Introduction to SETA ecological transforms and sample-level latent spaces
Kyle Kimler
Cell Discovery Networkkkimler@broadinstitute.org
Marc Elosua-Bayes
Cell Discovery Networkmarc.elosuabayes@childrens.harvard.edu
23 October 2025
Introduction
Why use SETA?
SETA (Single Cell Ecological Taxonomic Analysis) performs compositional analysis of single-cell data by combining established compositional data analysis (CoDA) methods with ecological principles. While other excellent Bioconductor packages address cell-type compositions, SETA is uniquely designed to teach and apply a variety of CoDA methods.
Compositional Analysis of Single-Cell RNA-seq Data
Single-cell RNA-seq experiments produce data where cell-type abundances are inherently compositional - the proportions of different cell types sum to 1 (or 100%). This compositional constraint means that changes in one cell type affect all others, and standard statistical methods can produce misleading results. SETA applies well-established compositional data analysis (CoDA) methods from ecology to properly handle these constraints.
What SETA Does Well
Several Bioconductor packages already address single-cell compositional analysis, each with strong statistical background, including DCATS, speckle, muscat and sccomp. While these packages, especially sccomp, provides the statistical rigor for complex analyses, SETA aims to open up the entire compositional analysis workflow to users who want to understand and apply these methods step-by-step. SETA focuses on simpler models that respect the compositional constraint.
SETA’s unique strengths: - Explicit distance calculations: Aitchison distances and compositional geometry - Ecological metrics: Balance transforms, diversity indices, and ecological framing - Comprehensive transforms: CLR, ALR, ILR, and user-defined balance transforms - Educational focus: separating each step of the workflow into separate functions, SETA aims to make the workflow transparent and easier to explain.
Why These Steps Should Be Executed
The workflow presented in this vignette is essential because:
- Proper Data Handling: Cell-type counts must be treated as compositional data to avoid spurious correlations and misleading statistical results
- Appropriate Transforms: Log-ratio transforms (CLR, ALR, ILR) maintain compositional properties while enabling standard statistical analysis
- Ecological Insights: Applying ecological methods provides novel perspectives on cell-type community structure and diversity
- Educational Value:
SETAopens up the entire compositional analysis workflow to users, teaching the mathematical foundations rather than hiding them in black-box functions - Unique Capabilities:
SETAprovides explicit distance calculations, ecological metrics, and comprehensive compositional transforms in a single, educational framework
Package Overview
SETA provides a set of functions for compositional analysis of single-cell RNA-seq data.
In this vignette we show how to:
- Extract a taxonomic counts matrix from various objects (e.g. a
Seuratobject) - Apply compositional transforms such as
CLRandALR - Compute a latent space using
PCA(with options forPCoAorNMDS)
This example works with the Tabula Muris Senis lung dataset which can be downloaded directly with bioconductor It contains lung single-cell profiles from 14 donor mice (mouse_id). Each mouse is assigned to one of five age groups, from young adult to old, recorded in the age column. Standard cell-type labels (free_annotation) and additional metadata (e.g. sex) are also included.
This vignette is modular; users can update or replace the dataset-specific settings (e.g., metadata column names, reference cell type) as appropriate for their own data.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SETA")Loading the Data
library(SingleCellExperiment)
library(SETA)
library(ggplot2)
library(dplyr)
library(TabulaMurisSenisData)
library(reshape2)
# library(tidytext)Load and prepare data
sce <- TabulaMurisSenisDroplet(tissues = "Lung")$Lung
# Two samples in this dataset have had certain celltype lineages depleted.
# For compositional comparisons, we remove these for simplicity.
sce <- sce[, colData(sce)$subtissue != "immune-endo-depleted"]Quick data exploration:
table(sce$free_annotation, sce$age)##
## 1m 3m 18m 21m 30m
## Adventitial Fibroblast 29 17 56 43 23
## Airway Smooth Muscle 2 0 10 0 0
## Alveolar Epithelial Type 2 7 2 35 18 11
## Alveolar Fibroblast 87 37 166 101 26
## Alveolar Macrophage 517 116 193 91 276
## Artery 13 16 32 26 11
## B 98 135 459 198 180
## Basophil 9 8 19 46 40
## CD4+ T 143 122 72 107 106
## CD8+ T 38 70 234 335 193
## Capillary 194 119 617 522 343
## Capillary Aerocyte 125 35 129 148 93
## Ccr7+ Dendritic 2 2 2 5 7
## Ciliated 10 0 9 4 3
## Classical Monocyte 485 111 968 242 3615
## Club 2 0 4 5 3
## Intermediate Monocyte 111 29 95 19 1495
## Interstitial Macrophage 7 13 16 21 1098
## Ly6g5b+ T 0 0 53 65 42
## Lympatic 6 2 11 12 10
## Myeloid Dendritic Type 1 38 20 31 26 44
## Myeloid Dendritic Type 2 8 10 17 16 26
## Myofibroblast 23 13 9 9 0
## Natural Killer 140 201 153 224 120
## Natural Killer T 20 7 152 50 191
## Neuroendocrine 0 0 0 0 0
## Neutrophil 64 22 413 29 22
## Nonclassical Monocyte 157 160 187 194 287
## Pericyte 11 7 8 1 8
## Plasma 3 1 5 16 23
## Plasmacytoid Dendritic 21 8 15 10 14
## Proliferating Alveolar Macrophage 60 9 7 0 14
## Proliferating Classical Monocyte 1 0 13 0 2473
## Proliferating Dendritic 4 3 8 4 8
## Proliferating NK 5 2 5 3 9
## Proliferating T 15 9 11 18 36
## Regulatory T 8 11 28 20 55
## Vein 19 28 93 107 73
## Zbtb32+ B 26 18 41 216 115table(sce$free_annotation, sce$sex)##
## female male
## Adventitial Fibroblast 85 83
## Airway Smooth Muscle 9 3
## Alveolar Epithelial Type 2 24 49
## Alveolar Fibroblast 217 200
## Alveolar Macrophage 310 883
## Artery 48 50
## B 583 487
## Basophil 57 65
## CD4+ T 267 283
## CD8+ T 474 396
## Capillary 851 944
## Capillary Aerocyte 204 326
## Ccr7+ Dendritic 7 11
## Ciliated 7 19
## Classical Monocyte 514 4907
## Club 6 8
## Intermediate Monocyte 90 1659
## Interstitial Macrophage 34 1121
## Ly6g5b+ T 95 65
## Lympatic 16 25
## Myeloid Dendritic Type 1 61 98
## Myeloid Dendritic Type 2 33 44
## Myofibroblast 25 29
## Natural Killer 506 332
## Natural Killer T 165 255
## Neuroendocrine 0 0
## Neutrophil 61 489
## Nonclassical Monocyte 457 528
## Pericyte 12 23
## Plasma 20 28
## Plasmacytoid Dendritic 23 45
## Proliferating Alveolar Macrophage 12 78
## Proliferating Classical Monocyte 1 2486
## Proliferating Dendritic 10 17
## Proliferating NK 6 18
## Proliferating T 33 56
## Regulatory T 57 65
## Vein 168 152
## Zbtb32+ B 256 160# Set up a color palette for plots
# 3-group distinct categoricals
age_palette <- c(
"1m" = "#90EE90",
"3m" = "#4CBB17",
"18m" = "#228B22",
"21m" = "#355E3B",
"30m" = "#023020")Extracting the Taxonomic Counts Matrix
The first step in compositional analysis is to extract a cell-type counts matrix from single-cell objects. The setaCounts() function converts cell-level metadata into a sample-by-cell-type count matrix, where each row represents a sample and each column represents a cell type. This transformation is crucial because compositional analysis requires sample-level data, not individual cell data.
For this dataset, we want the cell-type annotations to come from the free_annotation column and the sample identifiers from mouse.id. The function aggregates individual cells by sample and cell type, creating a counts matrix suitable for compositional analysis. If necessary, we reassign these columns before extraction to match the expected column names.
# Extract the taxonomic counts matrix using custom metadata column names
# (Users can specify these column names according to their dataset.)
df <- data.frame(colData(sce))
# Fix special characters in mouse.id before working with the data
df$mouse.id <- gsub("/", "", df$mouse.id)
taxa_counts <- setaCounts(
df,
cell_type_col = "free_annotation",
sample_col = "mouse.id",
bc_col = "cell")
head(taxa_counts)##
## Adventitial Fibroblast Airway Smooth Muscle
## 1-M-62 20 1
## 1-M-63 9 1
## 18-F-50 2 1
## 18-F-51 23 8
## 18-M-52 16 1
## 18-M-53 15 0
##
## Alveolar Epithelial Type 2 Alveolar Fibroblast Alveolar Macrophage
## 1-M-62 7 35 148
## 1-M-63 0 52 369
## 18-F-50 0 4 15
## 18-F-51 4 75 88
## 18-M-52 20 36 49
## 18-M-53 11 51 41
##
## Artery B Basophil CD4+ T CD8+ T Capillary Capillary Aerocyte
## 1-M-62 5 72 6 48 16 119 90
## 1-M-63 8 26 3 95 22 75 35
## 18-F-50 1 83 2 5 12 38 7
## 18-F-51 5 167 1 33 57 172 14
## 18-M-52 12 100 10 11 79 229 59
## 18-M-53 14 109 6 23 86 178 49
##
## Ccr7+ Dendritic Ciliated Classical Monocyte Club
## 1-M-62 1 5 201 1
## 1-M-63 1 5 284 1
## 18-F-50 0 0 39 1
## 18-F-51 0 3 122 0
## 18-M-52 1 2 462 1
## 18-M-53 1 4 345 2
##
## Intermediate Monocyte Interstitial Macrophage Ly6g5b+ T Lympatic
## 1-M-62 50 3 0 2
## 1-M-63 61 4 0 4
## 18-F-50 5 0 7 0
## 18-F-51 37 0 23 2
## 18-M-52 23 6 15 6
## 18-M-53 30 10 8 3
##
## Myeloid Dendritic Type 1 Myeloid Dendritic Type 2 Myofibroblast
## 1-M-62 17 4 10
## 1-M-63 21 4 13
## 18-F-50 4 1 1
## 18-F-51 11 6 2
## 18-M-52 3 7 2
## 18-M-53 13 3 4
##
## Natural Killer Natural Killer T Neutrophil Nonclassical Monocyte
## 1-M-62 53 10 13 80
## 1-M-63 87 10 51 77
## 18-F-50 27 11 3 41
## 18-F-51 54 97 7 62
## 18-M-52 34 3 307 45
## 18-M-53 38 41 96 39
##
## Pericyte Plasma Plasmacytoid Dendritic
## 1-M-62 8 1 6
## 1-M-63 3 2 15
## 18-F-50 2 0 0
## 18-F-51 2 3 5
## 18-M-52 3 0 3
## 18-M-53 1 2 7
##
## Proliferating Alveolar Macrophage Proliferating Classical Monocyte
## 1-M-62 10 1
## 1-M-63 50 0
## 18-F-50 0 0
## 18-F-51 3 1
## 18-M-52 1 12
## 18-M-53 3 0
##
## Proliferating Dendritic Proliferating NK Proliferating T Regulatory T
## 1-M-62 2 1 4 1
## 1-M-63 2 4 11 7
## 18-F-50 1 0 2 4
## 18-F-51 2 1 4 22
## 18-M-52 1 1 1 1
## 18-M-53 4 3 4 1
##
## Vein Zbtb32+ B
## 1-M-62 11 16
## 1-M-63 8 10
## 18-F-50 3 9
## 18-F-51 30 13
## 18-M-52 30 11
## 18-M-53 30 8Applying Compositional Transforms
Once we have our cell-type counts matrix, we must apply appropriate compositional transforms before statistical analysis. Raw count data cannot be used directly because it violates the compositional constraint (proportions must sum to 1). Log-ratio transforms solve this problem by converting the data to a space where standard statistical methods can be applied.
Below we demonstrate four different compositional transforms, each with specific advantages:
- Centered Log-Ratio (CLR): Centers log-transformed data around the geometric mean, useful when no reference cell type is available
- Additive Log-Ratio (ALR): Uses a specific cell type as reference, appropriate when one cell type remains stable across conditions
- Isometric Log-Ratio (ILR): Projects data onto orthonormal basis, reducing dimensionality by one while maintaining all information
- Simple transforms: Percentages and log-CPM for comparison and visualization
CLR Transformation
# CLR Transformation with a pseudocount of 1 (default)
clr_out <- setaCLR(taxa_counts, pseudocount = 1)
print(clr_out$counts[1:5, 1:5])##
## Adventitial Fibroblast Airway Smooth Muscle
## 1-M-62 0.6493376 -1.7020377
## 1-M-63 -0.2008121 -1.8102500
## 18-F-50 -0.2928298 -0.6982949
## 18-F-51 0.7495280 -0.2313013
## 18-M-52 0.4036570 -1.7364092
##
## Alveolar Epithelial Type 2 Alveolar Fibroblast Alveolar Macrophage
## 1-M-62 -0.3157433 1.1883341 2.6087615
## 1-M-63 -2.5033972 1.4668947 3.4101058
## 18-F-50 -1.3914421 0.2179958 1.3811467
## 18-F-51 -0.8190880 1.9022075 2.0601105
## 18-M-52 0.6149661 1.1813615 1.4824666For the ALR transform we need to choose a reference cell type whose numbers remain stable across conditions pre-sampling. In an ideal scenario this could a spike-in of a known number of cells. In this example, we assume that one of the reference cell types are “NK cells”. If it does not exist in your dataset, change the reference accordingly or use CLR.
# ALR Transformation: using "NK cell" as the reference cell type
if ("Natural Killer" %in% colnames(taxa_counts)) {
alr_out <- setaALR(taxa_counts, ref = "Natural Killer", pseudocount = 1)
print(alr_out$counts[1:5, 1:5])
} else {
message("Reference 'NK cell' not found in taxa_counts.
Please choose an available cell type.")
}##
## Adventitial Fibroblast Airway Smooth Muscle
## 1-M-62 -0.94446161 -3.29583687
## 1-M-63 -2.17475172 -3.78418963
## 18-F-50 -2.23359222 -2.63905733
## 18-F-51 -0.82927935 -1.81010861
## 18-M-52 -0.72213472 -2.86220088
##
## Alveolar Epithelial Type 2 Alveolar Fibroblast Alveolar Macrophage
## 1-M-62 -1.90954250 -0.40546511 1.01496226
## 1-M-63 -4.47733681 -0.50704490 1.43616619
## 18-F-50 -3.33220451 -1.72276660 -0.55961579
## 18-F-51 -2.39789527 0.32340016 0.48130318
## 18-M-52 -0.51082562 0.05556985 0.35667494ILR Transform using Helmert basis (boxcox_p = 0 for standard ILR) This is the method used by Cacoa (Viktor Petukhov & Kharchenko Lab)
ilr_out <- setaILR(taxa_counts, boxcox_p = 0, pseudocount = 1)
print(ilr_out$counts[1:5, 1:5])##
## [,1] [,2] [,3] [,4] [,5]
## 1-M-62 -1.6626734 0.1719597 1.4241631 2.373621 -0.9942587
## 1-M-63 -1.1380445 -1.2230026 2.5735802 3.731548 -0.3456867
## 18-F-50 -0.2867071 -0.7314827 0.8765777 1.719348 -0.4944201
## 18-F-51 -0.6935510 -0.8803477 1.7342112 1.484547 -1.2497730
## 18-M-52 -1.5132553 1.0462115 1.2302961 1.222300 -0.2317007Simple Percentage Transform
pct_out <- setaPercent(taxa_counts)
print(pct_out$counts[1:5, 1:5])##
## Adventitial Fibroblast Airway Smooth Muscle
## 1-M-62 1.85528757 0.09276438
## 1-M-63 0.62937063 0.06993007
## 18-F-50 0.60422961 0.30211480
## 18-F-51 1.98446937 0.69025022
## 18-M-52 0.99812851 0.06238303
##
## Alveolar Epithelial Type 2 Alveolar Fibroblast Alveolar Macrophage
## 1-M-62 0.64935065 3.24675325 13.72912801
## 1-M-63 0.00000000 3.63636364 25.80419580
## 18-F-50 0.00000000 1.20845921 4.53172205
## 18-F-51 0.34512511 6.47109577 7.59275237
## 18-M-52 1.24766064 2.24578915 3.05676856LogCPM (counts per 10k) Transform
lcpm_out <- setaLogCPM(taxa_counts, pseudocount = 1)
print(lcpm_out$counts[1:5, 1:5])##
## Adventitial Fibroblast Airway Smooth Muscle
## 1-M-62 14.248407 10.856089
## 1-M-63 12.770689 10.448761
## 18-F-50 13.141492 12.556529
## 18-F-51 14.336622 12.921584
## 18-M-52 13.371573 10.284110
##
## Alveolar Epithelial Type 2 Alveolar Fibroblast Alveolar Macrophage
## 1-M-62 12.856089 15.026014 17.075258
## 1-M-63 9.448761 15.176681 17.980142
## 18-F-50 11.556529 13.878457 15.556529
## 18-F-51 12.073588 15.999587 16.227393
## 18-M-52 13.676428 14.493564 14.927966Latent Space Analysis
After applying compositional transforms, we can perform dimensionality reduction to visualize and analyze the relationships between samples. The transformed data can be analyzed using standard multivariate methods, but it’s important to remember that we’re now working in log-ratio space, not the original count space.
We demonstrate three different latent space methods, each with specific advantages:
- Principal Component Analysis (PCA): Linear dimensionality reduction that maximizes variance, provides loadings for interpretation
- Principal Coordinate Analysis (PCoA): Works with distance matrices, useful when relationships between samples are more important than individual features
- Non-metric Multidimensional Scaling (NMDS): Non-linear method that preserves rank-order relationships, robust to outliers
latent_pca <- setaLatent(clr_out, method = "PCA", dims = 5)
# PCA Latent Space Coordinates:
head(latent_pca$latentSpace)## PC1 PC2 PC3 PC4 PC5
## 1-M-62 -0.3350223 3.1938325 -0.4848707 0.04635990 -0.76225078
## 1-M-63 -0.5097368 4.8874855 0.9040002 0.04858906 0.36921601
## 18-F-50 -1.5751990 -0.8085481 1.1262639 -1.31190158 -0.78190046
## 18-F-51 -2.3212913 -0.6116102 1.3477482 -2.63828824 -2.03933177
## 18-M-52 0.3601475 -0.5633859 -4.5736259 -1.47565863 -0.01092873
## 18-M-53 -1.1101814 0.2754001 -2.4996341 -0.80132618 0.66184605# Variance Explained:
latent_pca$varExplained## [1] 0.49890109 0.15856671 0.10540212 0.06273258 0.05150588Visualization
Below we show how to create basic visualizations of SETA latent spaces
Variance Explained Plot
Plot the variance explained by the first few principal components.
ve_df <- data.frame(
PC = factor(seq_along(latent_pca$varExplained),
levels = seq_along(latent_pca$varExplained)),
VarianceExplained = cumsum(latent_pca$varExplained)
)ggplot(ve_df, aes(x = PC, y = VarianceExplained)) +
geom_line(color = "#2ca1db", size = 1.5, group = 1) +
geom_point(color = "#f44323", size = 3) +
labs(title = "Cumulative Variance Explained by Principal Components",
x = "Principal Component", y = "Cumulative Variance Explained (%)") +
theme_minimal() +
ylim(c(0, 1)) +
theme(text = element_text(size = 12))## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
PCA Scatter plot
We overlay the PCA latent space coordinates (using the CLR transform) with metadata from the Seurat object. In this example, points are colored by age.
# Extract latent space coordinates from PCA result.
pca_coords <- latent_pca$latentSpace
# Extract metadata
meta_df <- setaMetadata(
df,
sample_col = "mouse.id",
meta_cols = c("age", "sex"))
# Merge latent coordinates with metadata
pca_plot_df <- cbind(pca_coords, meta_df)# Create the scatter plot
ggplot(pca_plot_df, aes(x = PC1, y = PC2, color = age)) +
geom_text(aes(label = sample_id), size = 5) +
scale_color_manual(
values = age_palette
) +
labs(title = "PCA Scatter Plot",
x = "PC1", y = "PC2", color = "Age") +
theme_linedraw() +
xlab(sprintf("PC1 (%s%%)", signif(latent_pca$varExplained[1], 4) * 100)) +
ylab(sprintf("PC2 (%s%%)", signif(latent_pca$varExplained[2], 4) * 100))
Loadings Plot
Plot the variance explained by interesting principal components.
loadings_df <- as.data.frame(latent_pca$loadings)
loadings_df$Celltype <- rownames(loadings_df)
# Melt the loadings into long format for `ggplot2`
loadings_long <- melt(loadings_df, id.vars = "Celltype",
variable.name = "PC", value.name = "Loading")Visualize the PC loadings for each cell
# Subset rows where PC is "PC1" or "PC2"
loading_df <- loadings_long[loadings_long$PC %in% c("PC1", "PC2"), ]
# Append PC to Celltype to mimic tidytext::reorder_within()
loading_df$Celltype_PC <- paste(loading_df$Celltype, loading_df$PC, sep = "___")
# Reorder by Loading *within each PC*
loading_df$Celltype_PC <- with(loading_df, reorder(Celltype_PC, Loading))
ggplot(loading_df,
aes(x = Loading, y = Celltype, label = Celltype, fill = Loading)) +
geom_col() +
facet_wrap(~PC, scales = "free") +
# scale_y_reordered() +
labs(title = "PC Loadings",
x = "Loading",
y = "Cell Type") +
theme_linedraw() +
scale_fill_viridis_c() +
expand_limits(x = c(-0.2, 0.4))
Conclusion
This vignette illustrated a basic workflow using the SETA package:
Extracting a taxonomic counts matrix from a
SeuratObject withsetaCounts()Applying compositional transforms (
CLR,ALR,ILR, and simple transforms)Performing latent space analysis (
PCA)Visualizing sample-level latent spaces
Each step is modular, so you can easily swap in your own data, update metadata column names, or choose different parameters (e.g., pseudocount, reference cell type) to suit your analysis needs.
For further details, see the package documentation and relevant literature on compositional data analysis (e.g., Aitchison, 1982).
Session Info
sessionInfo()## R version 4.5.1 Patched (2025-08-23 r88802)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] reshape2_1.4.4 rhdf5_2.53.6
## [3] TabulaMurisSenisData_1.15.1 caret_7.0-1
## [5] lattice_0.22-7 corrplot_0.95
## [7] tidyr_1.3.1 dplyr_1.1.4
## [9] ggplot2_4.0.0 SETA_0.99.7
## [11] SingleCellExperiment_1.31.1 SummarizedExperiment_1.39.2
## [13] Biobase_2.69.1 GenomicRanges_1.61.6
## [15] Seqinfo_0.99.3 IRanges_2.43.5
## [17] S4Vectors_0.47.4 BiocGenerics_0.55.4
## [19] generics_0.1.4 MatrixGenerics_1.21.0
## [21] matrixStats_1.5.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 pROC_1.19.0.1 httr2_1.2.1
## [4] rlang_1.1.6 magrittr_2.0.4 e1071_1.7-16
## [7] compiler_4.5.1 RSQLite_2.4.3 gdata_3.0.1
## [10] png_0.1-8 vctrs_0.6.5 stringr_1.5.2
## [13] pkgconfig_2.0.3 shape_1.4.6.1 crayon_1.5.3
## [16] fastmap_1.2.0 dbplyr_2.5.1 XVector_0.49.1
## [19] labeling_0.4.3 rmarkdown_2.30 prodlim_2025.04.28
## [22] bit_4.6.0 purrr_1.1.0 xfun_0.53
## [25] glmnet_4.1-10 cachem_1.1.0 jsonlite_2.0.0
## [28] blob_1.2.4 recipes_1.3.1 rhdf5filters_1.21.4
## [31] DelayedArray_0.35.3 Rhdf5lib_1.31.1 parallel_4.5.1
## [34] R6_2.6.1 bslib_0.9.0 stringi_1.8.7
## [37] RColorBrewer_1.1-3 parallelly_1.45.1 rpart_4.1.24
## [40] lubridate_1.9.4 jquerylib_0.1.4 Rcpp_1.1.0
## [43] iterators_1.0.14 knitr_1.50 future.apply_1.20.0
## [46] Matrix_1.7-4 splines_4.5.1 nnet_7.3-20
## [49] igraph_2.2.0 timechange_0.3.0 tidyselect_1.2.1
## [52] dichromat_2.0-0.1 abind_1.4-8 yaml_2.3.10
## [55] timeDate_4051.111 codetools_0.2-20 curl_7.0.0
## [58] listenv_0.9.1 tibble_3.3.0 plyr_1.8.9
## [61] KEGGREST_1.49.2 withr_3.0.2 S7_0.2.0
## [64] evaluate_1.0.5 future_1.67.0 survival_3.8-3
## [67] proxy_0.4-27 BiocFileCache_2.99.6 ExperimentHub_2.99.6
## [70] Biostrings_2.77.2 filelock_1.0.3 pillar_1.11.1
## [73] BiocManager_1.30.26 foreach_1.5.2 BiocVersion_3.22.0
## [76] scales_1.4.0 gtools_3.9.5 BiocStyle_2.37.1
## [79] globals_0.18.0 class_7.3-23 glue_1.8.0
## [82] tools_4.5.1 AnnotationHub_3.99.6 data.table_1.17.8
## [85] ModelMetrics_1.2.2.2 gower_1.0.2 tidygraph_1.3.1
## [88] grid_4.5.1 AnnotationDbi_1.71.2 ipred_0.9-15
## [91] nlme_3.1-168 HDF5Array_1.37.0 cli_3.6.5
## [94] rappdirs_0.3.3 viridisLite_0.4.2 S4Arrays_1.9.1
## [97] lava_1.8.1 gtable_0.3.6 sass_0.4.10
## [100] digest_0.6.37 SparseArray_1.9.1 farver_2.1.2
## [103] memoise_2.0.1 htmltools_0.5.8.1 lifecycle_1.0.4
## [106] h5mread_1.1.1 httr_1.4.7 hardhat_1.4.2
## [109] bit64_4.6.0-1 MASS_7.3-65