tidybulk: An R tidy framework for modular transcriptomic data analysis

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 example 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)

Comparison of Differential Expression methods

# 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__") 

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 |>
  pivot_transcript() |> 
  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 |> pivot_transcript() |> 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)

library(GGally)

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

airway |> 
  pivot_transcript() |> 
  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")

Pairplot of effect sizes across methods (GGpairs)

library(GGally)
airway |> 
  pivot_transcript() |> 
  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")

Quality Control

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 is stored as an attribute 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 in understanding the dispersion of the data.

library(edgeR)

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 the mean plot.

library(DESeq2)

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

Volcano Plots for Each Method

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

# Create volcano plots
airway |>

    # Select the columns we want to plot
    pivot_transcript() |> 
    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.

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

Automatic 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}
##  }