TiDEomics Tutorial

Tianen He1*

1University of Oxford

*tianen.he@ndm.ox.ac.uk

Package

TiDEomics 0.99.4

0.1 Introduction

TiDEomics (Time-course Differential Expression analysis of omics data) is an R package for multi-group time-course omics analysis built on SummarizedExperiment, especially for comparison of more than two conditions across time.

TiDEomics provides:

• An integrated workflow from data processing to functional interpretation, on different omics data types in feature x sample matrix format, with handling of missing values.

• An approach to separate time-dominant, group-dominant, and group-specific temporal effects through the combination of pairwise differential expression, variance decomposition, and co-expression module analysis (WGCNA).

• A filtering strategy based on residual variance after mixed-model variance decomposition, prioritising features with structured differential expression over unexplained variation. In comparison, traditional filtering by total variance or coefficient of variation is less appropriate when biological variation across groups and time course is expected.

Existing packages on time-course omics analysis are available, typically designed for transcriptomics data. Some packages are limited to two-group comparisons (e.g. splineTimeR) or expect raw sequencing counts (e.g. ImpulseDE2, TCseq). maSigPro supports multi-group time-course DE analysis via polynomial regression, complementary to TiDEomics with fewer assumptions about temporal trends. TiDEomics also integrates QC, filtering and downstream functional enrichment within the same workflow.

TiDEomics incorporates Trendy (Bacher et al. 2018) which operates on single time series to estimate breakpoints per group per feature, and derived variance decomposition from PALMO (Vasaikar et al. 2023) and introduced residual variance as a filtering criterion.

This tutorial demonstrates a basic workflow, explains key parameters, and gives practical tips.

Key features covered:

• Preparing input: Create a SummarizedExperiment object with the data matrix and sample annotation, with checks for correct formatting.

• Quality control: Visualise value distributions and missingness patterns.

• Normalisation, grouping and merging replicates: Normalise each feature to the starting time point when focused on changes from baseline. Transform the data for analyses that require single groups or single time series.

• Sample relationships visualisation (correlation matrix, PCA, UMAP): Explore global structure and major sources of variance, check for batch effects, and identify features driving PCs.

• Pairwise differential expression (by time or group): Identify DE features between pairs of time points within each group, and between pairs of groups at each time point.

• Classification by feature property (randomness and overall fold change): Classify features into different categories (e.g., non-random vs. random, differentially expressed vs. stable) based on properties including trend significance and maximum fold change.

• Segmented regression with Trendy: Estimate breakpoints for each feature in each group and summarise dynamic patterns.

• Variance decomposition modified from PALMO: Quantify relative contributions from Time, Group, and Residual, distinguish time or group-dependent DE features and “noisy” features.

• Module identification with WGCNA: Identify co-expression modules with different temporal and group-specific patterns.

• Functional enrichment (gene ontology, drugs, etc.): Interpret biological meaning of identified DE features and modules.

0.2 Installation

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("TiDEomics")

library(TiDEomics)
#> 
#> Registered S3 method overwritten by 'lme4':
#>   method           from
#>   na.action.merMod car
library(SummarizedExperiment)
#> Loading required package: MatrixGenerics
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library(org.Mm.eg.db)
#> Loading required package: AnnotationDbi
#> 

0.3 Example input

TiDEomics expects:

• data: log2-transformed data.frame
  • rows: features (e.g. genes, proteins)
  • columns: First column should be feature identifiers. Other columns are numeric measurements of features for each sample.
  • Missing values (NA) are allowed.
• sample_ann: data.frame with columns:
  • Sample: sample names, match column names of data
  • Group: experimental group
  • Time: numeric time point
  • Replicate: replicate ID (optional, auto-generated if absent)
  • Batch: batch ID (optional)
  • Subject: biological subject ID for repeated-measures designs (optional, set via subject_col)

Example: subset of GSE263759 data set published in Traxler et al. (2025).

• Ensembl IDs were mapped to symbols, genes with all zero counts were excluded.
• Use time 0 untreated samples for other groups’ time 0.
• Sample 500 random genes.
• Normalised to log2(CPM + 1)

data("tutorial_sample_info", package = "TiDEomics")
data("tutorial_data", package = "TiDEomics")

0.3.1 Preprocessing for different data types

TiDEomics expects log-transformed, normalised data as input. The core workflow is compatible with any quantitative omics data in a feature x sample matrix. Enrichment analysis (enrichGO_list, enrichGO_rank, enrichR_list, enrich_msigdb) requires gene identifiers and is not applicable to non-gene features (e.g., metabolites, lipids).

Batch correction should be applied before input if batch effects are present.

For the most common scenarios in transcriptomics and proteomics:

• RNA-seq data should be normalised for sequencing depth and composition bias before TiDEomics, e.g. with TMM. The tutorial uses log2(CPM + 1) for simplicity. For pairwise differential expression, trend = TRUE can be used to account for the mean-variance relationship. See below section on pairwise differential expression for details.

• MS-based proteomics data processing software may include normalisation when producing protein x sample matrix. Depending on software and settings, manual normalisation and log transformation may be required, e.g. median normalisation. Missing values are accepted in most TiDEomics functions, and a basic imputation function impute_groups() is provided.

0.4 Workflow

We present the workflow as below sections, explaining the usage and parameters.

0.4.1 Data preparation

data_obj <- create_input(
    data = tutorial_data,
    sample_ann = tutorial_sample_info)
#> No Subject column specified. Samples treated as independent. For repeated-measures designs, set subject_col to the column identifying biological subjects.
#> Converting 'Group' column to factor. Default order is alphabetical.
#> Converting 'Replicate' column to factor. Default order is numerical.
#> Converting 'Batch' column to factor. Default order is numerical.

data_obj

#> class: SummarizedExperiment 
#> dim: 500 40 
#> metadata(0):
#> assays(1): orig
#> rownames(500): Scpep1 Dbt ... Rps2-ps8 Gm30082
#> rowData names(0):
#> colnames(40): RNA_IFNbeta_0h_R1_1 RNA_IFNbeta_0h_R2_1 ...
#>   RNA_untreated_24h_R1_2 RNA_untreated_24h_R2_2
#> colData names(5): Sample Group Time Replicate Batch

The SummarizedExperiment stores data in named assays: "orig" (assay 1) for the original input and "norm" (assay 2) for time-0-normalised values (added by normalise_to_start()). Downstream functions accept either the name or the index.

assays(data_obj)[["orig"]] |> as.data.frame() |> utils::head()

colData(data_obj) # sample annotation

#> DataFrame with 40 rows and 5 columns
#>                                        Sample     Group      Time Replicate
#>                                   <character>  <factor> <numeric>  <factor>
#> RNA_IFNbeta_0h_R1_1       RNA_IFNbeta_0h_R1_1   IFNbeta         0         1
#> RNA_IFNbeta_0h_R2_1       RNA_IFNbeta_0h_R2_1   IFNbeta         0         2
#> RNA_IFNbeta_2h_R1_1       RNA_IFNbeta_2h_R1_1   IFNbeta         2         1
#> RNA_IFNbeta_2h_R2_1       RNA_IFNbeta_2h_R2_1   IFNbeta         2         2
#> RNA_IFNbeta_4h_R1_1       RNA_IFNbeta_4h_R1_1   IFNbeta         4         1
#> ...                                       ...       ...       ...       ...
#> RNA_untreated_0h_R1_1   RNA_untreated_0h_R1_1 untreated         0         1
#> RNA_untreated_0h_R2_1   RNA_untreated_0h_R2_1 untreated         0         2
#> RNA_untreated_8h_R2_2   RNA_untreated_8h_R2_2 untreated         8         2
#> RNA_untreated_24h_R1_2 RNA_untreated_24h_R1_2 untreated        24         1
#> RNA_untreated_24h_R2_2 RNA_untreated_24h_R2_2 untreated        24         2
#>                           Batch
#>                        <factor>
#> RNA_IFNbeta_0h_R1_1           1
#> RNA_IFNbeta_0h_R2_1           1
#> RNA_IFNbeta_2h_R1_1           1
#> RNA_IFNbeta_2h_R2_1           1
#> RNA_IFNbeta_4h_R1_1           1
#> ...                         ...
#> RNA_untreated_0h_R1_1         1
#> RNA_untreated_0h_R2_1         1
#> RNA_untreated_8h_R2_2         2
#> RNA_untreated_24h_R1_2        2
#> RNA_untreated_24h_R2_2        2

0.4.2 Optional: custom color palette

By default, TiDEomics generates a default color palette (scales::pal_hue()) based on the number of groups. A custom palette for each group can be set with set_custom_palette(), which will be used in all subsequent plotting functions where applicable.

custom_palette <- c(
    "untreated" = "#1b9e77", "IFNbeta" = "#d95f02",
    "IFNgamma" = "#7570b3", "LPS" = "#e7298a"
)
set_custom_palette(custom_palette)
#> Custom palette has been set successfully for groups untreated, IFNbeta, IFNgamma, LPS.

0.4.3 Quality control

plot_distribution(data_obj, facet_by = "Group")
#> Picking joint bandwidth of 1.3
#> Picking joint bandwidth of 1.31
#> Picking joint bandwidth of 1.3
#> Picking joint bandwidth of 1.32

If distributions differ significantly, consider global normalisation (e.g., quantile, median) before using TiDEomics.

For data with missing values (e.g., proteomics), plot_ID() and plot_missing() can be used to check missingness patterns.

0.4.4 Normalisation to starting time point

normalise_to_start() subtracts the baseline value from each feature at starting time point (referred to as time 0) or the first available time point if the feature is missing at time 0.

Two modes are available for defining the baseline:

• by_subject = FALSE (default): group-level baseline, mean of all samples at starting time point in the group. This is used when no subject-level information is available or when between-subject baseline differences are of interest.

• by_subject = TRUE: subject-level baseline, mean of all samples at starting time point for each subject. This is used when subject-level information is available and the focus is on subject-specific changes from baseline.

data_obj <- normalise_to_start(data_obj)
#> Normalising to group baseline at each feature's first non-NA time point.

Both original and time-0 normalised data are stored in the SummarizedExperiment object and downstream functions allow the user to specify which to use for each analysis (assay = 1 or 'orig' for original, assay = 2 or 'norm' for time-0 normalised).

Use original data when absolute abundance differences across groups are of interest (e.g., constitutively different baseline levels). Use time-0 normalised data when relative changes from baseline are of interest.

0.4.5 Splitting groups and merging replicates

Use split_groups() to obtain group-specific SummarizedExperiment objects and merge_replicates() to average replicates for analyses / visualisation that require single time series for features, including calc_feature_property(), run_Trendy(), plot_modules_v() and plot_modules_h().

data_obj_list <- split_groups(data_obj)

data_obj_merged_list <- merge_replicates(data_obj_list)

data_obj_merged <- merge_groups(data_obj_merged_list)

0.4.6 Sample relationships (correlation, PCA, UMAP)

Correlation matrix can be plotted with plot_cor_matrix() to check sample relationships and potential batch effects.

plot_cor_matrix(data_obj,
    method = "spearman",
    label_rep = TRUE, label_batch = TRUE,
    cellwidth = 2, cellheight = 2
)

High within-replicate correlation and clustering by Group or Time increase confidence in data quality and downstream analysis. If batch effects are present, consider batch correction before using TiDEomics.

There are multiple functions for PCA and UMAP:

• Use plot_pca() to visualise sample relationships in 2D. Output loadings can be used to identify features driving the principal components.

• Use plot_pca_3D() to visualise sample relationships in 3D.

• Use PCAtools::eigencorplot() to correlate PCs with Group and Time.

• Use plot_umap() for UMAP and set seed for reproducibility.

Features with missing values (NA) are automatically excluded from PCA and UMAP. Consider imputation (e.g., group-wise minimum imputation with split_groups(), impute_groups() and merge_groups()) to include features with missing values in PCA and UMAP.

PC <- plot_pca(data_obj,
    # pc1 = 1, pc2 = 2, # default to plot PC1 and PC2
    plot_screeplot = TRUE,
    plot_loadings = FALSE,
    plot_morepc = TRUE,
    circle = FALSE,
    morepc = 1:5
)
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> ℹ The deprecated feature was likely used in the PCAtools package.
#>   Please report the issue to the authors.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> Warning: Using size for a discrete variable is not advised.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
PC$p_list

#> $p3

#> 
#> $p1

#> 
#> $p5

plot_pca_3D(PC$pca, pcs = 1:3)
#> Warning: `line.width` does not currently support multiple values.
#> Warning: `line.width` does not currently support multiple values.
#> Warning: `line.width` does not currently support multiple values.
#> Warning: `line.width` does not currently support multiple values.

Note: the 3D plot may not display properly in some html, but should work in an interactive R session.

PCAtools::eigencorplot(PC$pca,
    metavars = c("Group", "Time"),
    components = paste0("PC", 1:5),
    col = colorRampPalette(c("#3C5488FF", "white", "#E64B35FF"))(100),
    colCorval = "black"
)
#> Warning in PCAtools::eigencorplot(PC$pca, metavars = c("Group", "Time"), :
#> Group is not numeric - please check the source data as non-numeric variables
#> will be coerced to numeric

umap <- plot_umap(data_obj, seed = 1234)
#> Using n_neighbors = 8
#> Warning: Using size for a discrete variable is not advised.
umap$p_list

#> $p3

#> 
#> $p1

PCA and UMAP can also be performed separately on each group to explore within-group structure and dynamics.

By default, when plotting PCA by group, circles and arrows are added to show the trajectory of samples along time course. Set circle = FALSE or arrow = FALSE to remove circles and arrows.

plot_pca_by_group(data_obj, circle = TRUE, arrow = TRUE, legend_pos = "top")
#> Warning: Using size for a discrete variable is not advised.
#> Using size for a discrete variable is not advised.
#> Using size for a discrete variable is not advised.
#> Using size for a discrete variable is not advised.

# also accepts a list: plot_pca_by_group(data_obj_list)

plot_umap_by_group(data_obj, seed = 1234, legend_pos = "top")
#> Using n_neighbors = 5
#> Warning: Using size for a discrete variable is not advised.
#> Using n_neighbors = 5
#> Warning: Using size for a discrete variable is not advised.
#> Using n_neighbors = 5
#> Warning: Using size for a discrete variable is not advised.
#> Using n_neighbors = 4
#> Warning: Using size for a discrete variable is not advised.

# also accepts a list: plot_umap_by_group(data_obj_list)

0.4.7 Pairwise differential expression

The two DE functions for pairwise comparison between time points and groups are built based on the limma package (Ritchie et al. 2015).

DE_between_time():

• Compare pairs of time points within each group.

• Output: Nested lists of DE statistics tables, including all features or only significant features.

• Use plot_DE_between_time() to summarise number of DE features per pair of time points per group.

• When a Subject column is provided via subject_col in create_input(), DE_between_time() automatically uses a paired design via limma::duplicateCorrelation(block = Subject).

DE_between_group():

• Compare pairs of groups at each time point.

• Output: Nested lists of DE statistics tables, including all features or only significant features.

• Use plot_DE_between_group() to summarise number of DE features per pair of groups with line plots.

Common parameters for both functions:

• assay: index of assay to use (1 = original, 2 = time0-normalised if available)

• filter: minimal number of non-NA replicates for the feature to be included in DE testing (e.g., filter = 1 means requiring features to have at least 1 non-NA value at both time points in the specific group, or in both groups at the specific time point).

• trend: passed to limma::eBayes(). Set to TRUE for RNA-seq count-derived data to model the mean-variance trend. FALSE (default) is usually appropriate for other types of data, where the trend is typically weak. The trend can be visualised with limma::plotSA() with elements of $fit_list of both functions’ output.

• Default thresholds for DE are p.adj < 0.05 and |log2FC| > 1.

• plot_DE_between_time() and plot_DE_between_group() allow re-filtering DE by setting new thresholds.

Please note that the example data only has two replicates per time point. More replicates are recommended for robust pairwise DE analysis.

DE_between_time():

DE_between_time_out <- DE_between_time(data_obj, assay = 1,
    filter = 1, trend = FALSE)
#> Comparing group IFNbeta time 2 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 4 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 6 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 8 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 24 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 4 to 2: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 6 to 2: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 8 to 2: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 24 to 2: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 6 to 4: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 8 to 4: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 24 to 4: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 8 to 6: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 24 to 6: keeping 500 of 500 features (100.0%)
#> Comparing group IFNbeta time 24 to 8: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 2 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 4 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 6 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 8 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 24 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 4 to 2: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 6 to 2: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 8 to 2: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 24 to 2: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 6 to 4: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 8 to 4: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 24 to 4: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 8 to 6: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 24 to 6: keeping 500 of 500 features (100.0%)
#> Comparing group IFNgamma time 24 to 8: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 2 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 4 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 6 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 8 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 24 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 4 to 2: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 6 to 2: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 8 to 2: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 24 to 2: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 6 to 4: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 8 to 4: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 24 to 4: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 8 to 6: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 24 to 6: keeping 500 of 500 features (100.0%)
#> Comparing group LPS time 24 to 8: keeping 500 of 500 features (100.0%)
#> Comparing group untreated time 8 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group untreated time 24 to 0: keeping 500 of 500 features (100.0%)
#> Comparing group untreated time 24 to 8: keeping 500 of 500 features (100.0%)

DE_between_time_out$all_list$IFNbeta$`t2-t0` |> utils::head()

DE_between_time_out$de_list$IFNbeta$`t2-t0` |> utils::head()

# Filtered with thresholds in `DE_between_time()`
plot_DE_between_time(DE_between_time_out,
    fontsize = 8, value = FALSE, nrow = 1, heatmap_width = 3
)

# Re-filtering with new thresholds
plot_DE_between_time(DE_between_time_out,
    fontsize = 8, value = FALSE, nrow = 1, heatmap_width = 3,
    adjP_thres = 0.01, logFC_thres = 1
)
#> Re-filtering DE features with adjP_thres = 0.01 and logFC_thres = 1.

DE_between_group():

DE_between_group_out <- DE_between_group(data_obj, assay = 2,
    filter = 1, trend = TRUE)
#> Comparing group IFNgamma to IFNbeta at Time 0: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to IFNbeta at Time 2: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to IFNbeta at Time 4: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to IFNbeta at Time 6: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to IFNbeta at Time 8: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to IFNbeta at Time 24: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNbeta at Time 0: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNbeta at Time 2: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNbeta at Time 4: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNbeta at Time 6: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNbeta at Time 8: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNbeta at Time 24: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing untreated vs IFNbeta: time points only in IFNbeta: 2, 4, 6; only in untreated: none
#> Comparing group untreated to IFNbeta at Time 0: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated to IFNbeta at Time 8: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated to IFNbeta at Time 24: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to IFNgamma at Time 0: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to IFNgamma at Time 2: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to IFNgamma at Time 4: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to IFNgamma at Time 6: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to IFNgamma at Time 8: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to IFNgamma at Time 24: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNgamma at Time 0: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNgamma at Time 2: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNgamma at Time 4: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNgamma at Time 6: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNgamma at Time 8: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to IFNgamma at Time 24: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing untreated vs IFNgamma: time points only in IFNgamma: 2, 4, 6; only in untreated: none
#> Comparing group untreated to IFNgamma at Time 0: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated to IFNgamma at Time 8: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated to IFNgamma at Time 24: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to LPS at Time 0: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to LPS at Time 2: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to LPS at Time 4: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to LPS at Time 6: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to LPS at Time 8: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to LPS at Time 24: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to LPS at Time 0: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to LPS at Time 2: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to LPS at Time 4: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to LPS at Time 6: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to LPS at Time 8: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to LPS at Time 24: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing untreated vs LPS: time points only in LPS: 2, 4, 6; only in untreated: none
#> Comparing group untreated to LPS at Time 0: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated to LPS at Time 8: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group untreated to LPS at Time 24: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing IFNbeta vs untreated: time points only in untreated: none; only in IFNbeta: 2, 4, 6
#> Comparing group IFNbeta to untreated at Time 0: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to untreated at Time 8: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNbeta to untreated at Time 24: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing IFNgamma vs untreated: time points only in untreated: none; only in IFNgamma: 2, 4, 6
#> Comparing group IFNgamma to untreated at Time 0: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to untreated at Time 8: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group IFNgamma to untreated at Time 24: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing LPS vs untreated: time points only in untreated: none; only in LPS: 2, 4, 6
#> Comparing group LPS to untreated at Time 0: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to untreated at Time 8: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero
#> Comparing group LPS to untreated at Time 24: keeping 500 of 500 features (100.0%)
#> Warning: Zero sample variances detected, have been offset away from zero

# Filtered with thresholds in `DE_between_group()`
plot_DE_between_group(DE_between_group_out)

#> $IFNbeta

#> 
#> $IFNgamma

#> 
#> $LPS

#> 
#> $untreated

# Re-filtering with new thresholds
plot_DE_between_group(DE_between_group_out, adjP_thres = 0.01, logFC_thres = 1)
#> Re-filtering DE features with adjP_thres = 0.01 and logFC_thres = 1.

#> $IFNbeta

#> 
#> $IFNgamma

#> 
#> $LPS

#> 
#> $untreated

DE_between_group_out$all_list$`IFNgamma-untreated`$`24` |> utils::head()

DE_between_group_out$de_list$`IFNgamma-untreated` |> utils::head()

Output of DE_between_group()and DE_between_time() can be plotted with plot_volcano() to visualise DE features of selected group(s) and time point(s).

plot_volcano(DE_between_group_out,
    group1 = "untreated", group2 = "IFNgamma", time = 24,
    logFC_thres = 0.5, adjP_thres = 0.05, label = TRUE)

0.4.8 Feature properties and classification

calc_feature_property() computes per-feature properties:

• P_trend: calculated with randtests::bartels.rank.test(), testing for non-randomness of the time profile (i.e., whether the feature shows a significant overall trend across time points).

• Max_FC: maximum fold change across time points, calculated as the difference between the feature’s maximum and minimum abundance across time points. This captures the overall magnitude of change across the time course, regardless of the specific time points at which changes occur.

• Max_FC_time: the difference between time points with maximum and minimum abundance, providing information about the direction (up or down) and duration of changing.

• Exp_threshold: user-set value threshold for a feature to be considered expressed in a sample. This can be used to filter features based on expression level, e.g. setting threshold = 0 means that only values > 0 (log2(CPM + 1) > 0, i.e. CPM > 0 in the tutorial dataset) are considered expressed. The default is NULL, which means all non-NA values are considered expressed.

• T_total, T_exp and Exp_ratio: number of total time points, number of expressed time points with values > Exp_threshold (or non-NA values if threshold = NULL), and proportion of expressed time points (T_exp / T_total).

• Rho_time: Spearman correlation with time (positive = up, negative = down). Requires at least 3 expressed time points.

• Peak_ratio: proportion of time points that are local maxima (0 = monotonic, higher = more oscillatory).

• Log2_CV: log2(1 + SD / |mean|), coefficient of variation for relative temporal variability.

• AUC: area under the time-0-normalised curve from assay 2. Positive = net increase from baseline, negative = net decrease. NA if assay 2 is not available (run normalise_to_start() first).

The property values can be used to classify features into different categories (e.g., non-random vs. random, differentially expressed vs. stable) for downstream analysis and interpretation. For example, features with P_trend < 0.05 and Max_FC >= 1 can be candidates for non-randomly overall-changing DE features.

Note:

• When replicates are present, the input should be the mean of replicates at each time point, output of merge_replicates().

• The function is set to require at least 3 values > threshold to calculate P_trend.

data_obj_merged_list <- calc_feature_property(data_obj_merged_list,
    threshold = 0)
property_tb <- summarise_feature_property(data_obj_merged_list)

utils::head(property_tb)

Features expressed in only one group or a subset of groups can be extracted with group_specific_features() based on the output table of summarise_feature_property().

group_specific_features(property_tb, groups = c("untreated"),
    genename = FALSE, GO = FALSE
)
#> Filtering criteria: >=50% values >0 in >=1 of groups: untreated

#> $features
#> [1] "Gm12117" "Gm6689"  "Mmp17"

0.4.9 Segmented regression analysis

run_Trendy fits segmented linear models to the time course for each feature to estimate breakpoints with Trendy package (Bacher et al. 2018), see Trendy for details.

Notes:

• The function requires complete input (no NA). When data contains missing values (e.g. proteomics), use impute_groups() to impute (default: group minimum; pass fun = function(x) min(x) / 2 for half-minimum, fun = median for median imputation, etc.), or restrict to features with complete data.

• If feature is not specified, the function will run on all features filtered by expression / missing rate, which requires running calc_feature_property() before impute_groups().

• Number of time points needed = [# segments] x [minimum number of samples in a segment]. For example, when # segments = 2 (maxK = 1) and minNumInSeg = 2, at least 4 time points are needed.

data_obj_merged_imp_list <- impute_groups(data_obj_merged_list)
#> Group IFNbeta: no missing values.
#> Group IFNgamma: no missing values.
#> Group LPS: no missing values.
#> Group untreated: no missing values.

A subset of features is used for demonstration as run_Trendy() can be time-consuming.

set.seed(1234)
random_features <- sample(rownames(data_obj_merged_imp_list[[1]]), 50)

example_res_list <- run_Trendy(data_obj_merged_imp_list,
    feature = random_features,
    minExp = 0.5,
    maxK = 1,
    minNumInSeg = 2, meanCut = 0, NCores = 2
)
#> Max number of breakpoints: 1
#> Min mean expression: 0
#> Min number of samples in each segment: 2
#> Running Trendy for group: IFNbeta
#> Using 50 specified features present in the data.
#> breakpoint estimate(s): 9.519174 
#> breakpoint estimate(s): 8.000004
#> breakpoint estimate(s): 8.885903 
#> breakpoint estimate(s): 8.000128
#> Running Trendy for group: IFNgamma
#> Using 50 specified features present in the data.
#> breakpoint estimate(s): 8.885903 
#> breakpoint estimate(s): 9.519174 
#> breakpoint estimate(s): 9.519174 
#> breakpoint estimate(s): 9.519174
#> breakpoint estimate(s): 9.519174
#> Running Trendy for group: LPS
#> Using 50 specified features present in the data.
#> breakpoint estimate(s): 9.519174 
#> breakpoint estimate(s): 8.885903
#> Running Trendy for group: untreated
#> Trendy analysis is not performed for group untreated: number of time points (3) less than required ((maxK + 1) * minNumInSeg = 4

Use plot_segments()to plot the fitted segments and breakpoints for selected features.

plot_segments(data_obj_merged_imp_list,
    example_res_list,
    feature = c("Slc25a51", "Aunip"), # example features
    ylab = "Log2(CPM + 1)"
)
#> Plotting segmented regression for group: IFNbeta

#> Plotting segmented regression for group: IFNgamma

#> Plotting segmented regression for group: LPS

Plot the distribution of breakpoints across time points with plot_breakpoints() in each group, and summarise the temporal patterns (combination of trends of each segment) of features with summarise_Trendy() and extract_segment_trends().

plot_breakpoints(example_res_list)
#> Warning in (function (..., deparse.level = 1) : number of columns of result is
#> not a multiple of vector length (arg 13)
#> Warning in (function (..., deparse.level = 1) : number of columns of result is
#> not a multiple of vector length (arg 7)
#> Warning in (function (..., deparse.level = 1) : number of columns of result is
#> not a multiple of vector length (arg 8)

trendy_summary <- summarise_Trendy(example_res_list)
#> Warning in (function (..., deparse.level = 1) : number of columns of result is
#> not a multiple of vector length (arg 13)
#> Warning in (function (..., deparse.level = 1) : number of columns of result is
#> not a multiple of vector length (arg 7)
#> Warning in (function (..., deparse.level = 1) : number of columns of result is
#> not a multiple of vector length (arg 8)
trendy_summary |> utils::head()

trendy_list <- extract_segment_trends(trendy_summary)
trendy_list$IFNbeta

#> $up_down
#> [1] "Epsti1" "Mgst2" 
#> 
#> $down_up
#> [1] "Clcn4"    "Slc25a51" "Sigmar1"  "Egln1"    "Cebpz"    "Gga1"     "P2ry6"   
#> [8] "Trub1"   
#> 
#> $down_down
#> [1] "Aunip"
#> 
#> $down_stable
#> [1] "Gm18169" "Lrp5"    "Esco1"  
#> 
#> $up
#> [1] "Gata3un"       "Gm9979"        "Dmrta1"        "1700018A04Rik"
#> [5] "Cldn23"       
#> 
#> $stable_stable
#> [1] "Luc7l2" "Stx16"  "Sbno2"  "Eif2s1" "Smad7" 
#> 
#> $stable_up
#> [1] "Memo1"   "Slc35b3"
#> 
#> $down
#> [1] "Cenpl" "H2ac7"

0.4.10 Variance decomposition

Variance of each feature is decomposed by linear mixed models (LMM) into contributions from Group, Subject (when present), Time, Residual, and optionally Group:Time (or Subject:Time) interaction, which captures group-specific temporal patterns. This helps to identify time-dependent features, group-dependent features, and “noisy” features with high residual variance.

Interpreting the components:

• High Time: time-dependent features, dynamic along time course consistently across groups. Good candidates for run_Trendy() or other time-focused analysis.

• High Group: group-dependent features, stable along time but differentially expressed between groups. May be baseline biological markers.

• High Subject (when present): features with baseline differences between individuals, biologically meaningful in patient studies.

• High Residual: unexplained variation, consider excluding for downstream analysis.

The function decomp_variance() is inspired by PALMO::lmeVariance()(Vasaikar et al. 2023). Group and Time are always included (auto-skipped if only 1 group or time point present). Subject is included when present in colData. Set interaction = TRUE to add Group:Time (or Subject:Time with Subject) as a variance component capturing group-specific temporal patterns. Sufficient replicates per combination are required for stable estimates.

# filter genes for variance decomposition:
# at least 50% values > 0 in at least 2 groups
decomp_filter_genes <- group_specific_features(property_tb,
    filter_ratio = 0.5,
    group_pct = 2 / 4,
    GO = FALSE, genename = FALSE
)$features
#> Filtering criteria: >=50% values >0 in >=2 of groups: IFNbeta, IFNgamma, LPS, untreated

var_decomp <- decomp_variance(data_obj,
    features = decomp_filter_genes,
    assay = 1, core = 2
)
#> LMM: exp ~ (1|Group) + (1|Time)  |  Output: Group, Time, Residual

plot_variance(var_decomp, rank = "Time", top_n = 20)
#> Features not specified. Plotting top 20 features ranked by Time.

plot_variance(var_decomp, rank = "Group", top_n = 20)
#> Features not specified. Plotting top 20 features ranked by Group.

0.4.11 WGCNA (co-expression modules)

Detailed tutorials of weighted gene correlation network analysis (WGCNA) (Langfelder and Horvath 2008, 2012) can be found online. The following is a brief demonstration of how to prepare input and run WGCNA with TiDEomics functions.

Filtering strategies:

• Features with too many missing values will be automatically removed with WGCNA::goodGenes() included in the prepare_WGCNA() function, and can also be pre-filtered with the calculated feature property (output of calc_feature_property() and summarise_feature_property()).

• Residual variance can be used to exclude noisy features.

• It is not recommended to filter by differential expression before WGCNA, see WGCNA FAQ.

# Example filtering by residual variance < Q3
var_res_q3 <- stats::quantile(var_decomp$Residual, 0.75, na.rm = TRUE)
filter_wgcna <- var_decomp |> dplyr::filter(Residual < var_res_q3) |>
    dplyr::pull(Feature)

data_obj_wgcna <- data_obj[filter_wgcna, ]

Prepare data format and choose power:

prepare_WGCNA() prepares the input for WGCNA and helps to choose the soft-thresholding power. Check the scale-free topology fit indices to confirm that the chosen power is appropriate.

Reasonable powers are less than 15 for unsigned or signed hybrid networks, and less than 30 for signed networks, to reach scale-free topology fit index > 0.8, and mean connectivity (mean.k) not too high (in the hundreds or above). See WGCNA FAQ for details.

wgcna_input <- prepare_WGCNA(data_obj_wgcna, assay = 2,
    powers = seq(1, 20),
    networkType = "signed", RsquaredCut = 0.8
)

#> Allowing multi-threading with up to 48 threads.
#> pickSoftThreshold: will use block size 255.
#>  pickSoftThreshold: calculating connectivity for given powers...
#>    ..working on genes 1 through 255 of 255
#>    Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
#> 1      1    0.583  3.260          0.516  142.00    149.00 169.00
#> 2      2    0.121  0.579         -0.114   89.10     94.10 126.00
#> 3      3    0.040 -0.272         -0.189   60.20     63.30  98.50
#> 4      4    0.200 -0.514          0.161   42.70     44.40  79.30
#> 5      5    0.502 -0.750          0.746   31.40     31.60  65.10
#> 6      6    0.547 -0.765          0.853   23.60     23.50  54.10
#> 7      7    0.654 -0.834          0.958   18.20     17.10  45.40
#> 8      8    0.673 -0.904          0.904   14.20     13.00  38.50
#> 9      9    0.708 -0.993          0.930   11.30     10.10  33.00
#> 10    10    0.757 -1.030          0.958    9.07      8.04  28.40
#> 11    11    0.753 -1.070          0.965    7.37      6.43  24.60
#> 12    12    0.765 -1.120          0.930    6.04      5.10  21.50
#> 13    13    0.794 -1.180          0.951    5.00      4.19  18.80
#> 14    14    0.751 -1.260          0.915    4.17      3.29  16.60
#> 15    15    0.772 -1.290          0.915    3.50      2.66  14.70
#> 16    16    0.794 -1.310          0.919    2.96      2.18  13.10
#> 17    17    0.819 -1.310          0.939    2.52      1.77  11.70
#> 18    18    0.839 -1.340          0.934    2.16      1.46  10.50
#> 19    19    0.858 -1.370          0.949    1.86      1.22   9.42
#> 20    20    0.873 -1.380          0.961    1.61      1.05   8.51
wgcna_input$plot

picked_power <- wgcna_input$powerEstimate
picked_power

#> [1] 17

Run WGCNA:

Use run_WGCNA() with the output of prepare_WGCNA(), and visualise the resulting modules with plot_WGCNA().

run_WGCNA() is a wrapper of WGCNA::blockwiseModules(), which runs the WGCNA analysis and returns the module assignment for each feature. Parameters for WGCNA::blockwiseModules() can be set with run_WGCNA(), e.g., corType for correlation method.

net <- run_WGCNA(wgcna_input,
    power = picked_power,
    # corType = "pearson", # other option is "bicor"
    numericLabels = TRUE
)

#> Allowing multi-threading with up to 48 threads.

plot_WGCNA(net, fontsize = 8)

#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter
#> Warning in par(usr): argument 1 does not name a graphical parameter

#> Warning in par(usr): argument 1 does not name a graphical parameter

Extract modules: show module sizes

gene_module <- WGCNA_module(net, exclude_grey = TRUE)

gene_module |>
    dplyr::group_by(Module) |>
    dplyr::summarise(n = dplyr::n())

Module metrics (size, mean kME, etc.) can be summarised with summarise_module_metrics():

summarise_module_metrics(net)

Plot module profiles:

plot_modules_v(gene_module,
    data_obj_merged, scale = TRUE,
    ylabel = "Z-score of log2(CPM + 1)",
    height_ratio = 2
)
#> Warning: Removed 675 rows containing non-finite outside the scale range
#> (`stat_summary()`).

0.4.12 Functional enrichment

TiDEomics wrap clusterProfiler::enrichGO() and clusterProfiler::gseGO()(Yu 2024; Xu et al. 2024; Wu et al. 2021; Yu et al. 2012) to run GO enrichment efficiently on gene sets and ranked gene list.

For ranked gene list (e.g., ranked by Time or Group contribution from variance decomposition), use enrichGO_rank() and plot with enrichplot::gseaplot2().

gse_group <- enrichGO_rank(var_decomp,
    gene_rank_by = "Group",
    OrgDb = org.Mm.eg.db,
    keyType = "SYMBOL", category = "BP",
    go_rank_by = "p.adjust")
#> Warning in gsea(geneList = geneList, gene_sets = geneSets, weight = weight, :
#> There were 3643 pathways for which P-values were not calculated properly due to
#> unbalanced gene-level statistic values. For such pathways pvalue, NES and
#> log2err are set to NA. You can try to increase nPermSimple.
#> Warning in calculate_qvalue(gsea_res$pvalue): Invalid p-values detected (NA,
#> non-finite, <0, or >1). qvalue will be computed on valid p-values only.
#> Warning in enrichit::gsea_gson(geneList = geneList, exponent = exponent, : NA
#> values detected in gene set IDs. Replacing with string 'NA'.
#> Warning in enrichit::gsea_gson(geneList = geneList, exponent = exponent, :
#> Duplicate gene set IDs detected: NA... (Total 1). Unique suffixes added.
#> Removing NA ID gene sets for BP.

enrichplot::gseaplot2(gse_group[["BP"]], geneSetID = 1:3, base_size = 8)

For multiple defined gene sets (e.g. WGCNA modules, DE genes by feature classification), use enrichGO_list() and plot with plot_GO().

Optionally, use simplify = TRUE with enrichGO_list() to simplify the GO results by removing redundant terms (default: FALSE).

background_wgcna <- colnames(wgcna_input$data)

go_list <- enrichGO_list(
    gene_list = gene_module, OrgDb = org.Mm.eg.db,
    universe = background_wgcna,
    pvalueCutoff = 0.9, # get more results for demonstration
    qvalueCutoff = 0.9,
    simplify = FALSE,
    keyType = "SYMBOL"
)
#> GO category not specified. Using all three: BP, MF, CC.
#> Performing GO enrichment for category: BP
#> Processing gene list: 1
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Processing gene list: 2
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Processing gene list: 3
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Processing gene list: 4
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Processing gene list: 5
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Performing GO enrichment for category: MF
#> Processing gene list: 1
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Processing gene list: 2
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Processing gene list: 3
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Processing gene list: 4
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Processing gene list: 5
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Performing GO enrichment for category: CC
#> Processing gene list: 1
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Processing gene list: 2
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Processing gene list: 3
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Processing gene list: 4
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Processing gene list: 5
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Merging GO enrichment results across gene lists for each category.

plot_GO(go_list$all, plot_dotplot = TRUE,
    plot_emapplot = FALSE,
    plot_cnetplot = FALSE,
    showCategory_dotplot = 3)

#> $dotplot_BP

#> 
#> $dotplot_MF

#> 
#> $dotplot_CC

Enrichment can also be performed against the Molecular Signatures Database (MSigDB) via enrich_msigdb(), which queries the gene sets with msigdbr::msigdbr() and runs a hypergeometric test across user-selected collections (Hallmark, GO, etc.) or specific gene sets.

# Mouse Hallmark gene sets
hallmark_msigdb <- enrich_msigdb(gene_module, universe = background_wgcna,
    minGSSize = 5, category = "MH", species = "Mus musculus", db_species = "MM")
#> Processing gene list: 1
#> Processing gene list: 2
#> Processing gene list: 3
#> Processing gene list: 4
#> Processing gene list: 5

# Human C2 (chemical and genetic perturbations)
perturb_msigdb <- enrich_msigdb(gene_module, universe = background_wgcna,
    category = "C2", subcategory = "CGP", species = "Mus musculus")
#> Using human MSigDB with ortholog mapping to mouse. Use `db_species = "MM"` for mouse-native gene sets.
#> This message is displayed once per session.
#> Processing gene list: 1
#> Processing gene list: 2
#> Processing gene list: 3
#> Processing gene list: 4
#> Processing gene list: 5

# Specific gene sets (category not required)
ifn_msigdb <- enrich_msigdb(gene_module, universe = background_wgcna,
    gene_sets = c("HALLMARK_INTERFERON_ALPHA_RESPONSE",
        "HALLMARK_INTERFERON_GAMMA_RESPONSE"),
    minGSSize = 5, species = "Mus musculus", db_species = "MM")
#> Using 2 gene set(s).
#> Processing gene list: 1
#> Processing gene list: 2
#> Processing gene list: 3
#> Processing gene list: 4
#> Processing gene list: 5

Enrichment of other gene sets (e.g., drug signatures, TF targets, pathways) can be performed with enrichR_list(), which wraps enrichR::enrichr(). Available databases can be checked with enrichR::listEnrichrDbs().

enrichGO_list(), enrich_msigdb() and enrichR_list() all return a named list of data.frames (one per category for enrichGO_list, one for enrich_msigdb(), one per database for enrichR_list()) with Cluster and Description columns, compatible with plot_modules_h().

0.4.13 Integrated visualisation of WGCNA modules and functional enrichment

Enriched terms can be added to the module profile plot with plot_modules_h(), which is similar to plot_modules_v() above but horizontally aligned with annotation of the modules with their top enriched GO terms. The plotting function is adapted from ClusterGVis package (Zhang et al. 2026).

plot_modules_h(gene_module,
    data_obj_merged, scale = TRUE,
    ylabel = "Z-score of log2(CPM + 1)",
    enrich_list = go_list$all,
    enrich_category = "BP",
    heatmap_width = 6,
    heatmap_height = 8
)
#> Warning: Removed 294 rows containing non-finite outside the scale range
#> (`stat_summary()`).
#> Warning: Removed 114 rows containing non-finite outside the scale range
#> (`stat_summary()`).
#> Warning: Removed 96 rows containing non-finite outside the scale range
#> (`stat_summary()`).
#> Warning: Removed 93 rows containing non-finite outside the scale range
#> (`stat_summary()`).
#> Warning: Removed 78 rows containing non-finite outside the scale range
#> (`stat_summary()`).

Multiple enrichment categories (e.g. GO BP, CC, MF, MSigDB collections, enrichR databases) can be displayed side-by-side by passing a vector to enrich_category. Features of interest can be marked on the heatmap or shown as an annotation column via mark_features and enrich_category = "Hub features". The enrich_p_threshold parameter controls per-category p-value colour-coding of enrichment terms; set to NA for “Hub features”, which use auto-assigned colours instead.

Hub features can be extracted by WGCNA module membership (the correlation of the feature with the corresponding module eigengene) with extract_hubs()

hub_features <- extract_hubs(net, top_n = 3)

plot_modules_h(gene_module,
    data_obj_merged, scale = TRUE,
    ylabel = "Z-score of log2(CPM + 1)",
    enrich_list = c(go_list$all, hallmark_msigdb),
    enrich_category = c("BP", "CC", "MH"),
    enrich_p_threshold = c(0.05, 0.05, 0.05),
    mark_features = hub_features,
    heatmap_width = 6,
    heatmap_height = 8
)
#> Warning: Removed 294 rows containing non-finite outside the scale range
#> (`stat_summary()`).
#> Warning: Removed 114 rows containing non-finite outside the scale range
#> (`stat_summary()`).
#> Warning: Removed 96 rows containing non-finite outside the scale range
#> (`stat_summary()`).
#> Warning: Removed 93 rows containing non-finite outside the scale range
#> (`stat_summary()`).
#> Warning: Removed 78 rows containing non-finite outside the scale range
#> (`stat_summary()`).

0.4.14 Universal: plot features of interest

Selected features can be plotted with plot_trend(), which shows the mean and standard deviation of replicates at each time point.

plot_trend(data_obj, assay = 1,
    features = c("Abtb1", "Dram1", "Ifi27", "Nufip1"),
    title = "Example features")
#> Group not specified. Plotting all groups: IFNbeta, IFNgamma, LPS, untreated

# Or pre-calculate mean and sd with calc_mean_sd()
table_mean_sd_orig <- calc_mean_sd(data_obj)$orig
plot_trend(table_mean_sd_orig,
    features = c("Abtb1", "Dram1", "Ifi27", "Nufip1"),
    title = "Example features")
#> Group not specified. Plotting all groups: IFNbeta, IFNgamma, LPS, untreated

Visualisation of features with high & low residual variance justify the filtering strategy for WGCNA input.

plot_trend(data_obj, assay = 1,
    features = var_decomp |>
        dplyr::arrange(Residual) |> utils::head(12) |> dplyr::pull(Feature),
    title = "Features with lowest residual variance"
)
#> Group not specified. Plotting all groups: IFNbeta, IFNgamma, LPS, untreated

plot_trend(data_obj, assay = 1,
    features = var_decomp |>
        dplyr::arrange(dplyr::desc(Residual)) |>
        utils::head(12) |> dplyr::pull(Feature),
    title = "Features with highest residual variance"
)
#> Group not specified. Plotting all groups: IFNbeta, IFNgamma, LPS, untreated

0.4.15 One-step preprocessing with prepare_tide()

prepare_tide() is a wrapper function that runs multiple preprocessing steps in a single call, including input creation, normalisation, merging replicates, variance decomposition, and feature filtering. It is convenient for standard analyses, while running each step individually allows for more control over parameters.

tide <- prepare_tide(
    data = tutorial_data,
    sample_ann = tutorial_sample_info,
    filter_ratio = 0.5,
    min_groups = 2,
    keep = "below_quantile",
    residual_threshold = 0.75
)
#> No Subject column specified. Samples treated as independent. For repeated-measures designs, set subject_col to the column identifying biological subjects.
#> Converting 'Group' column to factor. Default order is alphabetical.
#> Converting 'Replicate' column to factor. Default order is numerical.
#> Converting 'Batch' column to factor. Default order is numerical.
#> Normalising to group baseline at each feature's first non-NA time point.
#> Preparing TiDEomics input: 500 features, 40 samples, 4 groups
#> Filtering criteria: >=50% values >0 in >=2 of groups: IFNbeta, IFNgamma, LPS, untreated
#> Cross-group filter (Exp_ratio >= 0.5 in >= 2 groups): kept 340 of 500 features (68%)
#> --- assay: orig ---
#> LMM: exp ~ (1|Group) + (1|Time)  |  Output: Group, Time, Residual
#> Residual filter (below_quantile): kept 255 of 340 features (75%)
#> --- assay: norm ---
#> LMM: exp ~ (1|Group) + (1|Time)  |  Output: Group, Time, Residual
#> Residual filter (below_quantile): kept 255 of 340 features (75%)
tide$filter_summary

Variance decomposition and filtering are run on both "orig" and "norm" assays. The returned list tide contains:

Element	Description	Use for	Step-by-step equivalent in the tutorial
tide$se	Unmerged SE, all features	Correlation matrix, PCA, UMAP, Pairwise DE, plot_trend	data_obj
tide$se_filtered$orig/norm	Unmerged SE, filtered features	WGCNA	data_obj_wgcna
tide$merged_list	Per-group merged SEs, all features	calc_feature_property, run_Trendy	data_obj_merged_list
tide$merged_list_filtered$orig/norm	Per-group merged SEs, filtered features	-	-
tide$merged_se	Single merged SE, all features	plot_modules_v/h	data_obj_merged
tide$merged_se_filtered$orig/norm	Single merged SE, filtered features	plot_modules_v/h	-
tide$variance$orig/norm	Variance decomposition	plot_variance, enrichGO_rank	var_decomp
tide$feature_property	Feature property summary	group_specific_features	property_tb
tide$filter_summary	Per-stage filtering stats	QC	-
tide$group_specific_filter	Features removed by cross-group filter	Review group-specific features	-
tide$DE	DE results placeholder	Centralised storage	DE_between_time/group_out
tide$enrichment	Enrichment results placeholder	Centralised storage	go_list, gse_group, hallmark_msigdb etc.
tide$WGCNA	WGCNA results placeholder	Centralised storage	net

Pairwise DE, WGCNA and enrichment can be run on the returned elements, and the results can be appended to the list for centralised storage.

tide$DE <- list(
    between_group = DE_between_group_out,
    between_time  = DE_between_time_out
)
tide$WGCNA <- net

tide$enrichment$GO_modules <- go_list
tide$enrichment$MSigDB <- hallmark_msigdb
tide$enrichment$GO_group <- gse_group

0.5 Interoperability within Bioconductor ecosystem

TiDEomics uses SummarizedExperiment as the central data container. It keeps the feature x sample matrices, sample annotation, and feature metadata synchronized during normalisation, splitting, and filtering. The use of multiple assays within the same object also allows original (“orig”) and baseline-normalized (“norm”) data to remain linked throughout the workflow.

As SummarizedExperiment is a standard Bioconductor container, TiDEomics data can be passed to other Bioconductor tools for further custom analysis and visualisation. For example, iSEE interactive SummarizedExperiment browser can be used to explore the data in an interactive Shiny app.

iSEE::iSEE(data_obj)

prepare_tide() output can be converted to a DeeDeeExperiment object for formatted summary. The nested DE and enrichment results can be flattened with flatten_DE() or flatten_enrich() before passing to the DeeDeeExperiment constructor.

# Flatten nested results
de_flat <- flatten_DE(tide$DE)
enrich_flat <- flatten_enrich(tide$enrichment)

# Build DeeDeeExperiment
dde <- DeeDeeExperiment::DeeDeeExperiment(
    sce = tide$se,
    de_results = de_flat,
    enrich_results = enrich_flat
)
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_BP_1' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 334 gene sets in `enrichResult` object, of which 0 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_BP_2' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 308 gene sets in `enrichResult` object, of which 194 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_BP_3' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 295 gene sets in `enrichResult` object, of which 7 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_BP_4' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 308 gene sets in `enrichResult` object, of which 50 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_BP_5' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 293 gene sets in `enrichResult` object, of which 0 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_MF_1' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 66 gene sets in `enrichResult` object, of which 6 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_MF_2' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 58 gene sets in `enrichResult` object, of which 0 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_MF_3' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 55 gene sets in `enrichResult` object, of which 0 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_MF_4' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 58 gene sets in `enrichResult` object, of which 56 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_MF_5' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 59 gene sets in `enrichResult` object, of which 52 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_CC_1' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 74 gene sets in `enrichResult` object, of which 21 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_CC_2' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 66 gene sets in `enrichResult` object, of which 3 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_CC_3' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 75 gene sets in `enrichResult` object, of which 7 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_CC_4' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 70 gene sets in `enrichResult` object, of which 66 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_GO_modules_CC_5' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> Found 71 gene sets in `enrichResult` object, of which 14 are significant.
#> Converting for usage within the DeeDeeExperiment framework...
#> Warning in DeeDeeExperiment::DeeDeeExperiment(sce = tide$se, de_results = de_flat, : Could not match FEA 'clusterProfiler_MSigDB_MH' to any DE contrast.
#> Available DE results: between_group_IFNgamma-IFNbeta_T0, between_group_IFNgamma-IFNbeta_T2, between_group_IFNgamma-IFNbeta_T4, between_group_IFNgamma-IFNbeta_T6, between_group_IFNgamma-IFNbeta_T8, between_group_IFNgamma-IFNbeta_T24, between_group_LPS-IFNbeta_T0, between_group_LPS-IFNbeta_T2, between_group_LPS-IFNbeta_T4, between_group_LPS-IFNbeta_T6, between_group_LPS-IFNbeta_T8, between_group_LPS-IFNbeta_T24, between_group_untreated-IFNbeta_T0, between_group_untreated-IFNbeta_T8, between_group_untreated-IFNbeta_T24, between_group_IFNbeta-IFNgamma_T0, between_group_IFNbeta-IFNgamma_T2, between_group_IFNbeta-IFNgamma_T4, between_group_IFNbeta-IFNgamma_T6, between_group_IFNbeta-IFNgamma_T8, between_group_IFNbeta-IFNgamma_T24, between_group_LPS-IFNgamma_T0, between_group_LPS-IFNgamma_T2, between_group_LPS-IFNgamma_T4, between_group_LPS-IFNgamma_T6, between_group_LPS-IFNgamma_T8, between_group_LPS-IFNgamma_T24, between_group_untreated-IFNgamma_T0, between_group_untreated-IFNgamma_T8, between_group_untreated-IFNgamma_T24, between_group_IFNbeta-LPS_T0, between_group_IFNbeta-LPS_T2, between_group_IFNbeta-LPS_T4, between_group_IFNbeta-LPS_T6, between_group_IFNbeta-LPS_T8, between_group_IFNbeta-LPS_T24, between_group_IFNgamma-LPS_T0, between_group_IFNgamma-LPS_T2, between_group_IFNgamma-LPS_T4, between_group_IFNgamma-LPS_T6, between_group_IFNgamma-LPS_T8, between_group_IFNgamma-LPS_T24, between_group_untreated-LPS_T0, between_group_untreated-LPS_T8, between_group_untreated-LPS_T24, between_group_IFNbeta-untreated_T0, between_group_IFNbeta-untreated_T8, between_group_IFNbeta-untreated_T24, between_group_IFNgamma-untreated_T0, between_group_IFNgamma-untreated_T8, between_group_IFNgamma-untreated_T24, between_group_LPS-untreated_T0, between_group_LPS-untreated_T8, between_group_LPS-untreated_T24, between_time_IFNbeta_t2-t0, between_time_IFNbeta_t4-t0, between_time_IFNbeta_t6-t0, between_time_IFNbeta_t8-t0, between_time_IFNbeta_t24-t0, between_time_IFNbeta_t4-t2, between_time_IFNbeta_t6-t2, between_time_IFNbeta_t8-t2, between_time_IFNbeta_t24-t2, between_time_IFNbeta_t6-t4, between_time_IFNbeta_t8-t4, between_time_IFNbeta_t24-t4, between_time_IFNbeta_t8-t6, between_time_IFNbeta_t24-t6, between_time_IFNbeta_t24-t8, between_time_IFNgamma_t2-t0, between_time_IFNgamma_t4-t0, between_time_IFNgamma_t6-t0, between_time_IFNgamma_t8-t0, between_time_IFNgamma_t24-t0, between_time_IFNgamma_t4-t2, between_time_IFNgamma_t6-t2, between_time_IFNgamma_t8-t2, between_time_IFNgamma_t24-t2, between_time_IFNgamma_t6-t4, between_time_IFNgamma_t8-t4, between_time_IFNgamma_t24-t4, between_time_IFNgamma_t8-t6, between_time_IFNgamma_t24-t6, between_time_IFNgamma_t24-t8, between_time_LPS_t2-t0, between_time_LPS_t4-t0, between_time_LPS_t6-t0, between_time_LPS_t8-t0, between_time_LPS_t24-t0, between_time_LPS_t4-t2, between_time_LPS_t6-t2, between_time_LPS_t8-t2, between_time_LPS_t24-t2, between_time_LPS_t6-t4, between_time_LPS_t8-t4, between_time_LPS_t24-t4, between_time_LPS_t8-t6, between_time_LPS_t24-t6, between_time_LPS_t24-t8, between_time_untreated_t8-t0, between_time_untreated_t24-t0, between_time_untreated_t24-t8
#>  Consider naming your enrich_results starting with one of the following prefixes: 'topGO_', 'clusterProfiler_','GeneTonic_', 'DAVID_','gsea_', 'fgsea_', 'enrichr_', 'gPro_',followed by the contrast name
#> ℹ No shaking method available for this functional enrichment results.
#> Returning only the original object.

0.6 Common usage scenarios

TiDEomics supports two experimental designs, auto-detected from the sample annotation:

• Cell culture / independent samples: create_input(data, sample_ann) – each sample is independent. LMM uses (1|Group) + (1|Time). DE uses unpaired designs.
• Patient / repeated-measures: create_input(data, sample_ann, subject_col = "PatientID") – same subjects tracked across time points. LMM adds (1|Subject). DE_between_time() uses paired design via limma::duplicateCorrelation(). normalise_to_start(by_subject = TRUE) subtracts each subject’s own baseline. merge_replicates() and plot_trend() compute statistics across subjects.

Users can ask different biological questions and use different functions in TiDEomics to answer them. For example:

• Which features change over time in each sample group? -> DE_between_time(), calc_feature_property(), run_Trendy().

• Which features differ between groups at the same time point? -> DE_between_group() (For starting time point, use assay = 1; for later time points, use assay = 1 for original input data, or use assay = 2 for change-from-baseline).

• What is the temporal pattern for individual features? -> run_Trendy() + summarise_Trendy() + extract_segment_trends().

• Which features are noisy vs biologically driven? -> decomp_variance() + plot_variance(), noisiness can be represented by Residual variance.

• What co-expression modules are present and what are their temporal profiles? -> prepare_WGCNA() + run_WGCNA() + plot_modules_v() or plot_modules_h().

• What biological processes are associated with the genes of interest (e.g. co-expression modules, differentially expressed genes in each sample group)? -> enrichGO_list() + plot_GO().

• What biological processes are associated with the genes ranked by their property (e.g. time or group contributed variance)? -> enrichGO_rank().

• How to normalise the data? -> normalise_to_start() to focus on changes from baseline; apply global normalisation (quantile, median) and batch correction when appropriate, before create_input().

• How to handle missing values? -> impute_groups() for run_Trendy. plot_pca() and plot_umap() automatically exclude features with missing values. prepare_WGCNA() filter out features with too many missing values.

0.7 Session information

sessionInfo()

#> R version 4.6.1 Patched (2026-06-24 r90190)
#> Platform: x86_64-apple-darwin20
#> Running under: macOS Ventura 13.7.8
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.6-x86_64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.6-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
#> 
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: America/New_York
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] org.Mm.eg.db_3.23.0         AnnotationDbi_1.75.2       
#>  [3] SummarizedExperiment_1.43.0 Biobase_2.73.1             
#>  [5] GenomicRanges_1.65.1        Seqinfo_1.3.0              
#>  [7] IRanges_2.47.2              S4Vectors_0.51.5           
#>  [9] BiocGenerics_0.59.10        generics_0.1.4             
#> [11] MatrixGenerics_1.25.0       matrixStats_1.5.0          
#> [13] TiDEomics_0.99.4            BiocStyle_2.41.0           
#> 
#> loaded via a namespace (and not attached):
#>   [1] segmented_2.2-1             fs_2.1.0                   
#>   [3] bitops_1.0-9                enrichplot_1.33.0          
#>   [5] httr_1.4.8                  RColorBrewer_1.1-3         
#>   [7] doParallel_1.0.17           ggsci_5.1.0                
#>   [9] DeeDeeExperiment_1.3.0      dynamicTreeCut_1.63-1      
#>  [11] tools_4.6.1                 backports_1.5.1            
#>  [13] R6_2.6.1                    lazyeval_0.2.3             
#>  [15] GetoptLong_1.1.1            withr_3.0.3                
#>  [17] gridExtra_2.3.1             preprocessCore_1.75.0      
#>  [19] WGCNA_1.74                  cli_3.6.6                  
#>  [21] Cairo_1.7-0                 scatterpie_0.2.6           
#>  [23] labeling_0.4.3              sass_0.4.10                
#>  [25] S7_0.2.2                    askpass_1.2.1              
#>  [27] pbapply_1.7-4               ggridges_0.5.7             
#>  [29] systemfonts_1.3.2           yulab.utils_0.2.4          
#>  [31] gson_0.2.0                  foreign_0.8-91             
#>  [33] DOSE_4.7.2                  dichromat_2.0-1            
#>  [35] limma_3.69.2                rstudioapi_0.19.0          
#>  [37] impute_1.87.0               RSQLite_3.53.3             
#>  [39] gridGraphics_0.5-1          shape_1.4.6.1              
#>  [41] crosstalk_1.2.2             gtools_3.9.5               
#>  [43] car_3.1-5                   dplyr_1.2.1                
#>  [45] GO.db_3.23.1                Matrix_1.7-6               
#>  [47] abind_1.4-8                 PCAtools_2.25.0            
#>  [49] lifecycle_1.0.5             edgeR_4.11.4               
#>  [51] yaml_2.3.12                 carData_3.0-6              
#>  [53] gplots_3.3.0                qvalue_2.45.0              
#>  [55] SparseArray_1.13.2          grid_4.6.1                 
#>  [57] blob_1.3.0                  promises_1.5.0             
#>  [59] dqrng_0.4.1                 crayon_1.5.3               
#>  [61] ggtangle_0.1.2              lattice_0.22-9             
#>  [63] msigdbr_26.1.0              beachmat_2.29.0            
#>  [65] cowplot_1.2.0               KEGGREST_1.53.6            
#>  [67] magick_2.9.1                pillar_1.11.1              
#>  [69] knitr_1.51                  ComplexHeatmap_2.29.0      
#>  [71] rjson_0.2.23                boot_1.3-32                
#>  [73] codetools_0.2-20            glue_1.8.1                 
#>  [75] ggiraph_0.9.6               ggfun_0.2.1                
#>  [77] fontLiberation_0.1.0        data.table_1.18.4          
#>  [79] vctrs_0.7.3                 png_0.1-9                  
#>  [81] treeio_1.37.0               Rdpack_2.6.6               
#>  [83] gtable_0.3.6                assertthat_0.2.1           
#>  [85] cachem_1.1.0                xfun_0.60                  
#>  [87] rbibutils_2.4.1             S4Arrays_1.13.0            
#>  [89] mime_0.13                   reformulas_0.4.4           
#>  [91] survival_3.8-9              aisdk_1.4.12               
#>  [93] SingleCellExperiment_1.35.2 iterators_1.0.14           
#>  [95] statmod_1.5.2               nlme_3.1-170               
#>  [97] ggtree_4.3.0                bit64_4.8.2                
#>  [99] fontquiver_0.2.1            bslib_0.11.0               
#> [101] irlba_2.3.7                 KernSmooth_2.23-26         
#> [103] otel_0.2.0                  rpart_4.1.27               
#> [105] colorspace_2.1-3            DBI_1.3.0                  
#> [107] Hmisc_5.2-6                 nnet_7.3-20                
#> [109] DESeq2_1.53.2               tidyselect_1.2.1           
#> [111] processx_3.9.0              curl_7.1.0                 
#> [113] bit_4.6.0                   compiler_4.6.1             
#> [115] httr2_1.3.0                 htmlTable_2.5.0            
#> [117] plotly_4.12.1               randtests_1.0.2            
#> [119] fontBitstreamVera_0.1.1     DelayedArray_0.39.3        
#> [121] bookdown_0.47               checkmate_2.3.4            
#> [123] scales_1.4.0                caTools_1.18.4             
#> [125] callr_3.8.0                 rappdirs_0.3.4             
#> [127] stringr_1.6.0               digest_0.6.39              
#> [129] minqa_1.2.8                 rmarkdown_2.31             
#> [131] XVector_0.53.0              htmltools_0.5.9            
#> [133] pkgconfig_2.0.3             base64enc_0.1-6            
#> [135] umap_0.2.10.0               lme4_2.0-6                 
#> [137] sparseMatrixStats_1.25.0    fastmap_1.2.0              
#> [139] rlang_1.3.0                 GlobalOptions_0.1.4        
#> [141] htmlwidgets_1.6.4           shiny_1.14.0               
#> [143] DelayedMatrixStats_1.35.0   ggh4x_0.3.1                
#> [145] farver_2.1.2                jquerylib_0.1.4            
#> [147] jsonlite_2.0.0              BiocParallel_1.47.0        
#> [149] GOSemSim_2.39.2             BiocSingular_1.29.0        
#> [151] magrittr_2.0.5              Formula_1.2-5              
#> [153] ggplotify_0.1.3             patchwork_1.3.2            
#> [155] Rcpp_1.1.2                  babelgene_22.9             
#> [157] reticulate_1.46.0           ape_5.8-1                  
#> [159] ggnewscale_0.5.2            gdtools_0.5.1              
#> [161] stringi_1.8.7               MASS_7.3-66                
#> [163] plyr_1.8.9                  shinyFiles_0.9.3           
#> [165] parallel_4.6.1              ggrepel_0.9.8              
#> [167] Biostrings_2.81.5           splines_4.6.1              
#> [169] circlize_0.4.18             locfit_1.5-9.12            
#> [171] igraph_2.3.3                ggpubr_1.0.0               
#> [173] fastcluster_1.3.0           ggsignif_0.6.4             
#> [175] enrichit_0.2.0              reshape2_1.4.5             
#> [177] ScaledMatrix_1.21.0         evaluate_1.0.5             
#> [179] BiocManager_1.30.27         nloptr_2.2.1               
#> [181] foreach_1.5.2               tweenr_2.0.3               
#> [183] httpuv_1.6.17               openssl_2.4.2              
#> [185] tidyr_1.3.2                 purrr_1.2.2                
#> [187] polyclip_1.10-7             clue_0.3-68                
#> [189] ggplot2_4.0.3               Trendy_1.35.0              
#> [191] BiocBaseUtils_1.15.1        ggforce_0.5.0              
#> [193] rsvd_1.0.5                  broom_1.0.13               
#> [195] xtable_1.8-8                RSpectra_0.16-2            
#> [197] tidytree_0.4.8              tidydr_0.0.6               
#> [199] rstatix_1.1.0               later_1.4.8                
#> [201] viridisLite_0.4.3           tibble_3.3.1               
#> [203] clusterProfiler_4.21.1      aplot_0.3.1                
#> [205] memoise_2.0.1               writexl_1.5.4              
#> [207] cluster_2.1.8.2

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