tidybulk: An R tidy framework for modular transcriptomic data analysis

Stefano Mangiola 2025-09-11

tidybulk is a powerful R package designed for modular transcriptomic data analysis that brings transcriptomics to the tidyverse.

Why tidybulk?

Tidybulk provides a unified interface for comprehensive transcriptomic data analysis with seamless integration of SummarizedExperiment objects and tidyverse principles. It streamlines the entire workflow from raw data to biological insights.

Functions/utilities available

Abundance Normalization Functions

Function Description
scale_abundance() Scale abundance data
quantile_normalise_abundance() Quantile normalization
adjust_abundance() Adjust abundance for unwanted variation
fill_missing_abundance() Fill missing abundance values
impute_missing_abundance() Impute missing abundance values

Filtering and Selection Functions

Function Description
identify_abundant() Identify abundant transcripts without removing them
keep_abundant() Keep abundant transcripts
keep_variable() Keep variable transcripts
filterByExpr() Filter by expression

Dimensionality Reduction Functions

Function Description
reduce_dimensions() Reduce dimensions with PCA/MDS/tSNE/UMAP
rotate_dimensions() Rotate dimensions
remove_redundancy() Remove redundant features

Clustering Functions

Function Description
cluster_elements() Cluster elements with various methods
kmeans clustering K-means clustering
SNN clustering Shared nearest neighbor clustering
hierarchical clustering Hierarchical clustering
DBSCAN clustering Density-based clustering

Differential Analysis Functions

Function Description
test_differential_expression() Test differential expression with various methods

Cellularity Analysis Functions

Function Description
deconvolve_cellularity() Deconvolve cellularity with various methods
cibersort() CIBERSORT analysis

Gene Enrichment Functions

Function Description
test_gene_enrichment() Test gene enrichment
test_gene_overrepresentation() Test gene overrepresentation
test_gene_rank() Test gene rank

Utility Functions

Function Description
describe_transcript() Describe transcript characteristics
get_bibliography() Get bibliography
resolve_complete_confounders_of_non_interest() Resolve confounders

Validation and Utility Functions

Function Description
check_if_counts_is_na() Check if counts contain NA values
check_if_duplicated_genes() Check for duplicated genes
check_if_wrong_input() Validate input data
log10_reverse_trans() Log10 reverse transformation
logit_trans() Logit transformation

All functions are directly compatible with SummarizedExperiment objects and follow tidyverse principles for seamless integration with the tidyverse ecosystem.

Scientific Citation

Mangiola, Stefano, Ramyar Molania, Ruining Dong, Maria A. Doyle, and Anthony T. Papenfuss. 2021. “Tidybulk: An R tidy framework for modular transcriptomic data analysis.” Genome Biology 22 (42). https://doi.org/10.1186/s13059-020-02233-7

Genome Biology - tidybulk: an R tidy framework for modular transcriptomic data analysis

In this vignette we will use the airway dataset, a SummarizedExperiment object containing RNA-seq data from an experiment studying the effect of dexamethasone treatment on airway smooth muscle cells. This dataset is available in the airway package.

library(airway)
data(airway)

This workflow, will use the tidySummarizedExperiment package to manipulate the data in a tidyverse fashion. This approach streamlines the data manipulation and analysis process, making it more efficient and easier to understand.

## tidySummarizedExperiment says: Printing is now handled externally. If you want to visualize the data in a tidy way, do library(tidyprint). See https://github.com/tidyomics/tidyprint for more information.

## 
## Attaching package: 'tidySummarizedExperiment'

## The following object is masked from 'package:generics':
## 
##     tidy

Here we will add a gene symbol column to the airway object. This will be used to interpret the differential expression analysis, and to deconvolve the cellularity.

library(org.Hs.eg.db)
## Loading required package: AnnotationDbi

## 
## Attaching package: 'AnnotationDbi'

## The following object is masked from 'package:dplyr':
## 
##     select

## 
library(AnnotationDbi)

# Add gene symbol and entrez
airway <-
  airway |>
  
  mutate(entrezid = mapIds(org.Hs.eg.db,
                                      keys = gene_name,
                                      keytype = "SYMBOL",
                                      column = "ENTREZID",
                                      multiVals = "first"
)) 
## 'select()' returned 1:many mapping between keys and columns
detach("package:org.Hs.eg.db", unload = TRUE)
detach("package:AnnotationDbi", unload = TRUE)

Installation Guide

Bioconductor

if (!requireNamespace("BiocManager")) install.packages("BiocManager")
BiocManager::install("tidybulk")

Github

devtools::install_github("stemangiola/tidybulk")

Comprehensive Example Pipeline

This vignette demonstrates a complete transcriptomic analysis workflow using tidybulk, with special emphasis on differential expression analysis.

Data Overview

We will use the airway dataset, a SummarizedExperiment object containing RNA-seq data from an experiment studying the effect of dexamethasone treatment on airway smooth muscle cells:

airway
## class: RangedSummarizedExperiment 
## dim: 63677 8 
## metadata(2): '' latest_mutate_scope_report
## assays(1): counts
## rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
##   ENSG00000273493
## rowData names(10): gene_id gene_name ... seq_coord_system symbol
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample

Loading tidySummarizedExperiment automatically abstracts this object as a tibble, making it compatible with tidyverse tools while maintaining its SummarizedExperiment nature:

class(airway)
## [1] "RangedSummarizedExperiment"
## attr(,"package")
## [1] "SummarizedExperiment"

Prepare Data for Analysis

Before analysis, we need to ensure our variables are in the correct format:

# Convert dex to factor for proper differential expression analysis
colData(airway)$dex = as.factor(colData(airway)$dex)

Visualize Raw Counts

Visualize the distribution of raw counts before any filtering:

ggplot(as_tibble(airway), aes(counts + 1, group = .sample, color = `dex`)) +
  geom_density() +
  scale_x_log10() +
  my_theme +
  labs(title = "Raw counts by treatment (before any filtering)")

Step 1: Data Preprocessing

Aggregate Duplicated Transcripts (optional)

Aggregate duplicated transcripts (e.g., isoforms, ensembl IDs):

Transcript aggregation is a standard bioinformatics approach for gene-level summarization.

# Add gene names to rowData
rowData(airway)$gene_name = rownames(airway)

# Aggregate duplicates
airway = airway |> aggregate_duplicates(.transcript = gene_name, aggregation_function = mean)
## tidybulk says: your object does not have duplicates along the gene_name column. The input dataset is returned.

Abundance Filtering

Abundance filtering can be performed using established methods (Robinson, McCarthy, and Smyth 2010; Chen, Lun, and Smyth 2016).

Run multiple methods 

# Default (simple filtering)
airway_abundant_default = airway |> keep_abundant()
## Warning in filterByExpr.DGEList(y, design = design, group = group, lib.size =
## lib.size, : All samples appear to belong to the same group.
# With factor_of_interest (recommended for complex designs)
airway_abundant_formula = airway |> keep_abundant(minimum_counts = 10, minimum_proportion = 0.5, factor_of_interest = dex)
## Warning: The `factor_of_interest` argument of `keep_abundant()` is deprecated as of
## tidybulk 2.0.0.
## ℹ Please use the `formula_design` argument instead.
## ℹ The argument 'factor_of_interest' is deprecated and will be removed in a
##   future release. Please use the 'design' or 'formula_design' argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: The `factor_of_interest` argument of `identify_abundant()` is deprecated as of
## tidybulk 2.0.0.
## ℹ Please use the `formula_design` argument instead.
## ℹ The argument 'factor_of_interest' is deprecated and will be removed in a
##   future release. Please use the 'design' or 'formula_design' argument instead.
## ℹ The deprecated feature was likely used in the tidybulk package.
##   Please report the issue at <https://github.com/stemangiola/tidybulk/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# With CPM threshold (using design parameter)
airway_abundant_cpm = airway |> keep_abundant(minimum_counts = 10, minimum_proportion = 0.5)
## Warning in filterByExpr.DGEList(y, design = design, group = group, lib.size =
## lib.size, : All samples appear to belong to the same group.

Compare methods 

# Example: summary for default tidybulk filtering
# Before filtering
airway |> as_tibble() |> summarise(
  n_features = n_distinct(.feature),
  min_count = min(counts),
  median_count = median(counts),
  max_count = max(counts)
)
## # A tibble: 1 × 4
##   n_features min_count median_count max_count
##        <int>     <int>        <dbl>     <int>
## 1      63677         0            0    513766
# After filtering
airway_abundant_default |> as_tibble() |> summarise(
  n_features = n_distinct(.feature),
  min_count = min(counts),
  median_count = median(counts),
  max_count = max(counts)
)
## # A tibble: 1 × 4
##   n_features min_count median_count max_count
##        <int>     <int>        <dbl>     <int>
## 1      14224         8          394    513766
airway_abundant_formula |> as_tibble() |> summarise(
  n_features = n_distinct(.feature),
  min_count = min(counts),
  median_count = median(counts),
  max_count = max(counts)
)
## # A tibble: 1 × 4
##   n_features min_count median_count max_count
##        <int>     <int>        <dbl>     <int>
## 1      15926         0          310    513766
airway_abundant_cpm |> as_tibble() |> summarise(
  n_features = n_distinct(.feature),
  min_count = min(counts),
  median_count = median(counts),
  max_count = max(counts)
)
## # A tibble: 1 × 4
##   n_features min_count median_count max_count
##        <int>     <int>        <dbl>     <int>
## 1      14224         8          394    513766
# Merge all methods into a single tibble
airway_abundant_all = 
  bind_rows(
    airway |> assay() |> as_tibble(rownames = ".feature") |> pivot_longer(cols = -.feature, names_to = ".sample", values_to = "counts") |> mutate(method = "no filter"),
    airway_abundant_default |> assay() |> as_tibble(rownames = ".feature") |> pivot_longer(cols = -.feature, names_to = ".sample", values_to = "counts") |> mutate(method = "default"),
    airway_abundant_formula |> assay() |> as_tibble(rownames = ".feature") |> pivot_longer(cols = -.feature, names_to = ".sample", values_to = "counts") |> mutate(method = "formula"),
    airway_abundant_cpm |> assay() |> as_tibble(rownames = ".feature") |> pivot_longer(cols = -.feature, names_to = ".sample", values_to = "counts") |> mutate(method = "cpm")
  )

# Density plot across methods
airway_abundant_all |> 
  as_tibble() |> 
  ggplot(aes(counts + 1, group = .sample, color = method)) +
    geom_density() +
    scale_x_log10() +
    facet_wrap(~method) +
    my_theme +
    labs(title = "Counts after abundance filtering (tidybulk default)")

Update the airway object with the filtered data:

airway = airway_abundant_formula

Tip: Use formula_design for complex designs, and use the CPM threshold for library-size-aware filtering.

Remove Redundant Transcripts

Redundancy removal is a standard approach for reducing highly correlated features.

airway_non_redundant = 
  airway |> 
  remove_redundancy(method = "correlation", top = 100) 
## Getting the 100 most variable genes
  # Make  

airway |> as_tibble() |> summarise(
  n_features = n_distinct(.feature),
  min_count = min(counts),
  median_count = median(counts),
  max_count = max(counts)
)
## # A tibble: 1 × 4
##   n_features min_count median_count max_count
##        <int>     <int>        <dbl>     <int>
## 1      15926         0          310    513766
# Summary statistics
airway_non_redundant |> as_tibble() |> summarise(
  n_features = n_distinct(.feature),
  min_count = min(counts),
  median_count = median(counts),
  max_count = max(counts)
)
## # A tibble: 1 × 4
##   n_features min_count median_count max_count
##        <int>     <int>        <dbl>     <int>
## 1      15926         0          357    401539
# Plot before and after
# Merge before and after into a single tibble
airway_all = bind_rows(
  airway |> assay() |> as_tibble(rownames = ".feature") |> pivot_longer(cols = -.feature, names_to = ".sample", values_to = "counts") |>  mutate(method = "before"),
  airway_non_redundant |> assay() |> as_tibble(rownames = ".feature") |> pivot_longer(cols = -.feature, names_to = ".sample", values_to = "counts") |>  mutate(method = "after")
)

# Density plot
ggplot(as_tibble(airway_all), aes(counts + 1, group = .sample, color = method)) +
  geom_density() +
  scale_x_log10() +
  facet_wrap(~method) +
  my_theme +
  labs(title = "Counts after removing redundant transcripts")

Filter Variable Transcripts

Keep only the most variable transcripts for downstream analysis.

Variance-based feature selection using edgeR methodology (Robinson, McCarthy, and Smyth 2010) is used for selecting informative features.

airway_variable = airway |> keep_variable()
## Getting the 500 most variable genes

Visualize After Variable Filtering Variable Transcripts (optional) 

# Before filtering
airway |> as_tibble() |> summarise(
  n_features = n_distinct(.feature),
  min_count = min(counts),
  median_count = median(counts),
  max_count = max(counts)
)
## # A tibble: 1 × 4
##   n_features min_count median_count max_count
##        <int>     <int>        <dbl>     <int>
## 1      15926         0          310    513766
# After filtering
airway_variable |> as_tibble() |> summarise(
  n_features = n_distinct(.feature),
  min_count = min(counts),
  median_count = median(counts),
  max_count = max(counts)
)
## # A tibble: 1 × 4
##   n_features min_count median_count max_count
##        <int>     <int>        <dbl>     <int>
## 1        500         0           46     76824
# Density plot
# Merge before and after into a single tibble
airway_all = bind_rows(
  airway |> assay() |> as_tibble(rownames = ".feature") |> pivot_longer(cols = -.feature, names_to = ".sample", values_to = "counts") |> mutate(method = "before"),
  airway_variable |> assay() |> as_tibble(rownames = ".feature") |> pivot_longer(cols = -.feature, names_to = ".sample", values_to = "counts") |> mutate(method = "after")
)

# Density plot
ggplot(as_tibble(airway_all), aes(counts + 1, group = .sample, color = method)) +
  geom_density() +
  scale_x_log10() +
  facet_wrap(~method) +
  my_theme +
  labs(title = "Counts after variable filtering")

Scale Abundance

Scale for sequencing depth using TMM (Robinson, McCarthy, and Smyth 2010), upper quartile (Bullard et al. 2010), and RLE (Anders and Huber 2010) normalization.

airway = 
airway |> 
    scale_abundance(method = "TMM", suffix = "_tmm") |>
    scale_abundance(method = "upperquartile", suffix = "_upperquartile") |>
    scale_abundance(method = "RLE", suffix = "_RLE")
## tidybulk says: the sample with largest library size SRR1039517 was chosen as reference for scaling
## tidybulk says: the sample with largest library size SRR1039517 was chosen as reference for scaling
## tidybulk says: the sample with largest library size SRR1039517 was chosen as reference for scaling

Visualize After Scaling 

# Before scaling
airway |> assay("counts") |> as.matrix() |> rowMeans() |> summary()
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##      7.38     71.38    325.56   1375.62    989.38 328812.62
airway |> assay("counts_tmm") |> as.matrix() |> rowMeans() |> summary()
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##     10.50     97.19    444.13   1884.49   1350.66 457443.80
airway |> assay("counts_upperquartile") |> as.matrix() |> rowMeans() |> summary()
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##     10.51     99.10    452.57   1918.62   1376.18 465420.93
airway |> assay("counts_RLE") |> as.matrix() |> rowMeans() |> summary()
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##     10.45     97.02    443.51   1881.64   1348.31 456680.75
# Merge all methods into a single tibble
airway_scaled_all = bind_rows(
  airway |> assay("counts") |> as_tibble(rownames = ".feature") |> pivot_longer(cols = -.feature, names_to = ".sample", values_to = "counts") |> mutate(method = "no_scaling"),
  airway |> assay("counts_tmm") |> as_tibble(rownames = ".feature") |> pivot_longer(cols = -.feature, names_to = ".sample", values_to = "counts") |> mutate(method = "TMM"),
  airway |> assay("counts_upperquartile") |> as_tibble(rownames = ".feature") |> pivot_longer(cols = -.feature, names_to = ".sample", values_to = "counts") |> mutate(method = "upperquartile"),
  airway |> assay("counts_RLE") |> as_tibble(rownames = ".feature") |> pivot_longer(cols = -.feature, names_to = ".sample", values_to = "counts") |> mutate(method = "RLE")
)


# Density plot
ggplot(as_tibble(airway_scaled_all), aes(counts + 1, group = .sample, color = method)) +
  geom_density() +
  scale_x_log10() +
  facet_wrap(~method) +
  my_theme +
  labs(title = "Scaled counts by method (after scaling)")

Step 2: Exploratory Data Analysis

Remove Zero-Variance Features (required for PCA)

Variance filtering is a standard preprocessing step for dimensionality reduction.

library(matrixStats)
# Remove features with zero variance across samples
airway = airway[rowVars(assay(airway)) > 0, ]

Dimensionality Reduction

MDS (Kruskal 1964) using limma::plotMDS (ritchie2015limma?) and PCA (Hotelling 1933) are used for dimensionality reduction.

airway = airway |>
  reduce_dimensions(method="MDS", .dims = 2)
## Getting the 500 most variable genes

## [1] "MDS result_df colnames: sample, 1, 2"

## tidybulk says: to access the raw results do `metadata(.)$tidybulk$MDS`
airway = airway |>
  reduce_dimensions(method="PCA", .dims = 2)
## Getting the 500 most variable genes

## Fraction of variance explained by the selected principal components

## # A tibble: 2 × 2
##   `Fraction of variance`    PC
##                    <dbl> <int>
## 1                 0.0652     1
## 2                 0.0553     2

## tidybulk says: to access the raw results do `metadata(.)$tidybulk$PCA`

Visualize Dimensionality Reduction Results 

# MDS plot
airway |>
    pivot_sample() |>
    ggplot(aes(x=`Dim1`, y=`Dim2`, color=`dex`)) +
  geom_point() +
    my_theme +
    labs(title = "MDS Analysis")

# PCA plot
    airway |>
    pivot_sample() |>
    ggplot(aes(x=`PC1`, y=`PC2`, color=`dex`)) +
    geom_point() +
    my_theme +
    labs(title = "PCA Analysis")

Clustering Analysis

K-means clustering (MacQueen 1967) is used for unsupervised grouping.

airway = airway |>
  cluster_elements(method="kmeans", centers = 2)

# Visualize clustering
    airway |>
    ggplot(aes(x=`Dim1`, y=`Dim2`, color=`cluster_kmeans`)) +
  geom_point() +
  my_theme +
  labs(title = "K-means Clustering")

Step 3: Differential Expression Analysis

This workflow uses

edgeR quasi-likelihood (Robinson, McCarthy, and Smyth 2010; Chen, Lun, and Smyth 2016) edgeR robust likelihood ratio (Chen, Lun, and Smyth 2016), DESeq2 (Love, Huber, and Anders 2014), limma-voom (Law et al. 2014), and limma-voom with sample weights (Liu et al. 2015) for differential expression analysis.

Basic Differential Expression

Methods:

  • edgeR quasi-likelihood: Quasi-likelihood F-tests for differential expression

  • edgeR robust likelihood ratio: Robust likelihood ratio tests

  • DESeq2: Negative binomial distribution with dispersion estimation

  • limma-voom: Linear modeling with empirical Bayes moderation

  • limma-voom with sample weights: Enhanced voom with quality weights References:

  • Robinson et al. (2010) edgeR: a Bioconductor package for differential expression analysis

  • Chen et al. (2016) From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline

  • Love et al. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

  • Law et al. (2014) voom: precision weights unlock linear model analysis tools for RNA-seq read counts

  • Liu et al. (2015) Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses

# Standard differential expression analysis
airway = airway |>

# Use QL method
    test_differential_expression(~ dex, method = "edgeR_quasi_likelihood", prefix = "ql__") |>
    
    # Use edger_robust_likelihood_ratio
    test_differential_expression(~ dex, method = "edger_robust_likelihood_ratio", prefix = "lr_robust__") |>
    
# Use DESeq2 method
    test_differential_expression(~ dex, method = "DESeq2", prefix = "deseq2__") |>
    
    # Use limma_voom
    test_differential_expression(~ dex, method = "limma_voom", prefix = "voom__") |>

# Use limma_voom_sample_weights
    test_differential_expression(~ dex, method = "limma_voom_sample_weights", prefix = "voom_weights__") 
## Warning: The `.abundance` argument of `test_differential_abundance()` is deprecated as
## of tidybulk 2.0.0.
## ℹ Please use the `abundance` argument instead.
## ℹ The deprecated feature was likely used in the tidybulk package.
##   Please report the issue at <https://github.com/stemangiola/tidybulk/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## =====================================
## tidybulk says: All testing methods use raw counts, irrespective of if scale_abundance
## or adjust_abundance have been calculated. Therefore, it is essential to add covariates
## such as batch effects (if applicable) in the formula.
## =====================================
## tidybulk says: The design column names are "(Intercept), dexuntrt"
## 
## tidybulk says: to access the DE object do `metadata(.)$tidybulk$edgeR_quasi_likelihood_object`
## tidybulk says: to access the raw results (fitted GLM) do `metadata(.)$tidybulk$edgeR_quasi_likelihood_fit`
## tidybulk says: The design column names are "(Intercept), dexuntrt"
## 
## tidybulk says: to access the DE object do `metadata(.)$tidybulk$edger_robust_likelihood_ratio_object`
## tidybulk says: to access the raw results (fitted GLM) do `metadata(.)$tidybulk$edger_robust_likelihood_ratio_fit`
## estimating size factors
## 
## estimating dispersions
## 
## gene-wise dispersion estimates
## 
## mean-dispersion relationship
## 
## final dispersion estimates
## 
## fitting model and testing
## 
## tidybulk says: to access the DE object do `metadata(.)$tidybulk$DESeq2_object`
## tidybulk says: to access the raw results (fitted GLM) do `metadata(.)$tidybulk$DESeq2_fit`
## tidybulk says: The design column names are "(Intercept), dexuntrt"
## 
## tidybulk says: to access the DE object do `metadata(.)$tidybulk$limma_voom_object`
## tidybulk says: to access the raw results (fitted GLM) do `metadata(.)$tidybulk$limma_voom_fit`
## tidybulk says: The design column names are "(Intercept), dexuntrt"
## 
## tidybulk says: to access the DE object do `metadata(.)$tidybulk$limma_voom_sample_weights_object`
## tidybulk says: to access the raw results (fitted GLM) do `metadata(.)$tidybulk$limma_voom_sample_weights_fit`
## This message is displayed once per session.

Quality Control of the Fit

It is important to check the quality of the fit. All methods produce a fit object that can be used for quality control. The fit object produced by each underlying method are stored in as attributes of the airway_mini object. We can use them for example to perform quality control of the fit.

For edgeR

Plot the biological coefficient of variation (BCV) trend. This plot is helpful to understant the dispersion of the data.

## Warning: package 'edgeR' was built under R version 4.5.1

## Loading required package: limma

## Warning: package 'limma' was built under R version 4.5.1

## 
## Attaching package: 'limma'

## The following object is masked from 'package:BiocGenerics':
## 
##     plotMA
metadata(airway)$tidybulk$edgeR_quasi_likelihood_object |>
  plotBCV()

Plot the log-fold change vs mean plot.

library(edgeR)

metadata(airway)$tidybulk$edgeR_quasi_likelihood_fit |>
  plotMD()

For DESeq2

Plot the mean-variance trend.

library(DESeq2)

metadata(airway)$tidybulk$DESeq2_object |>
  plotDispEsts()

Plot the log-fold change vs mean plot.

library(DESeq2)

metadata(airway)$tidybulk$DESeq2_object |>
  plotMA()

Histograms of p-values across methods

Inspection of the raw p-value histogram provides a rapid check of differential-expression results. When no gene is truly differentially expressed, the p-values follow a uniform U(0,1) distribution across the interval 0–1, so the histogram appears flat Source. In a more realistic scenario where only a subset of genes changes, this uniform background is still present but an obvious spike emerges close to zero, created by the genuine signals.

Thanks to the modularity of the tidybulk workflow, that can multiplex different methods, we can easily compare the p-values across methods.

    airway |>
  rowData() |> 
  as_tibble() |> 
  select(
    ql__PValue, 
    lr_robust__PValue, 
    voom__P.Value, 
    voom_weights__P.Value, 
    deseq2__pvalue
  ) |> 
  pivot_longer(everything(), names_to = "method", values_to = "pvalue") |>
  ggplot(aes(x = pvalue, fill = method)) +
  geom_histogram(binwidth = 0.01) +
  facet_wrap(~method) +
  my_theme +
  labs(title = "Histogram of p-values across methods")
## Warning: Removed 30 rows containing non-finite outside the scale range
## (`stat_bin()`).

Compare Results Across Methods 

# Summary statistics
airway |> rowData() |> as_tibble() |> select(contains("ql|lr_robust|voom|voom_weights|deseq2")) |> select(contains("logFC")) |> 
summarise(across(everything(), list(min = min, median = median, max = max), na.rm = TRUE))
## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `across(...)`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
## 
##   # Previously
##   across(a:b, mean, na.rm = TRUE)
## 
##   # Now
##   across(a:b, \(x) mean(x, na.rm = TRUE))

## # A tibble: 1 × 0

Pairplot of pvalues across methods (GGpairs) 

## Warning: package 'GGally' was built under R version 4.5.1
airway |> 
  rowData() |> 
  as_tibble() |> 
  select(ql__PValue, lr_robust__PValue, voom__P.Value, voom_weights__P.Value, deseq2__pvalue) |> 
  ggpairs(columns = 1:5) +
  scale_x_continuous(trans = tidybulk::log10_reverse_trans()) +
  scale_y_continuous(trans = tidybulk::log10_reverse_trans()) +
  my_theme +
  labs(title = "Pairplot of p-values across methods")
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 30 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 30 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 30 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 30 rows containing missing values

## Warning in scale_y_continuous(trans = tidybulk::log10_reverse_trans()):
## log10_reverse transformation introduced infinite values.

## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 30 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 30 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 30 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 30 rows containing non-finite outside the scale range
## (`stat_density()`).

## Warning in scale_y_continuous(trans = tidybulk::log10_reverse_trans()):
## log10_reverse transformation introduced infinite values.

Pairplot of effect sizes across methods (GGpairs) 

library(GGally)
airway |> 
  rowData() |> 
  as_tibble() |> 
  select(ql__logFC, lr_robust__logFC, voom__logFC, voom_weights__logFC, deseq2__log2FoldChange) |> 
  ggpairs(columns = 1:5) +
  my_theme +
  labs(title = "Pairplot of effect sizes across methods")

Volcano Plots for Each Method

Visualising the significance and effect size of the differential expression results as a volcano plots we appreciate that some methods have much lower p-values distributions than other methods, for the same model and data.

# Create volcano plots
airway |>

    # Select the columns we want to plot
    rowData() |> 
    as_tibble(rownames = ".feature") |> 
    select(
            .feature,
      ql__logFC, ql__PValue,
      lr_robust__logFC, lr_robust__PValue,
      voom__logFC, voom__P.Value,
      voom_weights__logFC, voom_weights__P.Value,
      deseq2__log2FoldChange, deseq2__pvalue
    ) |>

    # Pivot longer to get a tidy data frame
    pivot_longer(
      - .feature,
      names_to = c("method", "stat"),
      values_to = "value", names_sep = "__"
    ) |>

    # Harmonize column names
    mutate(stat  = case_when(
        stat %in% c("logFC", "log2FoldChange") ~ "logFC",
        stat %in% c("PValue", "pvalue", "P.Value", "p.value") ~ "PValue"
    )) |>
  pivot_wider(names_from = "stat", values_from = "value") |>
  unnest(c(logFC, PValue)) |> 

    # Plot
  ggplot(aes(x = logFC, y = PValue)) +
  geom_point(aes(color = PValue < 0.05, size = PValue < 0.05)) +
  scale_y_continuous(trans = tidybulk::log10_reverse_trans()) +
  scale_color_manual(values = c("TRUE" = "red", "FALSE" = "black")) +
  scale_size_manual(values = c("TRUE" = 0.5, "FALSE" = 0.1)) +
  facet_wrap(~method) +
  my_theme +
  labs(title = "Volcano Plots by Method")
## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_point()`).

Plotting independent y-axis scales for the p-values and effect sizes allows us to compare the top genes across methods.

# Create volcano plots
airway |>

    # Select the columns we want to plot
    rowData() |> 
    as_tibble(rownames = ".feature") |> 
    select(
            symbol,
      ql__logFC, ql__PValue,
      lr_robust__logFC, lr_robust__PValue,
      voom__logFC, voom__P.Value,
      voom_weights__logFC, voom_weights__P.Value,
      deseq2__log2FoldChange, deseq2__pvalue
    ) |>

    # Pivot longer to get a tidy data frame
    pivot_longer(
      - symbol,
      names_to = c("method", "stat"),
      values_to = "value", names_sep = "__"
    ) |>

    # Harmonize column names
    mutate(stat  = case_when(
        stat %in% c("logFC", "log2FoldChange") ~ "logFC",
        stat %in% c("PValue", "pvalue", "P.Value", "p.value") ~ "PValue"
    )) |>
  pivot_wider(names_from = "stat", values_from = "value") |>
  unnest(c(logFC, PValue)) |> 

    # Plot
  ggplot(aes(x = logFC, y = PValue)) +
  geom_point(aes(color = PValue < 0.05, size = PValue < 0.05)) +
  ggrepel::geom_text_repel(aes(label = symbol), size = 2, max.overlaps = 20) +
  scale_y_continuous(trans = tidybulk::log10_reverse_trans()) +
  scale_color_manual(values = c("TRUE" = "red", "FALSE" = "black")) +
  scale_size_manual(values = c("TRUE" = 0.5, "FALSE" = 0.1)) +
  facet_wrap(~method, scales = "free_y") +
  my_theme +
  labs(title = "Volcano Plots by Method")
## Warning: Values from `value` are not uniquely identified; output will contain list-cols.
## • Use `values_fn = list` to suppress this warning.
## • Use `values_fn = {summary_fun}` to summarise duplicates.
## • Use the following dplyr code to identify duplicates.
##   {data} |>
##   dplyr::summarise(n = dplyr::n(), .by = c(symbol, method, stat)) |>
##   dplyr::filter(n > 1L)

## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_text_repel()`).

## Warning: ggrepel: 15885 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## Warning: ggrepel: 15926 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## Warning: ggrepel: 15916 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## Warning: ggrepel: 15925 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## ggrepel: 15925 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Differential Expression with Contrasts

Contrast-based differential expression analysis using edgeR (Robinson, McCarthy, and Smyth 2010; Chen, Lun, and Smyth 2016) is a standard statistical approach for testing specific comparisons in complex designs.

# Using contrasts for more complex comparisons
airway |>
    test_differential_expression(
        ~ 0 + dex,                  
        .contrasts = c("dextrt - dexuntrt"),
        method = "edgeR_quasi_likelihood", 
        prefix = "contrasts__"
    ) |> 

    # Print the gene statistics
  pivot_transcript() |>
  select(contains("contrasts"))
## Warning: The `.contrasts` argument of `test_differential_abundance()` is deprecated as
## of tidybulk 1.7.4.
## ℹ The argument .contrasts is now deprecated please use contrasts (without the
##   dot).
## ℹ The deprecated feature was likely used in the tidybulk package.
##   Please report the issue at <https://github.com/stemangiola/tidybulk/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## tidybulk says: The design column names are "dextrt, dexuntrt"

## tidybulk says: to access the DE object do `metadata(.)$tidybulk$edgeR_quasi_likelihood_object`
## tidybulk says: to access the raw results (fitted GLM) do `metadata(.)$tidybulk$edgeR_quasi_likelihood_fit`

## # A tibble: 15,926 × 5
##    contrasts__logFC___dextrt...d…¹ contrasts__logCPM___…² contrasts__F___dextr…³
##                              <dbl>                  <dbl>                  <dbl>
##  1                         -0.388                    5.06                 6.82  
##  2                          0.195                    4.61                 5.98  
##  3                          0.0239                   3.48                 0.0566
##  4                         -0.124                    1.48                 0.214 
##  5                          0.432                    8.09                 2.94  
##  6                         -0.250                    5.91                 6.08  
##  7                         -0.0387                   4.84                 0.0327
##  8                         -0.497                    4.12                 7.07  
##  9                         -0.140                    3.12                 1.03  
## 10                         -0.0527                   7.04                 0.0331
## # ℹ 15,916 more rows
## # ℹ abbreviated names: ¹​contrasts__logFC___dextrt...dexuntrt,
## #   ²​contrasts__logCPM___dextrt...dexuntrt, ³​contrasts__F___dextrt...dexuntrt
## # ℹ 2 more variables: contrasts__PValue___dextrt...dexuntrt <dbl>,
## #   contrasts__FDR___dextrt...dexuntrt <dbl>

Differential Expression with minimum fold change (TREAT method)

TREAT method (McCarthy and Smyth 2009) is used for testing significance relative to a fold-change threshold.

# Using contrasts for more complex comparisons
airway |>
    test_differential_expression(
        ~ 0 + dex,                  
        .contrasts = c("dextrt - dexuntrt"),
        method = "edgeR_quasi_likelihood", 
        test_above_log2_fold_change = 2, 
        prefix = "treat__"
    ) |> 

    # Print the gene statistics
  pivot_transcript() |>
  select(contains("treat"))
## tidybulk says: The design column names are "dextrt, dexuntrt"

## tidybulk says: to access the DE object do `metadata(.)$tidybulk$edgeR_quasi_likelihood_object`
## tidybulk says: to access the raw results (fitted GLM) do `metadata(.)$tidybulk$edgeR_quasi_likelihood_fit`

## # A tibble: 15,926 × 5
##    treat__logFC___dextrt...dexun…¹ treat__unshrunk.logF…² treat__logCPM___dext…³
##                              <dbl>                  <dbl>                  <dbl>
##  1                         -0.388                 -0.388                    5.06
##  2                          0.195                  0.195                    4.61
##  3                          0.0239                 0.0240                   3.48
##  4                         -0.124                 -0.124                    1.48
##  5                          0.432                  0.433                    8.09
##  6                         -0.250                 -0.250                    5.91
##  7                         -0.0387                -0.0387                   4.84
##  8                         -0.497                 -0.497                    4.12
##  9                         -0.140                 -0.140                    3.12
## 10                         -0.0527                -0.0527                   7.04
## # ℹ 15,916 more rows
## # ℹ abbreviated names: ¹​treat__logFC___dextrt...dexuntrt,
## #   ²​treat__unshrunk.logFC___dextrt...dexuntrt,
## #   ³​treat__logCPM___dextrt...dexuntrt
## # ℹ 2 more variables: treat__PValue___dextrt...dexuntrt <dbl>,
## #   treat__FDR___dextrt...dexuntrt <dbl>

Mixed Models for Complex Designs

glmmSeq (Ma et al. 2020) is used for generalized linear mixed models for RNA-seq data.

# Using glmmSeq for mixed models
airway |>
  keep_abundant(formula_design = ~ dex) |>
  
  # Select 100 genes in the interest of execution time
  _[1:100,] |>

  # Fit model
  test_differential_expression(
    ~ dex + (1|cell), 
    method = "glmmseq_lme4", 
    cores = 1,
    prefix = "glmmseq__"
  ) 
## 
## n = 8 samples, 4 individuals

## Time difference of 36.80137 secs

## tidybulk says: to access the DE object do
## `metadata(.)$tidybulk$glmmseq_lme4_object`

## tidybulk says: to access the raw results (fitted GLM) do
## `metadata(.)$tidybulk$glmmseq_lme4_fit`

## class: RangedSummarizedExperiment 
## dim: 100 8 
## metadata(3): '' latest_mutate_scope_report tidybulk
## assays(4): counts counts_tmm counts_upperquartile counts_RLE
## rownames(100): ENSG00000000003 ENSG00000000419 ... ENSG00000006114
##   ENSG00000006118
## rowData names(63): gene_id gene_name ... glmmseq__P_dex
##   glmmseq__P_dex_adjusted
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(16): SampleName cell ... PC2 cluster_kmeans
  airway |>
  pivot_transcript() 
## # A tibble: 15,926 × 41
##    .feature  gene_id gene_name entrezid gene_biotype gene_seq_start gene_seq_end
##    <chr>     <chr>   <chr>     <chr>    <chr>                 <int>        <int>
##  1 ENSG0000… ENSG00… ENSG0000… 7105     protein_cod…       99883667     99894988
##  2 ENSG0000… ENSG00… ENSG0000… 8813     protein_cod…       49551404     49575092
##  3 ENSG0000… ENSG00… ENSG0000… 57147    protein_cod…      169818772    169863408
##  4 ENSG0000… ENSG00… ENSG0000… <NA>     protein_cod…      169631245    169823221
##  5 ENSG0000… ENSG00… ENSG0000… 3075     protein_cod…      196621008    196716634
##  6 ENSG0000… ENSG00… ENSG0000… 2519     protein_cod…      143815948    143832827
##  7 ENSG0000… ENSG00… ENSG0000… 2729     protein_cod…       53362139     53481768
##  8 ENSG0000… ENSG00… ENSG0000… 4800     protein_cod…       41040684     41067715
##  9 ENSG0000… ENSG00… ENSG0000… 90529    protein_cod…       24683489     24743424
## 10 ENSG0000… ENSG00… ENSG0000… 57185    protein_cod…       24742284     24799466
## # ℹ 15,916 more rows
## # ℹ 34 more variables: seq_name <chr>, seq_strand <int>,
## #   seq_coord_system <int>, symbol <chr>, .abundant <lgl>, ql__logFC <dbl>,
## #   ql__logCPM <dbl>, ql__F <dbl>, ql__PValue <dbl>, ql__FDR <dbl>,
## #   lr_robust__logFC <dbl>, lr_robust__logCPM <dbl>, lr_robust__LR <dbl>,
## #   lr_robust__PValue <dbl>, lr_robust__FDR <dbl>, deseq2__baseMean <dbl>,
## #   deseq2__log2FoldChange <dbl>, deseq2__lfcSE <dbl>, deseq2__stat <dbl>, …

Gene Description

With tidybulk, retrieving gene descriptions is straightforward, making it easy to enhance the interpretability of your differential expression results.

# Add gene descriptions using the original SummarizedExperiment
airway |> 

    describe_transcript() |>

    # Filter top significant genes
    filter(ql__FDR < 0.05) |>

    # Print the gene statistics
    pivot_transcript() |> 
    dplyr::select(.feature, description, contains("ql")) |> 
    head()
## 

## 

## # A tibble: 6 × 7
##   .feature        description ql__logFC ql__logCPM ql__F ql__PValue ql__FDR
##   <chr>           <lgl>           <dbl>      <dbl> <dbl>      <dbl>   <dbl>
## 1 ENSG00000002834 NA             -0.389      8.39   16.3  0.00301   0.0319 
## 2 ENSG00000003096 NA              0.929      4.16   31.2  0.000358  0.00883
## 3 ENSG00000003402 NA             -1.17       6.90   65.0  0.0000224 0.00226
## 4 ENSG00000003987 NA             -0.967      0.347  17.3  0.00255   0.0290 
## 5 ENSG00000004059 NA             -0.359      5.84   17.1  0.00260   0.0292 
## 6 ENSG00000004487 NA              0.309      5.86   19.8  0.00164   0.0222

Step 4: Batch Effect Correction

ComBat-seq (Zhang, Parmigiani, and Johnson 2020) is used for batch effect correction in RNA-seq data.

# Adjust for batch effects
airway = airway |>
  adjust_abundance(
      .factor_unwanted = cell, 
      .factor_of_interest = dex, 
    method = "combat_seq", 
      abundance = "counts_tmm"
  )
## Found 4 batches
## Using null model in ComBat-seq.
## Adjusting for 1 covariate(s) or covariate level(s)
## Estimating dispersions
## Fitting the GLM model
## Shrinkage off - using GLM estimates for parameters
## Adjusting the data
# Scatter plot of adjusted vs unadjusted
airway |> 
  
  # Subset genes to speed up plotting
  _[1:100,] |> 
  select(symbol, .sample, counts_tmm, counts_tmm_adjusted) |> 
  
  ggplot(aes(x = counts_tmm + 1, y = counts_tmm_adjusted + 1)) +
  geom_point(aes(color = .sample), size = 0.1) +
  ggrepel::geom_text_repel(aes(label = symbol), size = 2, max.overlaps = 20) +
  scale_x_log10() +
  scale_y_log10() +
  my_theme +
  labs(title = "Scatter plot of adjusted vs unadjusted")
## tidySummarizedExperiment says: Key columns are missing. A data frame is returned for independent data analysis.

## Warning: ggrepel: 784 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Step 5: Gene Enrichment Analysis

Gene Set Enrichment Analysis (GSEA) (Subramanian et al. 2005) is used for gene set enrichment.

# Run gene rank enrichment (GSEA style)
gene_rank_res =
  airway |>

    # Filter for genes with entrez IDs
  filter(!entrezid |> is.na()) |>
  aggregate_duplicates(.transcript = entrezid) |> 

  # Test gene rank
  test_gene_rank(
    .entrez = entrezid,
    .arrange_desc = lr_robust__logFC,
    species = "Homo sapiens",
    gene_sets = c("H", "C2", "C5")
  )
## 

## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).

## preparing geneSet collections...

## GSEA analysis...

## leading edge analysis...

## done...

## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).

## preparing geneSet collections...

## GSEA analysis...

## leading edge analysis...

## done...

## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).

## preparing geneSet collections...

## GSEA analysis...

## leading edge analysis...

## done...

## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `fit = map(...)`.
## Caused by warning in `fgseaMultilevel()`:
## ! For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
# Inspect significant gene sets (example for C2 collection)
gene_rank_res |>
  filter(gs_collection == "C2") |>
  dplyr::select(-fit) |>
  unnest(test) |>
  filter(p.adjust < 0.05)
## # A tibble: 91 × 13
##    gs_collection idx_for_plotting ID         Description setSize enrichmentScore
##    <chr>                    <int> <chr>      <chr>         <int>           <dbl>
##  1 C2                           1 CHEN_LVAD… CHEN_LVAD_…      83          -0.726
##  2 C2                           2 VECCHI_GA… VECCHI_GAS…     205          -0.574
##  3 C2                           3 FEKIR_HEP… FEKIR_HEPA…      27          -0.840
##  4 C2                           4 BOQUEST_S… BOQUEST_ST…     375          -0.436
##  5 C2                           5 WP_OVERVI… WP_OVERVIE…      28           0.788
##  6 C2                           6 SHEDDEN_L… SHEDDEN_LU…     154          -0.554
##  7 C2                           7 BLANCO_ME… BLANCO_MEL…     142           0.527
##  8 C2                           8 CHARAFE_B… CHARAFE_BR…     388          -0.416
##  9 C2                           9 HOSHIDA_L… HOSHIDA_LI…     188          -0.502
## 10 C2                          10 MEBARKI_H… MEBARKI_HC…     427           0.380
## # ℹ 81 more rows
## # ℹ 7 more variables: NES <dbl>, pvalue <dbl>, p.adjust <dbl>, qvalue <dbl>,
## #   rank <dbl>, leading_edge <chr>, core_enrichment <chr>

Visualize enrichment 

## Warning: package 'enrichplot' was built under R version 4.5.1

## enrichplot v1.28.4 Learn more at https://yulab-smu.top/contribution-knowledge-mining/
## 
## Please cite:
## 
## Guangchuang Yu. Gene Ontology Semantic Similarity Analysis Using
## GOSemSim. In: Kidder B. (eds) Stem Cell Transcriptional Networks.
## Methods in Molecular Biology. 2020, 2117:207-215. Humana, New York, NY.

## 
## Attaching package: 'enrichplot'

## The following object is masked from 'package:GGally':
## 
##     ggtable
## Warning: package 'patchwork' was built under R version 4.5.1
  gene_rank_res |>
    unnest(test) |>
    head() |>
    mutate(plot = pmap(
      list(fit, ID, idx_for_plotting, p.adjust),
      ~ enrichplot::gseaplot2(
        ..1, geneSetID = ..3,
        title = sprintf("%s \nadj pvalue %s", ..2, round(..4, 2)),
        base_size = 6, rel_heights = c(1.5, 0.5), subplots = c(1, 2)
      )
    )) |>
    pull(plot) 
## [[1]]

## 
## [[2]]

## 
## [[3]]

## 
## [[4]]

## 
## [[5]]

## 
## [[6]]

detach("package:enrichplot", unload = TRUE)
## Warning: 'enrichplot' namespace cannot be unloaded:
##   namespace 'enrichplot' is imported by 'clusterProfiler' so cannot be unloaded

Gene Ontology overrepresentation analysis (Ashburner et al. 2000) is used for functional enrichment.

# Test gene overrepresentation
airway_overrep = 
  airway |>
  
  # Label genes to test overrepresentation of
  mutate(genes_to_test = ql__FDR < 0.05) |>
  
    # Filter for genes with entrez IDs
  filter(!entrezid |> is.na()) |>
  
  test_gene_overrepresentation(
    .entrez = entrezid,
    species = "Homo sapiens",
    .do_test = genes_to_test,
    gene_sets = c("H", "C2", "C5")
  )

  airway_overrep
## # A tibble: 5,346 × 13
##    gs_collection ID      Description GeneRatio BgRatio RichFactor FoldEnrichment
##    <chr>         <chr>   <chr>       <chr>     <chr>        <dbl>          <dbl>
##  1 C2            PASINI… PASINI_SUZ… 86/1759   316/22…      0.272           3.46
##  2 C2            CHARAF… CHARAFE_BR… 103/1759  465/22…      0.222           2.82
##  3 C2            CHARAF… CHARAFE_BR… 101/1759  456/22…      0.221           2.82
##  4 C2            REN_AL… REN_ALVEOL… 94/1759   407/22…      0.231           2.94
##  5 C2            ONDER_… ONDER_CDH1… 67/1759   257/22…      0.261           3.32
##  6 C2            CHEN_L… CHEN_LVAD_… 40/1759   102/22…      0.392           4.99
##  7 C2            LIU_PR… LIU_PROSTA… 99/1759   495/22…      0.2             2.54
##  8 C2            BOQUES… BOQUEST_ST… 90/1759   428/22…      0.210           2.68
##  9 C2            LU_AGI… LU_AGING_B… 66/1759   264/22…      0.25            3.18
## 10 C2            LIM_MA… LIM_MAMMAR… 95/1759   479/22…      0.198           2.52
## # ℹ 5,336 more rows
## # ℹ 6 more variables: zScore <dbl>, pvalue <dbl>, p.adjust <dbl>, qvalue <dbl>,
## #   Count <int>, entrez <list>

EGSEA (Alhamdoosh et al. 2017) is used for ensemble gene set enrichment analysis. EGSEA is a method that combines multiple gene set enrichment analysis methods to provide a more robust and comprehensive analysis of gene set enrichment. It creates a web-based interactive tool that allows you to explore the results of the gene set enrichment analysis.

library(EGSEA)
## Loading required package: gage

## Loading required package: AnnotationDbi

## 
## Attaching package: 'AnnotationDbi'

## The following object is masked from 'package:dplyr':
## 
##     select

## Loading required package: topGO

## Warning: package 'topGO' was built under R version 4.5.1

## Loading required package: graph

## Loading required package: GO.db

## Loading required package: SparseM

## 
## groupGOTerms:    GOBPTerm, GOMFTerm, GOCCTerm environments built.

## 
## Attaching package: 'topGO'

## The following object is masked from 'package:gage':
## 
##     geneData

## The following object is masked from 'package:IRanges':
## 
##     members

## Loading required package: pathview

## ##############################################################################
## Pathview is an open source software package distributed under GNU General
## Public License version 3 (GPLv3). Details of GPLv3 is available at
## http://www.gnu.org/licenses/gpl-3.0.html. Particullary, users are required to
## formally cite the original Pathview paper (not just mention it) in publications
## or products. For details, do citation("pathview") within R.
## 
## The pathview downloads and uses KEGG data. Non-academic uses may require a KEGG
## license agreement (details at http://www.kegg.jp/kegg/legal.html).
## ##############################################################################

## 

## 

## 
# Test gene enrichment
  airway |> 

  # Filter for genes with entrez IDs
  filter(!entrezid |> is.na()) |>
  aggregate_duplicates(.transcript = entrezid) |> 

  # Test gene enrichment
  test_gene_enrichment(
    .formula = ~dex,
    .entrez = entrezid,
    species = "human", 
    gene_sets = "h",
    methods = c("roast"),  # Use a more robust method
    cores = 2
  )
## tidybulk says: The design column names are "(Intercept), dexuntrt"

## [1] "Loading MSigDB Gene Sets ... "
## [1] "Loaded gene sets for the collection h ..."
## [1] "Indexed the collection h ..."
## [1] "Created annotation for the collection h ..."

## EGSEA analysis has started

## ##------ Thu Sep 11 15:54:13 2025 ------##

## The argument 'contrast' is recommended to be a matrix object.
## See Vignette or Help.

## Log fold changes are estimated using limma package ...

## limma DE analysis is carried out ...

## EGSEA is running on the provided data and h collection

## ..roast*

## ##------ Thu Sep 11 15:54:15 2025 ------##

## EGSEA analysis took 2.05200000000002 seconds.
## EGSEA analysis has completed
## EGSEA HTML report is being generated ...

## ##------ Thu Sep 11 15:54:15 2025 ------##

## Report pages and figures are being generated for the h collection ...
##    Heat maps are being generated for top-ranked gene sets 
## based on logFC ... 
##    Summary plots are being generated ... 
##    Comparison summary plots are being generated  ...

## ##------ Thu Sep 11 15:55:02 2025 ------##

## EGSEA report generation took 46.962 seconds.
## EGSEA report has been generated.

## # A tibble: 0 × 0
detach("package:EGSEA", unload = TRUE)

Step 6: Cellularity Analysis

CIBERSORT (Newman et al. 2015) is used for cell type deconvolution.

Cellularity deconvolution is a standard approach for estimating the cellular composition of a sample.

Available Deconvolution Methods

The tidybulk package provides several methods for deconvolution:

  • CIBERSORT (Newman et al. 2015): Uses support vector regression to deconvolve cell type proportions. Requires the class, e1071, and preprocessCore packages.
  • LLSR (Newman et al. 2015): Linear Least Squares Regression for deconvolution.
  • EPIC (racle2017epic?): Uses a reference-based approach to estimate cell fractions.
  • MCP-counter (becht2016mcp?): Quantifies the abundance of immune and stromal cell populations.
  • quanTIseq (finotello2019quantiseq?): A computational framework for inferring the immune contexture of tumors.
  • xCell (aran2017xcell?): Performs cell type enrichment analysis.

Example Usage 

airway = 
  airway |> 
  
  filter(!symbol |> is.na()) |> 
  deconvolve_cellularity(method = "cibersort", cores = 1, prefix = "cibersort__", feature_column = "symbol")

For the rest of the methods, you need to install the immunedeconv package.

if (!requireNamespace("immunedeconv")) BiocManager::install("immunedeconv")
airway = 

airway |> 
# Example using LLSR
deconvolve_cellularity(method = "llsr", prefix = "llsr__", feature_column = "symbol") |> 

# Example using EPIC
deconvolve_cellularity(method = "epic", cores = 1, prefix = "epic__") |> 

# Example using MCP-counter
deconvolve_cellularity(method = "mcp_counter", cores = 1, prefix = "mcp__") |> 

# Example using quanTIseq
deconvolve_cellularity(method = "quantiseq", cores = 1, prefix = "quantiseq__") |> 

# Example using xCell
deconvolve_cellularity(method = "xcell", cores = 1, prefix = "xcell__")

Plotting Results

Visualize the cell type proportions as a stacked barplot for each method:

# Visualize CIBERSORT results
airway   |>
  pivot_sample() |>
  dplyr::select(.sample, contains("cibersort__")) |>
  pivot_longer(cols = -1, names_to = "Cell_type_inferred", values_to = "proportion") |>
  ggplot(aes(x = .sample, y = proportion, fill = Cell_type_inferred)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = scales::percent) +
  my_theme +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(title = "CIBERSORT Cell Type Proportions")

 # Repeat similar plotting for LLSR, EPIC, MCP-counter, quanTIseq, and xCell
airway   |>
  pivot_sample() |>
  select(.sample, contains("llsr__")) |>
  pivot_longer(cols = -1, names_to = "Cell_type_inferred", values_to = "proportion") |>
  ggplot(aes(x = .sample, y = proportion, fill = Cell_type_inferred)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = scales::percent) +
  my_theme +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(title = "LLSR Cell Type Proportions")

  airway     |>
  pivot_sample() |>
  select(.sample, contains("epic__")) |>
  pivot_longer(cols = -1, names_to = "Cell_type_inferred", values_to = "proportion") |>
  ggplot(aes(x = .sample, y = proportion, fill = Cell_type_inferred)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = scales::percent) +
  my_theme +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(title = "EPIC Cell Type Proportions")

  airway     |>
  pivot_sample() |>
  select(.sample, contains("mcp__")) |>
  pivot_longer(cols = -1, names_to = "Cell_type_inferred", values_to = "proportion") |>
  ggplot(aes(x = .sample, y = proportion, fill = Cell_type_inferred)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = scales::percent) +
  my_theme +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(title = "MCP-counter Cell Type Proportions")

  airway     |>
  pivot_sample() |>
  select(.sample, contains("quantiseq__")) |>
  pivot_longer(cols = -1, names_to = "Cell_type_inferred", values_to = "proportion") |>
  ggplot(aes(x = .sample, y = proportion, fill = Cell_type_inferred)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = scales::percent) +
  my_theme +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(title = "quanTIseq Cell Type Proportions")

  airway     |>
  pivot_sample() |>
  select(.sample, contains("xcell__")) |>
  pivot_longer(cols = -1, names_to = "Cell_type_inferred", values_to = "proportion") |>
  ggplot(aes(x = .sample, y = proportion, fill = Cell_type_inferred)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = scales::percent) +
  my_theme +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(title = "xCell Cell Type Proportions")

Bibliography

tidybulk allows you to get the bibliography of all methods used in our workflow.

# Get bibliography of all methods used in our workflow
airway |> get_bibliography()
##  @Article{tidybulk,
##   title = {tidybulk: an R tidy framework for modular transcriptomic data analysis},
##   author = {Stefano Mangiola and Ramyar Molania and Ruining Dong and Maria A. Doyle & Anthony T. Papenfuss},
##   journal = {Genome Biology},
##   year = {2021},
##   volume = {22},
##   number = {42},
##   url = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02233-7},
##   }
## @article{wickham2019welcome,
##   title={Welcome to the Tidyverse},
##   author={Wickham, Hadley and Averick, Mara and Bryan, Jennifer and Chang, Winston and McGowan, Lucy D'Agostino and Francois, Romain and Grolemund, Garrett and Hayes, Alex and Henry, Lionel and Hester, Jim and others},
##   journal={Journal of Open Source Software},
##   volume={4},
##   number={43},
##   pages={1686},
##   year={2019}
##  }
## @article{robinson2010edger,
##   title={edgeR: a Bioconductor package for differential expression analysis of digital gene expression data},
##   author={Robinson, Mark D and McCarthy, Davis J and Smyth, Gordon K},
##   journal={Bioinformatics},
##   volume={26},
##   number={1},
##   pages={139--140},
##   year={2010},
##   publisher={Oxford University Press}
##  }
## @article{robinson2010scaling,
##   title={A scaling normalization method for differential expression analysis of RNA-seq data},
##   author={Robinson, Mark D and Oshlack, Alicia},
##   journal={Genome biology},
##   volume={11},
##   number={3},
##   pages={1--9},
##   year={2010},
##   publisher={BioMed Central}
##  }
## @incollection{smyth2005limma,
##   title={Limma: linear models for microarray data},
##   author={Smyth, Gordon K},
##   booktitle={Bioinformatics and computational biology solutions using R and Bioconductor},
##   pages={397--420},
##   year={2005},
##   publisher={Springer}
##  }
## @Manual{,
##     title = {R: A Language and Environment for Statistical Computing},
##     author = {{R Core Team}},
##     organization = {R Foundation for Statistical Computing},
##     address = {Vienna, Austria},
##     year = {2020},
##     url = {https://www.R-project.org/},
##   }
## @article{lund2012detecting,
##   title={Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates},
##   author={Lund, Steven P and Nettleton, Dan and McCarthy, Davis J and Smyth, Gordon K},
##   journal={Statistical applications in genetics and molecular biology},
##   volume={11},
##   number={5},
##   year={2012},
##   publisher={De Gruyter}
##     }
## @article{zhou2014robustly,
##   title={Robustly detecting differential expression in RNA sequencing data using observation weights},
##   author={Zhou, Xiaobei and Lindsay, Helen and Robinson, Mark D},
##   journal={Nucleic acids research},
##   volume={42},
##   number={11},
##   pages={e91--e91},
##   year={2014},
##   publisher={Oxford University Press}
##  }
## @article{love2014moderated,
##   title={Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2},
##   author={Love, Michael I and Huber, Wolfgang and Anders, Simon},
##   journal={Genome biology},
##   volume={15},
##   number={12},
##   pages={550},
##   year={2014},
##   publisher={Springer}
##  }
## @article{law2014voom,
##   title={voom: Precision weights unlock linear model analysis tools for RNA-seq read counts},
##   author={Law, Charity W and Chen, Yunshun and Shi, Wei and Smyth, Gordon K},
##   journal={Genome biology},
##   volume={15},
##   number={2},
##   pages={R29},
##   year={2014},
##   publisher={Springer}
##     }
## @article{liu2015weight,
##   title={Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses},
##   author={Liu, Ruijie and Holik, Aliaksei Z and Su, Shian and Jansz, Natasha and Chen, Kelan and Leong, Huei San and Blewitt, Marnie E and Asselin-Labat, Marie-Liesse and Smyth, Gordon K and Ritchie, Matthew E},
##   journal={Nucleic acids research},
##   volume={43},
##   number={15},
##   pages={e97--e97},
##   year={2015},
##   publisher={Oxford University Press}
##     }
## @article{leek2012sva,
##   title={The sva package for removing batch effects and other unwanted variation in high-throughput experiments},
##   author={Leek, Jeffrey T and Johnson, W Evan and Parker, Hilary S and Jaffe, Andrew E and Storey, John D},
##   journal={Bioinformatics},
##   volume={28},
##   number={6},
##   pages={882--883},
##   year={2012},
##   publisher={Oxford University Press}
##  }
## @article{newman2015robust,
##   title={Robust enumeration of cell subsets from tissue expression profiles},
##   author={Newman, Aaron M and Liu, Chih Long and Green, Michael R and Gentles, Andrew J and Feng, Weiguo and Xu, Yue and Hoang, Chuong D and Diehn, Maximilian and Alizadeh, Ash A},
##   journal={Nature methods},
##   volume={12},
##   number={5},
##   pages={453--457},
##   year={2015},
##   publisher={Nature Publishing Group}
##  }
## R version 4.5.0 (2025-04-11)
## Platform: x86_64-apple-darwin20
## Running under: macOS Sonoma 14.6.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Australia/Adelaide
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] pathview_1.48.0                 topGO_2.60.1                   
##  [3] SparseM_1.84-2                  GO.db_3.21.0                   
##  [5] graph_1.86.0                    AnnotationDbi_1.70.0           
##  [7] gage_2.58.0                     patchwork_1.3.2                
##  [9] GGally_2.3.0                    DESeq2_1.48.1                  
## [11] edgeR_4.6.3                     limma_3.64.3                   
## [13] tidySummarizedExperiment_1.19.7 airway_1.28.0                  
## [15] SummarizedExperiment_1.38.1     Biobase_2.68.0                 
## [17] GenomicRanges_1.60.0            GenomeInfoDb_1.44.2            
## [19] IRanges_2.42.0                  S4Vectors_0.46.0               
## [21] BiocGenerics_0.54.0             generics_0.1.4                 
## [23] MatrixGenerics_1.20.0           matrixStats_1.5.0              
## [25] tidybulk_1.99.2                 ttservice_0.5.3                
## [27] ggrepel_0.9.6                   ggplot2_3.5.2                  
## [29] magrittr_2.0.3                  purrr_1.1.0                    
## [31] tibble_3.3.0                    tidyr_1.3.1                    
## [33] dplyr_1.1.4                     knitr_1.50                     
## 
## loaded via a namespace (and not attached):
##   [1] SpatialExperiment_1.18.1    R.methodsS3_1.8.2          
##   [3] GSEABase_1.70.0             DT_0.33                    
##   [5] Biostrings_2.76.0           HDF5Array_1.36.0           
##   [7] TH.data_1.1-3               vctrs_0.6.5                
##   [9] ggtangle_0.0.7              digest_0.6.37              
##  [11] png_0.1-8                   proxy_0.4-27               
##  [13] org.Mm.eg.db_3.21.0         magick_2.8.7               
##  [15] MASS_7.3-65                 reshape2_1.4.4             
##  [17] foreach_1.5.2               qvalue_2.40.0              
##  [19] withr_3.0.2                 xfun_0.53                  
##  [21] ggfun_0.2.0                 ggpubr_0.6.1               
##  [23] ellipsis_0.3.2              survival_3.8-3             
##  [25] doRNG_1.8.6.2               memoise_2.0.1              
##  [27] HTMLUtils_0.1.9             clusterProfiler_4.16.0     
##  [29] emmeans_1.11.2              gson_0.1.0                 
##  [31] hgu133a.db_3.13.0           tidytree_0.4.6             
##  [33] zoo_1.8-14                  gtools_3.9.5               
##  [35] KEGGgraph_1.68.0            pbapply_1.7-4              
##  [37] R.oo_1.27.1                 Formula_1.2-5              
##  [39] KEGGREST_1.48.1             httr_1.4.7                 
##  [41] rstatix_0.7.2               rhdf5filters_1.20.0        
##  [43] rhdf5_2.52.1                rstudioapi_0.17.1          
##  [45] UCSC.utils_1.4.0            DOSE_4.2.0                 
##  [47] babelgene_22.9              curl_7.0.0                 
##  [49] ScaledMatrix_1.16.0         h5mread_1.0.1              
##  [51] TFisher_0.2.0               GenomeInfoDbData_1.2.14    
##  [53] SparseArray_1.8.1           xtable_1.8-4               
##  [55] stringr_1.5.1               evaluate_1.0.5             
##  [57] S4Arrays_1.8.1              preprocessCore_1.70.0      
##  [59] irlba_2.3.5.1               colorspace_2.1-1           
##  [61] Rgraphviz_2.52.0            ggtree_3.16.3              
##  [63] lattice_0.22-7              genefilter_1.90.0          
##  [65] XML_3.99-0.18               cowplot_1.2.0              
##  [67] class_7.3-23                pillar_1.11.0              
##  [69] nlme_3.1-168                iterators_1.0.14           
##  [71] caTools_1.18.3              compiler_4.5.0             
##  [73] beachmat_2.24.0             stringi_1.8.7              
##  [75] KEGGdzPathwaysGEO_1.46.0    tokenizers_0.3.0           
##  [77] minqa_1.2.8                 plyr_1.8.9                 
##  [79] msigdbr_25.1.1              crayon_1.5.3               
##  [81] abind_1.4-8                 gridGraphics_0.5-1         
##  [83] sn_2.1.1                    locfit_1.5-9.12            
##  [85] org.Hs.eg.db_3.21.0         bit_4.6.0                  
##  [87] mathjaxr_1.8-0              sandwich_3.1-1             
##  [89] fastmatch_1.1-6             codetools_0.2-20           
##  [91] multcomp_1.4-28             BiocSingular_1.24.0        
##  [93] e1071_1.7-16                plotly_4.11.0              
##  [95] hgu133plus2.db_3.13.0       tidytext_0.4.2             
##  [97] multtest_2.64.0             org.Rn.eg.db_3.21.0        
##  [99] splines_4.5.0               Rcpp_1.1.0                 
## [101] sparseMatrixStats_1.20.0    blob_1.2.4                 
## [103] utf8_1.2.6                  lme4_1.1-37                
## [105] fs_1.6.6                    Rdpack_2.6.4               
## [107] GSVA_2.2.0                  ggsignif_0.6.4             
## [109] ggplotify_0.1.2             estimability_1.5.1         
## [111] Matrix_1.7-3                statmod_1.5.0              
## [113] pkgconfig_2.0.3             tools_4.5.0                
## [115] cachem_1.1.0                rbibutils_2.3              
## [117] RSQLite_2.4.3               viridisLite_0.4.2          
## [119] globaltest_5.62.0           DBI_1.2.3                  
## [121] numDeriv_2016.8-1.1         fastmap_1.2.0              
## [123] rmarkdown_2.29              scales_1.4.0               
## [125] grid_4.5.0                  pbmcapply_1.5.1            
## [127] metap_1.12                  broom_1.0.8                
## [129] coda_0.19-4.1               ggstats_0.10.0             
## [131] carData_3.0-5               farver_2.1.2               
## [133] reformulas_0.4.1            mgcv_1.9-3                 
## [135] yaml_2.3.10                 cli_3.6.5                  
## [137] safe_3.48.0                 lifecycle_1.0.4            
## [139] glmmTMB_1.1.11              mvtnorm_1.3-3              
## [141] backports_1.5.0             BiocParallel_1.42.1        
## [143] widyr_0.1.5                 annotate_1.86.1            
## [145] gtable_0.3.6                rjson_0.2.23               
## [147] parallel_4.5.0              ape_5.8-1                  
## [149] SnowballC_0.7.1             jsonlite_2.0.0             
## [151] bitops_1.0-9                bit64_4.6.0-1              
## [153] assertthat_0.2.1            qqconf_1.3.2               
## [155] yulab.utils_0.2.0           PADOG_1.50.0               
## [157] mutoss_0.1-13               janeaustenr_1.0.0          
## [159] GOSemSim_2.34.0             R.utils_2.13.0             
## [161] lazyeval_0.2.2              htmltools_0.5.8.1          
## [163] enrichplot_1.28.4           glue_1.8.0                 
## [165] XVector_0.48.0              RCurl_1.98-1.17            
## [167] rprojroot_2.1.0             treeio_1.32.0              
## [169] mnormt_2.1.1                boot_1.3-31                
## [171] igraph_2.1.4                TMB_1.9.17                 
## [173] glmmSeq_0.5.5               R6_2.6.1                   
## [175] sva_3.56.0                  SingleCellExperiment_1.30.1
## [177] gplots_3.2.0                labeling_0.4.3             
## [179] rngtools_1.5.2              R2HTML_2.3.4               
## [181] Rhdf5lib_1.30.0             aplot_0.2.8                
## [183] nloptr_2.2.1                DelayedArray_0.34.1        
## [185] tidyselect_1.2.1            plotrix_3.8-4              
## [187] car_3.1-3                   rsvd_1.0.5                 
## [189] KernSmooth_2.23-26          S7_0.2.0                   
## [191] data.table_1.17.8           htmlwidgets_1.6.4          
## [193] fgsea_1.34.2                RColorBrewer_1.1-3         
## [195] hwriter_1.3.2.1             rlang_1.1.6                
## [197] lmerTest_3.1-3              GSA_1.03.3                 
## [199] EGSEAdata_1.36.0
Alhamdoosh, M, M Ng, NJ Wilson, JM Sheridan, H Huynh, MJ Wilson, and ME Ritchie. 2017. “Combining Multiple Tools Outperforms Individual Methods in Gene Set Enrichment Analyses.” Bioinformatics 33 (3): 414–24.
Anders, Simon, and Wolfgang Huber. 2010. “Differential Expression Analysis for Sequence Count Data.” Genome Biology 11 (10): R106.
Ashburner, Michael, Catherine A Ball, Judith A Blake, David Botstein, Heather Butler, J Michael Cherry, J Michael Davis, et al. 2000. “Gene Ontology: Tool for the Unification of Biology.” Nature Genetics 25 (1): 25–29.
Bullard, James H, Elizabeth Purdom, Kasper D Hansen, and Sandrine Dudoit. 2010. “Evaluation of Statistical Methods for Normalization and Differential Expression in mRNA-Seq Experiments.” BMC Bioinformatics 11 (1): 94.
Chen, Yunshun, Aaron TL Lun, and Gordon K Smyth. 2016. “From Reads to Genes to Pathways: Differential Expression Analysis of RNA-Seq Experiments Using Rsubread and the edgeR Quasi-Likelihood Pipeline.” F1000Research 5.
Hotelling, Harold. 1933. “Analysis of a Complex of Statistical Variables into Principal Components.” Journal of Educational Psychology 24 (6): 417.
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