.05714286 0.4230704
#> 121 193761 0.05714286 0.4230704
#> 122 357795 0.05714286 0.4230704
#> 123 223059 0.05714286 0.4230704
#> 124 287978 0.05714286 0.4230704
#> 125 357470 0.05714286 0.4230704
#> 126 367433 0.05714286 0.4230704
#> 127 187524 0.05714286 0.4230704
#> 128 518438 0.05714286 0.4230704
#> 129 327476 0.05714286 0.4230704
#> 130 199487 0.05714286 0.4230704
#> 131 191541 0.05714286 0.4230704
#> 132 365676 0.05714286 0.4230704
#> 133 269833 0.05714286 0.4230704
#> 134 194053 0.05714286 0.4230704
#> 135 190464 0.05714286 0.4230704
#> 136 288134 0.05714286 0.4230704
#> 137 194648 0.05714286 0.4230704
#> 138 138006 0.05714286 0.4230704
#> 139 212619 0.05714286 0.4230704
#> 140 293896 0.05714286 0.4230704
#> 141 195173 0.05714286 0.4230704
#> 142 192127 0.05714286 0.4230704
#> 143 193066 0.05714286 0.4230704
#> 144 177339 0.05714286 0.4230704
#> 145 193343 0.05714286 0.4230704
#> 146 203708 0.05714286 0.4230704
#> 147 261912 0.05714286 0.4230704
#> 148 175537 0.05714286 0.4230704
#> 149 557785 0.22857143 1.0000000
#> 150 179460 0.05714286 0.4230704
#> 151 181843 0.05714286 0.4230704
#> 152 310301 0.05714286 0.4230704
#> 153 162162 0.05714286 0.4230704
#> 154 190108 0.05714286 0.4230704
#> 155 82283 0.05714286 0.4230704
#> 156 262724 0.05714286 0.4230704
#> 157 111986 0.05714286 0.4230704
#> 158 250258 0.22857143 1.0000000
#> 159 188646 0.05714286 0.4230704
#> 160 469991 0.05714286 0.4230704
#> 161 346780 0.05714286 0.4230704
#> 162 174348 0.05714286 0.4230704
#> 163 185281 0.05714286 0.4230704
#> 164 167602 0.05714286 0.4230704
#> 165 145786 0.40000000 1.0000000
#> 166 182980 0.05714286 0.4230704
#> 167 204044 0.05714286 0.4230704
#> 168 51024 0.22857143 1.0000000
#> 169 255584 0.40000000 1.0000000
#> 170 216862 0.05714286 0.4230704
#> 171 154268 0.22857143 1.0000000
#> 172 547632 0.22857143 1.0000000
#> 173 294022 0.05714286 0.4230704
#> 174 259569 0.05714286 0.4230704
#> 175 348374 0.05714286 0.4230704
#> 176 470973 0.05714286 0.4230704
#> 177 589024 0.05714286 0.4230704
#> 178 268540 0.05714286 0.4230704
#> 179 302160 0.05714286 0.4230704
#> 180 352304 0.05714286 0.4230704
#> 181 2000 0.05714286 0.4230704
#> 182 109014 0.05714286 0.4230704
#> 183 366612 0.05714286 0.4230704
#> 184 170206 0.05714286 0.4230704
#> 185 104780 0.05714286 0.4230704
#> 186 511008 0.05714286 0.4230704
#> 187 278234 0.05714286 0.4230704
#> 188 213747 0.05714286 0.4230704
#> 189 190872 0.05714286 0.4230704
#> 190 178915 0.05714286 0.4230704
#> 191 191306 0.05714286 0.4230704
#> 192 510994 0.05714286 0.4230704
#> 193 21698 0.05714286 0.4230704
#> 194 193632 0.05714286 0.4230704
#> 195 469873 0.05714286 0.4230704
#> 196 328422 0.05714286 0.4230704
#> 197 405524 0.05714286 0.4230704
#> 198 367055 0.05714286 0.4230704
#> 199 470114 0.05714286 0.4230704
#> 200 544358 0.05714286 0.4230704
#> 201 204889 0.05714286 0.4230704
#> 202 470812 0.05714286 0.4230704
#> 203 363692 0.05714286 0.4230704
#> 204 171551 0.05714286 0.4230704
#> 205 322235 0.05714286 0.4230704
#> 206 331820 0.05714286 0.4230704
#> 207 158660 0.05714286 0.4230704
#> 208 244304 0.65714286 1.0000000
#> 209 263681 0.65714286 1.0000000
#> 210 326977 0.05714286 0.4230704
#> 211 278873 0.88571429 1.0000000
#> 212 357591 0.22857143 1.0000000
#> 213 127309 1.00000000 1.0000000
#> 214 517293 0.65714286 1.0000000
#> 215 248140 0.05714286 0.4230704
#> 216 211720 1.00000000 1.0000000
#> 217 470172 0.05714286 0.4230704
#> 218 549871 0.65714286 1.0000000
#> 219 347862 0.22857143 1.0000000
#> 220 249661 0.40000000 1.0000000
#> 221 183200 0.05714286 0.4230704
#> 222 42680 0.65714286 1.0000000
#> 223 326792 0.22857143 1.0000000
#> 224 171772 0.40000000 1.0000000
#> 225 541301 0.65714286 1.0000000
#> 226 469482 0.22857143 1.0000000
#> 227 181095 0.40000000 1.0000000
#> 228 299892 0.22857143 1.0000000
#> 229 175497 0.22857143 1.0000000
#> 230 366882 0.88571429 1.0000000
#> 231 109546 0.22857143 1.0000000
#> 232 320147 0.40000000 1.0000000
#> 233 243150 0.22857143 1.0000000
#> 234 191687 0.22857143 1.0000000
#> 235 302974 0.40000000 1.0000000
#> 236 181892 0.22857143 1.0000000
#> 237 181028 0.22857143 1.0000000
#> 238 550814 0.22857143 1.0000000
#> 239 360268 0.40000000 1.0000000
#> 240 263461 0.22857143 1.0000000
#> 241 275891 0.22857143 1.0000000
#> 242 368928 0.22857143 1.0000000
#> 243 230437 0.40000000 1.0000000
#> 244 212128 0.05714286 0.4230704
#> 245 469946 0.97142857 1.0000000
#> 246 103685 0.88571429 1.0000000
#> 247 565565 0.40000000 1.0000000
#> 248 197817 0.88571429 1.0000000
#> 249 173810 0.88571429 1.0000000
#> 250 215890 0.05714286 0.4230704
#>
#> $`WLX+LR`
#> feature Pval adjPval
#> 28 256977 0.01571932 0.3424946
#> 37 309788 0.01571932 0.3424946
#> 39 240687 0.01571932 0.3424946
#> 40 3046 0.01571932 0.3424946
#> 44 573135 0.01571932 0.3424946
#> 50 220494 0.01571932 0.3424946
#> 56 306180 0.01571932 0.3424946
#> 61 27669 0.01571932 0.3424946
#> 67 565399 0.01571932 0.3424946
#> 68 111806 0.01571932 0.3424946
#> 75 65683 0.01571932 0.3424946
#> 84 71074 0.01571932 0.3424946
#> 85 509622 0.01571932 0.3424946
#> 90 360298 0.01571932 0.3424946
#> 100 223329 0.01571932 0.3424946
#> 113 313985 0.01571932 0.3424946
#> 114 574458 0.01571932 0.3424946
#> 119 247010 0.01571932 0.3424946
#> 120 154102 0.01571932 0.3424946
#> 121 129759 0.01571932 0.3424946
#> 122 113626 0.01571932 0.3424946
#> 125 273055 0.01571932 0.3424946
#> 126 551424 0.01571932 0.3424946
#> 127 262714 0.01571932 0.3424946
#> 129 340507 0.01571932 0.3424946
#> 130 84768 0.01571932 0.3424946
#> 135 158370 0.01571932 0.3424946
#> 136 566886 0.01571932 0.3424946
#> 142 216191 0.01571932 0.3424946
#> 144 352632 0.01571932 0.3424946
#> 145 245203 0.01571932 0.3424946
#> 149 265043 0.01571932 0.3424946
#> 156 242675 0.01571932 0.3424946
#> 160 559200 0.01571932 0.3424946
#> 161 367659 0.01571932 0.3424946
#> 162 161227 0.01571932 0.3424946
#> 164 277931 0.01571932 0.3424946
#> 167 527683 0.01571932 0.3424946
#> 170 113866 0.01571932 0.3424946
#> 176 356083 0.01571932 0.3424946
#> 178 200741 0.01571932 0.3424946
#> 185 308892 0.01571932 0.3424946
#> 187 1605 0.01571932 0.3424946
#> 188 250291 0.01571932 0.3424946
#> 190 218467 0.01571932 0.3424946
#> 191 286104 0.01571932 0.3424946
#> 192 511844 0.01571932 0.3424946
#> 196 543366 0.01571932 0.3424946
#> 198 535437 0.01571932 0.3424946
#> 200 159711 0.01571932 0.3424946
#> 201 144381 0.01571932 0.3424946
#> 205 584276 0.01571932 0.3424946
#> 207 570888 0.01571932 0.3424946
#> 208 278390 0.01571932 0.3424946
#> 213 170339 0.01571932 0.3424946
#> 215 93865 0.01571932 0.3424946
#> 219 251499 0.01571932 0.3424946
#> 221 70100 0.01571932 0.3424946
#> 222 97561 0.01571932 0.3424946
#> 223 278272 0.01571932 0.3424946
#> 226 130368 0.01571932 0.3424946
#> 227 76270 0.01571932 0.3424946
#> 228 190723 0.01571932 0.3424946
#> 229 208694 0.01571932 0.3424946
#> 231 222729 0.01571932 0.3424946
#> 233 203846 0.01571932 0.3424946
#> 234 156101 0.01571932 0.3424946
#> 240 13034 0.01571932 0.3424946
#> 244 109907 0.01571932 0.3424946
#> 248 326414 0.01571932 0.3424946
#> 34 36155 0.03185593 0.4416879
#> 46 47916 0.03185593 0.4416879
#> 55 279297 0.03185593 0.4416879
#> 64 150925 0.03185593 0.4416879
#> 71 578268 0.03185593 0.4416879
#> 81 208283 0.03185593 0.4416879
#> 89 546313 0.03185593 0.4416879
#> 92 154822 0.03185593 0.4416879
#> 97 565556 0.03185593 0.4416879
#> 99 100954 0.03185593 0.4416879
#> 109 166835 0.03185593 0.4416879
#> 110 150508 0.03185593 0.4416879
#> 115 113959 0.03185593 0.4416879
#> 118 544749 0.03185593 0.4416879
#> 138 306921 0.03185593 0.4416879
#> 139 114339 0.03185593 0.4416879
#> 147 203722 0.03185593 0.4416879
#> 151 184899 0.03185593 0.4416879
#> 165 235270 0.03185593 0.4416879
#> 168 151018 0.03185593 0.4416879
#> 174 252410 0.03185593 0.4416879
#> 175 331647 0.03185593 0.4416879
#> 179 244491 0.03185593 0.4416879
#> 186 211351 0.03185593 0.4416879
#> 195 154230 0.03185593 0.4416879
#> 197 549552 0.03185593 0.4416879
#> 199 265094 0.03185593 0.4416879
#> 203 215700 0.03185593 0.4416879
#> 214 582012 0.03185593 0.4416879
#> 217 204592 0.03185593 0.4416879
#> 218 252778 0.03185593 0.4416879
#> 220 1791 0.03185593 0.4416879
#> 232 206632 0.03185593 0.4416879
#> 235 575937 0.03185593 0.4416879
#> 236 349634 0.03185593 0.4416879
#> 239 553113 0.03185593 0.4416879
#> 241 210176 0.03185593 0.4416879
#> 242 308535 0.03185593 0.4416879
#> 245 113921 0.03185593 0.4416879
#> 250 534348 0.03185593 0.4416879
#> 3 189047 0.08845351 0.7798066
#> 7 192573 0.08845351 0.7798066
#> 9 180658 0.08845351 0.7798066
#> 12 325407 0.08845351 0.7798066
#> 13 361496 0.08845351 0.7798066
#> 14 316732 0.08845351 0.7798066
#> 15 291090 0.08845351 0.7798066
#> 17 196731 0.08845351 0.7798066
#> 19 290260 0.08845351 0.7798066
#> 20 361480 0.08845351 0.7798066
#> 21 193761 0.08845351 0.7798066
#> 24 357795 0.08845351 0.7798066
#> 26 223059 0.08845351 0.7798066
#> 29 287978 0.08845351 0.7798066
#> 30 357470 0.08845351 0.7798066
#> 31 367433 0.08845351 0.7798066
#> 32 187524 0.08845351 0.7798066
#> 35 518438 0.08845351 0.7798066
#> 36 327476 0.08845351 0.7798066
#> 42 199487 0.08845351 0.7798066
#> 43 191541 0.08845351 0.7798066
#> 45 365676 0.08845351 0.7798066
#> 48 269833 0.08845351 0.7798066
#> 49 194053 0.08845351 0.7798066
#> 51 190464 0.08845351 0.7798066
#> 52 288134 0.08845351 0.7798066
#> 53 194648 0.08845351 0.7798066
#> 58 138006 0.08845351 0.7798066
#> 59 212619 0.08845351 0.7798066
#> 62 293896 0.08845351 0.7798066
#> 63 195173 0.08845351 0.7798066
#> 66 192127 0.08845351 0.7798066
#> 70 193066 0.08845351 0.7798066
#> 76 177339 0.08845351 0.7798066
#> 77 193343 0.08845351 0.7798066
#> 78 203708 0.08845351 0.7798066
#> 83 261912 0.08845351 0.7798066
#> 86 175537 0.08845351 0.7798066
#> 87 557785 0.03712191 0.4923233
#> 96 179460 0.08845351 0.7798066
#> 101 181843 0.08845351 0.7798066
#> 103 310301 0.08845351 0.7798066
#> 106 162162 0.08845351 0.7798066
#> 111 190108 0.08845351 0.7798066
#> 123 82283 0.08845351 0.7798066
#> 124 262724 0.08845351 0.7798066
#> 128 111986 0.07100559 0.7798066
#> 131 250258 0.03712191 0.4923233
#> 141 188646 0.08845351 0.7798066
#> 148 469991 0.08845351 0.7798066
#> 153 346780 0.08845351 0.7798066
#> 154 174348 0.08845351 0.7798066
#> 157 185281 0.08845351 0.7798066
#> 158 167602 0.08845351 0.7798066
#> 169 145786 0.04523193 0.5896267
#> 177 182980 0.08845351 0.7798066
#> 180 204044 0.08845351 0.7798066
#> 183 51024 0.03712191 0.4923233
#> 202 255584 0.04071219 0.5352842
#> 210 216862 0.08845351 0.7798066
#> 225 154268 0.03712191 0.4923233
#> 238 547632 0.03712191 0.4923233
#> 249 294022 0.08845351 0.7798066
#> 11 259569 0.10920535 0.8585391
#> 16 348374 0.10561819 0.8523046
#> 41 470973 0.10920535 0.8585391
#> 54 589024 0.10561819 0.8523046
#> 60 268540 0.10561819 0.8523046
#> 69 302160 0.10561819 0.8523046
#> 72 352304 0.10920535 0.8585391
#> 94 2000 0.10561819 0.8523046
#> 98 109014 0.10561819 0.8523046
#> 102 366612 0.10561819 0.8523046
#> 112 170206 0.10561819 0.8523046
#> 132 104780 0.10561819 0.8523046
#> 150 511008 0.10920535 0.8585391
#> 155 278234 0.10561819 0.8523046
#> 163 213747 0.10561819 0.8523046
#> 171 190872 0.10561819 0.8523046
#> 172 178915 0.10561819 0.8523046
#> 182 191306 0.10561819 0.8523046
#> 184 510994 0.10920535 0.8585391
#> 194 21698 0.10561819 0.8523046
#> 230 193632 0.10561819 0.8523046
#> 73 469873 0.12568184 0.9442661
#> 80 328422 0.12568184 0.9442661
#> 88 405524 0.12568184 0.9442661
#> 104 367055 0.12568184 0.9442661
#> 117 470114 0.12568184 0.9442661
#> 134 544358 0.12568184 0.9442661
#> 146 204889 0.12568184 0.9442661
#> 159 470812 0.12568184 0.9442661
#> 209 363692 0.12568184 0.9442661
#> 5 171551 0.26216189 1.0000000
#> 8 322235 0.26216189 1.0000000
#> 1 331820 1.00000000 1.0000000
#> 2 158660 1.00000000 1.0000000
#> 4 244304 0.31405659 1.0000000
#> 6 263681 0.65006789 1.0000000
#> 10 326977 1.00000000 1.0000000
#> 18 278873 0.92630130 1.0000000
#> 22 357591 0.40443382 1.0000000
#> 23 127309 1.00000000 1.0000000
#> 25 517293 0.92630130 1.0000000
#> 27 248140 1.00000000 1.0000000
#> 33 211720 1.00000000 1.0000000
#> 38 470172 0.63410287 1.0000000
#> 47 549871 0.66473219 1.0000000
#> 57 347862 0.18920661 1.0000000
#> 65 249661 0.89886959 1.0000000
#> 74 183200 0.63410287 1.0000000
#> 79 42680 0.56071994 1.0000000
#> 82 326792 0.18920661 1.0000000
#> 91 171772 0.43206312 1.0000000
#> 93 541301 0.80190256 1.0000000
#> 95 469482 0.25375509 1.0000000
#> 105 181095 0.43206312 1.0000000
#> 107 299892 0.56071994 1.0000000
#> 108 175497 0.43206312 1.0000000
#> 116 366882 1.00000000 1.0000000
#> 133 109546 0.43206312 1.0000000
#> 137 320147 0.88438586 1.0000000
#> 140 243150 0.88438586 1.0000000
#> 143 191687 0.30148798 1.0000000
#> 152 302974 0.43206312 1.0000000
#> 166 181892 0.18920661 1.0000000
#> 173 181028 0.84376712 1.0000000
#> 181 550814 0.84376712 1.0000000
#> 189 360268 0.88438586 1.0000000
#> 193 263461 0.30148798 1.0000000
#> 204 275891 0.84376712 1.0000000
#> 206 368928 0.88438586 1.0000000
#> 211 230437 0.89886959 1.0000000
#> 212 212128 0.63410287 1.0000000
#> 216 469946 0.92630130 1.0000000
#> 224 103685 1.00000000 1.0000000
#> 237 565565 0.89886959 1.0000000
#> 243 197817 0.90937751 1.0000000
#> 246 173810 1.00000000 1.0000000
#> 247 215890 0.63410287 1.0000000
#>
#> $`WLX+KS`
#> feature Pval adjPval
#> 28 256977 0.05714286 0.4230704
#> 37 309788 0.05714286 0.4230704
#> 39 240687 0.05714286 0.4230704
#> 40 3046 0.05714286 0.4230704
#> 44 573135 0.05714286 0.4230704
#> 50 220494 0.05714286 0.4230704
#> 56 306180 0.05714286 0.4230704
#> 61 27669 0.05714286 0.4230704
#> 67 565399 0.05714286 0.4230704
#> 68 111806 0.05714286 0.4230704
#> 75 65683 0.05714286 0.4230704
#> 84 71074 0.05714286 0.4230704
#> 85 509622 0.05714286 0.4230704
#> 90 360298 0.05714286 0.4230704
#> 100 223329 0.05714286 0.4230704
#> 113 313985 0.05714286 0.4230704
#> 114 574458 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
#> 125 273055 0.05714286 0.4230704
#> 126 551424 0.05714286 0.4230704
#> 127 262714 0.05714286 0.4230704
#> 129 340507 0.05714286 0.4230704
#> 130 84768 0.05714286 0.4230704
#> 135 158370 0.05714286 0.4230704
#> 136 566886 0.05714286 0.4230704
#> 142 216191 0.05714286 0.4230704
#> 144 352632 0.05714286 0.4230704
#> 145 245203 0.05714286 0.4230704
#> 149 265043 0.05714286 0.4230704
#> 156 242675 0.05714286 0.4230704
#> 160 559200 0.05714286 0.4230704
#> 161 367659 0.05714286 0.4230704
#> 162 161227 0.05714286 0.4230704
#> 164 277931 0.05714286 0.4230704
#> 167 527683 0.05714286 0.4230704
#> 170 113866 0.05714286 0.4230704
#> 176 356083 0.05714286 0.4230704
#> 178 200741 0.05714286 0.4230704
#> 185 308892 0.05714286 0.4230704
#> 187 1605 0.05714286 0.4230704
#> 188 250291 0.05714286 0.4230704
#> 190 218467 0.05714286 0.4230704
#> 191 286104 0.05714286 0.4230704
#> 192 511844 0.05714286 0.4230704
#> 196 543366 0.05714286 0.4230704
#> 198 535437 0.05714286 0.4230704
#> 200 159711 0.05714286 0.4230704
#> 201 144381 0.05714286 0.4230704
#> 205 584276 0.05714286 0.4230704
#> 207 570888 0.05714286 0.4230704
#> 208 278390 0.05714286 0.4230704
#> 213 170339 0.05714286 0.4230704
#> 215 93865 0.05714286 0.4230704
#> 219 251499 0.05714286 0.4230704
#> 221 70100 0.05714286 0.4230704
#> 222 97561 0.05714286 0.4230704
#> 223 278272 0.05714286 0.4230704
#> 226 130368 0.05714286 0.4230704
#> 227 76270 0.05714286 0.4230704
#> 228 190723 0.05714286 0.4230704
#> 229 208694 0.05714286 0.4230704
#> 231 222729 0.05714286 0.4230704
#> 233 203846 0.05714286 0.4230704
#> 234 156101 0.05714286 0.4230704
#> 240 13034 0.05714286 0.4230704
#> 244 109907 0.05714286 0.4230704
#> 248 326414 0.05714286 0.4230704
#> 34 36155 0.05714286 0.4230704
#> 46 47916 0.05714286 0.4230704
#> 55 279297 0.05714286 0.4230704
#> 64 150925 0.05714286 0.4230704
#> 71 578268 0.05714286 0.4230704
#> 81 208283 0.05714286 0.4230704
#> 89 546313 0.05714286 0.4230704
#> 92 154822 0.05714286 0.4230704
#> 97 565556 0.05714286 0.4230704
#> 99 100954 0.05714286 0.4230704
#> 109 166835 0.05714286 0.4230704
#> 110 150508 0.05714286 0.4230704
#> 115 113959 0.05714286 0.4230704
#> 118 544749 0.05714286 0.4230704
#> 138 306921 0.05714286 0.4230704
#> 139 114339 0.05714286 0.4230704
#> 147 203722 0.05714286 0.4230704
#> 151 184899 0.05714286 0.4230704
#> 165 235270 0.05714286 0.4230704
#> 168 151018 0.05714286 0.4230704
#> 174 252410 0.05714286 0.4230704
#> 175 331647 0.05714286 0.4230704
#> 179 244491 0.05714286 0.4230704
#> 186 211351 0.05714286 0.4230704
#> 195 154230 0.05714286 0.4230704
#> 197 549552 0.05714286 0.4230704
#> 199 265094 0.05714286 0.4230704
#> 203 215700 0.05714286 0.4230704
#> 214 582012 0.05714286 0.4230704
#> 217 204592 0.05714286 0.4230704
#> 218 252778 0.05714286 0.4230704
#> 220 1791 0.05714286 0.4230704
#> 232 206632 0.05714286 0.4230704
#> 235 575937 0.05714286 0.4230704
#> 236 349634 0.05714286 0.4230704
#> 239 553113 0.05714286 0.4230704
#> 241 210176 0.05714286 0.4230704
#> 242 308535 0.05714286 0.4230704
#> 245 113921 0.05714286 0.4230704
#> 250 534348 0.05714286 0.4230704
#> 3 189047 0.05714286 0.4230704
#> 7 192573 0.05714286 0.4230704
#> 9 180658 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
#> 17 196731 0.05714286 0.4230704
#> 19 290260 0.05714286 0.4230704
#> 20 361480 0.05714286 0.4230704
#> 21 193761 0.05714286 0.4230704
#> 24 357795 0.05714286 0.4230704
#> 26 223059 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
#> 35 518438 0.05714286 0.4230704
#> 36 327476 0.05714286 0.4230704
#> 42 199487 0.05714286 0.4230704
#> 43 191541 0.05714286 0.4230704
#> 45 365676 0.05714286 0.4230704
#> 48 269833 0.05714286 0.4230704
#> 49 194053 0.05714286 0.4230704
#> 51 190464 0.05714286 0.4230704
#> 52 288134 0.05714286 0.4230704
#> 53 194648 0.05714286 0.4230704
#> 58 138006 0.05714286 0.4230704
#> 59 212619 0.05714286 0.4230704
#> 62 293896 0.05714286 0.4230704
#> 63 195173 0.05714286 0.4230704
#> 66 192127 0.05714286 0.4230704
#> 70 193066 0.05714286 0.4230704
#> 76 177339 0.05714286 0.4230704
#> 77 193343 0.05714286 0.4230704
#> 78 203708 0.05714286 0.4230704
#> 83 261912 0.05714286 0.4230704
#> 86 175537 0.05714286 0.4230704
#> 87 557785 0.15390114 1.0000000
#> 96 179460 0.05714286 0.4230704
#> 101 181843 0.05714286 0.4230704
#> 103 310301 0.05714286 0.4230704
#> 106 162162 0.05714286 0.4230704
#> 111 190108 0.05714286 0.4230704
#> 123 82283 0.05714286 0.4230704
#> 124 262724 0.05714286 0.4230704
#> 128 111986 0.05714286 0.4230704
#> 131 250258 0.15390114 1.0000000
#> 141 188646 0.05714286 0.4230704
#> 148 469991 0.05714286 0.4230704
#> 153 346780 0.05714286 0.4230704
#> 154 174348 0.05714286 0.4230704
#> 157 185281 0.05714286 0.4230704
#> 158 167602 0.05714286 0.4230704
#> 169 145786 0.51629925 1.0000000
#> 177 182980 0.05714286 0.4230704
#> 180 204044 0.05714286 0.4230704
#> 183 51024 0.15390114 1.0000000
#> 202 255584 0.29829553 1.0000000
#> 210 216862 0.05714286 0.4230704
#> 225 154268 0.15390114 1.0000000
#> 238 547632 0.15390114 1.0000000
#> 249 294022 0.05714286 0.4230704
#> 11 259569 0.05714286 0.4230704
#> 16 348374 0.05714286 0.4230704
#> 41 470973 0.05714286 0.4230704
#> 54 589024 0.05714286 0.4230704
#> 60 268540 0.05714286 0.4230704
#> 69 302160 0.05714286 0.4230704
#> 72 352304 0.05714286 0.4230704
#> 94 2000 0.05714286 0.4230704
#> 98 109014 0.05714286 0.4230704
#> 102 366612 0.05714286 0.4230704
#> 112 170206 0.05714286 0.4230704
#> 132 104780 0.05714286 0.4230704
#> 150 511008 0.05714286 0.4230704
#> 155 278234 0.05714286 0.4230704
#> 163 213747 0.05714286 0.4230704
#> 171 190872 0.05714286 0.4230704
#> 172 178915 0.05714286 0.4230704
#> 182 191306 0.05714286 0.4230704
#> 184 510994 0.05714286 0.4230704
#> 194 21698 0.05714286 0.4230704
#> 230 193632 0.05714286 0.4230704
#> 73 469873 0.05714286 0.4230704
#> 80 328422 0.05714286 0.4230704
#> 88 405524 0.05714286 0.4230704
#> 104 367055 0.05714286 0.4230704
#> 117 470114 0.05714286 0.4230704
#> 134 544358 0.05714286 0.4230704
#> 146 204889 0.05714286 0.4230704
#> 159 470812 0.05714286 0.4230704
#> 209 363692 0.05714286 0.4230704
#> 5 171551 0.05714286 0.4230704
#> 8 322235 0.05714286 0.4230704
#> 1 331820 0.05714286 0.4230704
#> 2 158660 0.05714286 0.4230704
#> 4 244304 0.64316632 1.0000000
#> 6 263681 0.79214898 1.0000000
#> 10 326977 0.05714286 0.4230704
#> 18 278873 0.87293060 1.0000000
#> 22 357591 0.29829553 1.0000000
#> 23 127309 1.00000000 1.0000000
#> 25 517293 0.79214898 1.0000000
#> 27 248140 0.05714286 0.4230704
#> 33 211720 1.00000000 1.0000000
#> 38 470172 0.05714286 0.4230704
#> 47 549871 0.64316632 1.0000000
#> 57 347862 0.15390114 1.0000000
#> 65 249661 0.40000000 1.0000000
#> 74 183200 0.05714286 0.4230704
#> 79 42680 0.53380469 1.0000000
#> 82 326792 0.15390114 1.0000000
#> 91 171772 0.29829553 1.0000000
#> 93 541301 0.79214898 1.0000000
#> 95 469482 0.15390114 1.0000000
#> 105 181095 0.29829553 1.0000000
#> 107 299892 0.29829553 1.0000000
#> 108 175497 0.22857143 1.0000000
#> 116 366882 1.00000000 1.0000000
#> 133 109546 0.22857143 1.0000000
#> 137 320147 0.29829553 1.0000000
#> 140 243150 0.22857143 1.0000000
#> 143 191687 0.22857143 1.0000000
#> 152 302974 0.29829553 1.0000000
#> 166 181892 0.15390114 1.0000000
#> 173 181028 0.15390114 1.0000000
#> 181 550814 0.15390114 1.0000000
#> 189 360268 0.29829553 1.0000000
#> 193 263461 0.22857143 1.0000000
#> 204 275891 0.15390114 1.0000000
#> 206 368928 0.22857143 1.0000000
#> 211 230437 0.40000000 1.0000000
#> 212 212128 0.05714286 0.4230704
#> 216 469946 0.95209035 1.0000000
#> 224 103685 1.00000000 1.0000000
#> 237 565565 0.40000000 1.0000000
#> 243 197817 0.81724014 1.0000000
#> 246 173810 1.00000000 1.0000000
#> 247 215890 0.05714286 0.4230704
#>
#> $`LR+KS`
#> feature Pval adjPval
#> 28 256977 0.01571932 0.3424946
#> 37 309788 0.01571932 0.3424946
#> 39 240687 0.01571932 0.3424946
#> 40 3046 0.01571932 0.3424946
#> 44 573135 0.01571932 0.3424946
#> 50 220494 0.01571932 0.3424946
#> 56 306180 0.01571932 0.3424946
#> 61 27669 0.01571932 0.3424946
#> 67 565399 0.01571932 0.3424946
#> 68 111806 0.01571932 0.3424946
#> 75 65683 0.01571932 0.3424946
#> 84 71074 0.01571932 0.3424946
#> 85 509622 0.01571932 0.3424946
#> 90 360298 0.01571932 0.3424946
#> 100 223329 0.01571932 0.3424946
#> 113 313985 0.01571932 0.3424946
#> 114 574458 0.01571932 0.3424946
#> 119 247010 0.01571932 0.3424946
#> 120 154102 0.01571932 0.3424946
#> 121 129759 0.01571932 0.3424946
#> 122 113626 0.01571932 0.3424946
#> 125 273055 0.01571932 0.3424946
#> 126 551424 0.01571932 0.3424946
#> 127 262714 0.01571932 0.3424946
#> 129 340507 0.01571932 0.3424946
#> 130 84768 0.01571932 0.3424946
#> 135 158370 0.01571932 0.3424946
#> 136 566886 0.01571932 0.3424946
#> 142 216191 0.01571932 0.3424946
#> 144 352632 0.01571932 0.3424946
#> 145 245203 0.01571932 0.3424946
#> 149 265043 0.01571932 0.3424946
#> 156 242675 0.01571932 0.3424946
#> 160 559200 0.01571932 0.3424946
#> 161 367659 0.01571932 0.3424946
#> 162 161227 0.01571932 0.3424946
#> 164 277931 0.01571932 0.3424946
#> 167 527683 0.01571932 0.3424946
#> 170 113866 0.01571932 0.3424946
#> 176 356083 0.01571932 0.3424946
#> 178 200741 0.01571932 0.3424946
#> 185 308892 0.01571932 0.3424946
#> 187 1605 0.01571932 0.3424946
#> 188 250291 0.01571932 0.3424946
#> 190 218467 0.01571932 0.3424946
#> 191 286104 0.01571932 0.3424946
#> 192 511844 0.01571932 0.3424946
#> 196 543366 0.01571932 0.3424946
#> 198 535437 0.01571932 0.3424946
#> 200 159711 0.01571932 0.3424946
#> 201 144381 0.01571932 0.3424946
#> 205 584276 0.01571932 0.3424946
#> 207 570888 0.01571932 0.3424946
#> 208 278390 0.01571932 0.3424946
#> 213 170339 0.01571932 0.3424946
#> 215 93865 0.01571932 0.3424946
#> 219 251499 0.01571932 0.3424946
#> 221 70100 0.01571932 0.3424946
#> 222 97561 0.01571932 0.3424946
#> 223 278272 0.01571932 0.3424946
#> 226 130368 0.01571932 0.3424946
#> 227 76270 0.01571932 0.3424946
#> 228 190723 0.01571932 0.3424946
#> 229 208694 0.01571932 0.3424946
#> 231 222729 0.01571932 0.3424946
#> 233 203846 0.01571932 0.3424946
#> 234 156101 0.01571932 0.3424946
#> 240 13034 0.01571932 0.3424946
#> 244 109907 0.01571932 0.3424946
#> 248 326414 0.01571932 0.3424946
#> 34 36155 0.03185593 0.4416879
#> 46 47916 0.03185593 0.4416879
#> 55 279297 0.03185593 0.4416879
#> 64 150925 0.03185593 0.4416879
#> 71 578268 0.03185593 0.4416879
#> 81 208283 0.03185593 0.4416879
#> 89 546313 0.03185593 0.4416879
#> 92 154822 0.03185593 0.4416879
#> 97 565556 0.03185593 0.4416879
#> 99 100954 0.03185593 0.4416879
#> 109 166835 0.03185593 0.4416879
#> 110 150508 0.03185593 0.4416879
#> 115 113959 0.03185593 0.4416879
#> 118 544749 0.03185593 0.4416879
#> 138 306921 0.03185593 0.4416879
#> 139 114339 0.03185593 0.4416879
#> 147 203722 0.03185593 0.4416879
#> 151 184899 0.03185593 0.4416879
#> 165 235270 0.03185593 0.4416879
#> 168 151018 0.03185593 0.4416879
#> 174 252410 0.03185593 0.4416879
#> 175 331647 0.03185593 0.4416879
#> 179 244491 0.03185593 0.4416879
#> 186 211351 0.03185593 0.4416879
#> 195 154230 0.03185593 0.4416879
#> 197 549552 0.03185593 0.4416879
#> 199 265094 0.03185593 0.4416879
#> 203 215700 0.03185593 0.4416879
#> 214 582012 0.03185593 0.4416879
#> 217 204592 0.03185593 0.4416879
#> 218 252778 0.03185593 0.4416879
#> 220 1791 0.03185593 0.4416879
#> 232 206632 0.03185593 0.4416879
#> 235 575937 0.03185593 0.4416879
#> 236 349634 0.03185593 0.4416879
#> 239 553113 0.03185593 0.4416879
#> 241 210176 0.03185593 0.4416879
#> 242 308535 0.03185593 0.4416879
#> 245 113921 0.03185593 0.4416879
#> 250 534348 0.03185593 0.4416879
#> 3 189047 0.08845351 0.7798066
#> 7 192573 0.08845351 0.7798066
#> 9 180658 0.08845351 0.7798066
#> 12 325407 0.08845351 0.7798066
#> 13 361496 0.08845351 0.7798066
#> 14 316732 0.08845351 0.7798066
#> 15 291090 0.08845351 0.7798066
#> 17 196731 0.08845351 0.7798066
#> 19 290260 0.08845351 0.7798066
#> 20 361480 0.08845351 0.7798066
#> 21 193761 0.08845351 0.7798066
#> 24 357795 0.08845351 0.7798066
#> 26 223059 0.08845351 0.7798066
#> 29 287978 0.08845351 0.7798066
#> 30 357470 0.08845351 0.7798066
#> 31 367433 0.08845351 0.7798066
#> 32 187524 0.08845351 0.7798066
#> 35 518438 0.08845351 0.7798066
#> 36 327476 0.08845351 0.7798066
#> 42 199487 0.08845351 0.7798066
#> 43 191541 0.08845351 0.7798066
#> 45 365676 0.08845351 0.7798066
#> 48 269833 0.08845351 0.7798066
#> 49 194053 0.08845351 0.7798066
#> 51 190464 0.08845351 0.7798066
#> 52 288134 0.08845351 0.7798066
#> 53 194648 0.08845351 0.7798066
#> 58 138006 0.08845351 0.7798066
#> 59 212619 0.08845351 0.7798066
#> 62 293896 0.08845351 0.7798066
#> 63 195173 0.08845351 0.7798066
#> 66 192127 0.08845351 0.7798066
#> 70 193066 0.08845351 0.7798066
#> 76 177339 0.08845351 0.7798066
#> 77 193343 0.08845351 0.7798066
#> 78 203708 0.08845351 0.7798066
#> 83 261912 0.08845351 0.7798066
#> 86 175537 0.08845351 0.7798066
#> 87 557785 0.04071219 0.5399389
#> 96 179460 0.08845351 0.7798066
#> 101 181843 0.08845351 0.7798066
#> 103 310301 0.08845351 0.7798066
#> 106 162162 0.08845351 0.7798066
#> 111 190108 0.08845351 0.7798066
#> 123 82283 0.08845351 0.7798066
#> 124 262724 0.08845351 0.7798066
#> 128 111986 0.07100559 0.7798066
#> 131 250258 0.04071219 0.5399389
#> 141 188646 0.08845351 0.7798066
#> 148 469991 0.08845351 0.7798066
#> 153 346780 0.08845351 0.7798066
#> 154 174348 0.08845351 0.7798066
#> 157 185281 0.08845351 0.7798066
#> 158 167602 0.08845351 0.7798066
#> 169 145786 0.04295068 0.5598893
#> 177 182980 0.08845351 0.7798066
#> 180 204044 0.08845351 0.7798066
#> 183 51024 0.04071219 0.5399389
#> 202 255584 0.04295068 0.5598893
#> 210 216862 0.08845351 0.7798066
#> 225 154268 0.04071219 0.5399389
#> 238 547632 0.04071219 0.5399389
#> 249 294022 0.08845351 0.7798066
#> 11 259569 0.10920535 0.8585391
#> 16 348374 0.10561819 0.8523046
#> 41 470973 0.10920535 0.8585391
#> 54 589024 0.10561819 0.8523046
#> 60 268540 0.10561819 0.8523046
#> 69 302160 0.10561819 0.8523046
#> 72 352304 0.10920535 0.8585391
#> 94 2000 0.10561819 0.8523046
#> 98 109014 0.10561819 0.8523046
#> 102 366612 0.10561819 0.8523046
#> 112 170206 0.10561819 0.8523046
#> 132 104780 0.10561819 0.8523046
#> 150 511008 0.10920535 0.8585391
#> 155 278234 0.10561819 0.8523046
#> 163 213747 0.10561819 0.8523046
#> 171 190872 0.10561819 0.8523046
#> 172 178915 0.10561819 0.8523046
#> 182 191306 0.10561819 0.8523046
#> 184 510994 0.10920535 0.8585391
#> 194 21698 0.10561819 0.8523046
#> 230 193632 0.10561819 0.8523046
#> 73 469873 0.12568184 0.9442661
#> 80 328422 0.12568184 0.9442661
#> 88 405524 0.12568184 0.9442661
#> 104 367055 0.12568184 0.9442661
#> 117 470114 0.12568184 0.9442661
#> 134 544358 0.12568184 0.9442661
#> 146 204889 0.12568184 0.9442661
#> 159 470812 0.12568184 0.9442661
#> 209 363692 0.12568184 0.9442661
#> 5 171551 0.26216189 1.0000000
#> 8 322235 0.26216189 1.0000000
#> 1 331820 1.00000000 1.0000000
#> 2 158660 1.00000000 1.0000000
#> 4 244304 0.32663047 1.0000000
#> 6 263681 0.41920953 1.0000000
#> 10 326977 1.00000000 1.0000000
#> 18 278873 0.93092480 1.0000000
#> 22 357591 0.30148798 1.0000000
#> 23 127309 1.00000000 1.0000000
#> 25 517293 0.91074697 1.0000000
#> 27 248140 1.00000000 1.0000000
#> 33 211720 1.00000000 1.0000000
#> 38 470172 0.63410287 1.0000000
#> 47 549871 0.67772220 1.0000000
#> 57 347862 0.30148798 1.0000000
#> 65 249661 0.89886959 1.0000000
#> 74 183200 0.63410287 1.0000000
#> 79 42680 0.67772220 1.0000000
#> 82 326792 0.30148798 1.0000000
#> 91 171772 0.56071994 1.0000000
#> 93 541301 0.67772220 1.0000000
#> 95 469482 0.43206312 1.0000000
#> 105 181095 0.56071994 1.0000000
#> 107 299892 0.43206312 1.0000000
#> 108 175497 0.43206312 1.0000000
#> 116 366882 0.93092480 1.0000000
#> 133 109546 0.43206312 1.0000000
#> 137 320147 0.89886959 1.0000000
#> 140 243150 0.88438586 1.0000000
#> 143 191687 0.30148798 1.0000000
#> 152 302974 0.56071994 1.0000000
#> 166 181892 0.30148798 1.0000000
#> 173 181028 0.88438586 1.0000000
#> 181 550814 0.88438586 1.0000000
#> 189 360268 0.89886959 1.0000000
#> 193 263461 0.30148798 1.0000000
#> 204 275891 0.88438586 1.0000000
#> 206 368928 0.88438586 1.0000000
#> 211 230437 0.89886959 1.0000000
#> 212 212128 0.63410287 1.0000000
#> 216 469946 0.96381500 1.0000000
#> 224 103685 0.93092480 1.0000000
#> 237 565565 0.89886959 1.0000000
#> 243 197817 0.93092480 1.0000000
#> 246 173810 0.93092480 1.0000000
#> 247 215890 0.63410287 1.0000000
Because the core method is NULL, this example combines only enhancer tests.
The $ensembles element reports the CCT results for the enhancer combinations
when return_subensembles = TRUE.
Single-Cell Example
The single-cell example uses a small SingleCellExperiment object with two cell
groups. The object is wrapped in a MultiAssayExperiment before being passed to
DAssemble, matching the same input architecture used above.
set.seed(1)
n_genes <- 120L
n_cells <- 20L
cell_type <- factor(rep(c("control", "treated"), each = n_cells / 2L))
counts <- matrix(
stats::rnbinom(n_genes * n_cells, mu = 20, size = 5),
nrow = n_genes,
dimnames = list(
paste0("gene", seq_len(n_genes)),
paste0("cell", seq_len(n_cells))
)
)
counts[seq_len(12L), cell_type == "treated"] <-
counts[seq_len(12L), cell_type == "treated"] + 15L
metadata <- data.frame(
CellType = cell_type,
row.names = colnames(counts)
)
sce <- SingleCellExperiment::SingleCellExperiment(
assays = list(counts = counts),
colData = S4Vectors::DataFrame(metadata)
)
mae <- MultiAssayExperiment::MultiAssayExperiment(
experiments = list(single_cell = sce),
colData = S4Vectors::DataFrame(metadata)
)
res <- DAssemble::DAssemble(
features = mae,
assay_name = "single_cell",
core_method = "edgeR",
enhancers = c("WLX", "LR", "KS"),
enhancer_norm = "TSS",
expVar = "CellType",
p_adj = "BH",
return_components = TRUE,
return_subensembles = TRUE
)
#> calcNormFactors has been renamed to normLibSizes
head(res$res)
#> feature metadata pval_core pval_WLX coef_LR pval_LR pval_KS
#> 1 gene1 CellType 0.1319973332 0.089209552 NA NA 0.1678213427
#> 2 gene2 CellType 0.0127052773 0.006841456 NA NA 0.0123406006
#> 3 gene3 CellType 0.0610776934 0.089209552 NA NA 0.1678213427
#> 4 gene4 CellType 0.0001603293 0.001050034 NA NA 0.0002165018
#> 5 gene5 CellType 0.0791618751 0.063012839 NA NA 0.0524475524
#> 6 gene6 CellType 0.0032635317 0.008930698 NA NA 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"