- Introduction
- Installation
- Example Usage
- Session Info
- References
- How to Cite the
muleaPackage? - Code of Conduct
Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis.
mulea employs an innovative empirical false discovery rate (eFDR) correction method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. Beyond conventional tools, mulea incorporates a wide range of ontologies encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility empowers researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets.
To facilitate seamless analysis, mulea provides gene sets (in standardized GMT format) for 27 model organisms, covering 16 databases and various identifiers. The GMT files and the scripts we applied to create them are available at the GMT_files_for_mulea repository. Additionally, the muleaData ExperimentData Bioconductor R package simplifies access to these 879 pre-defined ontologies. Furthermore, mulea’s architecture allows for easy integration of user-defined ontologies, expanding its applicability across diverse research areas.
After installing the BiocManager package, you can install fgsea, a dependency for mulea from Bioconductor. Then, you can install mulea from this github repo using the install_github function of the devtools package:
# installing the BiocManager package if needed
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# installing the fgsea package from Bioconductor
BiocManager::install("fgsea")
# installing the devtools package if needed
if (!require("devtools", quietly = TRUE))
install.packages("devtools")
# installing the mulea package from GitHub
devtools::install_github("https://github.com/ELTEbioinformatics/mulea")This section demonstrates how to use mulea with a sample dataset. If you have your own data, feel free to skip this part and proceed directly to the OverRepresentation Analysis (ORA) or Gene Set Enrichment Analysis (GSEA) sections.
This example analyses a differential expression (DE) dataset from a microarray experiment deposited in the NCBI Gene Expression Omnibus 
under accession number GSE55662. The original study by Méhi et al. (2014), investigated the evolution of antibiotic resistance in Escherichia coli bacteria. The authors compared gene expression changes in ciprofloxacin-treated bacteria to non-treated controls.
The GEO2R tool was used for differential expression analysis, comparing:
- Non-treated control samples (2 replicates)
- Samples treated with ciprofloxacin (2 replicates)
This section would typically describe the format and key elements of the provided DE results table, guiding users on how to interpret the data for further analysis with mulea.
library(tidyverse)
geo2r_result_tab <- read_tsv("GSE55662.table_wt_non_vs_cipro.tsv")We take a closer look at the first few rows of the geo2r_result_tab data frame:
| ID | adj.P.Val | P.Value | t | B | logFC | Gene.symbol | Gene.title |
|---|---|---|---|---|---|---|---|
| 1765336_s_at | 0.0186 | 2.4e-06 | 21.5 | 4.95769 | 3.70 | gnsB | Qin prophage; multicopy suppressor of secG(Cs) and fabA6(Ts) |
| 1760422_s_at | 0.0186 | 3.8e-06 | 19.6 | 4.68510 | 3.14 | NA | NA |
| 1764904_s_at | 0.0186 | 5.7e-06 | 18.2 | 4.43751 | 2.54 | sulA///sulA///sulA///ECs1042 | SOS cell division inhibitor///SOS cell division inhibitor///SOS cell division inhibitor///SOS cell division inhibitor |
It’s important to format the data frame appropriately for enrichment analysis. This process often involves steps specific to the type of microarray experiment conducted. In this case, we perform the following transformations:
- Extract Gene Symbol: We extract the main gene symbol from the
Gene.symbolcolumn, removing any additional information that might be present. - Remove Missing Values: We remove rows where the gene symbol is missing (
NA). - Order by Fold Change: We sort the data frame by the log-fold change (
logFC) in descending order, prioritizing genes with the most significant expression changes.
geo2r_result_tab %<>%
# extracting the first gene symbol from the Gene.symbol column
mutate(Gene.symbol = str_remove(string = Gene.symbol,
pattern = "\\/.*")) %>%
# removing rows where Gene.symbol is NA
filter(!is.na(Gene.symbol)) %>%
# ordering by logFC
arrange(desc(logFC))Before proceeding with enrichment analysis, we take a closer look at the first few rows of the formatted geo2r_result_tab data frame:
| ID | adj.P.Val | P.Value | t | B | logFC | Gene.symbol | Gene.title |
|---|---|---|---|---|---|---|---|
| 1765336_s_at | 0.0186 | 2.40e-06 | 21.5 | 4.95769 | 3.70 | gnsB | Qin prophage; multicopy suppressor of secG(Cs) and fabA6(Ts) |
| 1764904_s_at | 0.0186 | 5.70e-06 | 18.2 | 4.43751 | 2.54 | sulA | SOS cell division inhibitor///SOS cell division inhibitor///SOS cell division inhibitor///SOS cell division inhibitor |
| 1761763_s_at | 0.0186 | 1.54e-05 | 15.0 | 3.73568 | 2.16 | recN | recombination and repair protein///recombination and repair protein///recombination and repair protein///recombination and repair protein |
After applying these formatting steps, the data frame is ready for further analysis.
This section explores the transcription factors influencing the significantly overexpressed genes. We employed the mulea package to conduct multiple enrichment analyses using the 
database.
The analysis utilized a GMT file downloaded from the dedicated GMT_files_for_mulea GitHub repository. This file associates gene symbols with the transcription factors that regulate them.
The GMT file contains lists of gene symbols regulated by specific transcription factors, also identified by gene symbols within the file. We proceed to parse and analyse this data to uncover the regulatory relationships.
We can download the required GMT file from the repository using the website and then read the file. For this we need to call the mulea package.
library(mulea)
tf_gmt <- read_gmt("Transcription_factor_RegulonDB_Escherichia_coli_GeneSymbol.gmt")Or we can read it directly from the GitHub repo:
library(mulea)
tf_gmt <- read_gmt("https://raw.githubusercontent.com/ELTEbioinformatics/GMT_files_for_mulea/main/GMT_files/Escherichia_coli_83333/Transcription_factor_RegulonDB_Escherichia_coli_GeneSymbol.gmt")How many transcription factors it contains?
nrow(tf_gmt)
#> [1] 211The first 3 rows of the tf_gmt:
| ontology_id | ontology_name | list_of_values |
|---|---|---|
| AccB | AccB | accC, accB |
| AcrR | AcrR | marB, ma…. |
| Ada | Ada | alkB, ad…. |
Important Note: The format of this GMT differs slightly from standard GMT files. In the tf_gmt, both the ontology_id and ontology_name columns contain gene symbols of the transcription factors, unlike other GMT files like GO, where these columns hold specific identifiers and corresponding names.
Each line in the file represents a group of genes regulated by a specific transcription factor. The list_of_values column lists the gene symbols under the control of the transcription factor mentioned in the ontology_id column.
For example, to see all genes regulated by the transcription factor “AcrR”, you can use the following code:
tf_gmt %>%
# filtering the row where the ontology_id is AcrR
filter(ontology_id == "AcrR") %>%
# selecting the list_of_values column
select(list_of_values) %>%
# converting tibble to vector
pull()
#> [[1]]
#> [1] "marB" "marR" "marA" "acrB" "micF" "flhD" "acrR" "flhC" "acrA" "soxS"
#> [11] "soxR"Enrichment analysis results can sometimes be skewed by overly specific or broad entries. mulea allows you to customize the size of ontology entries, ensuring your analysis aligns with your desired scope.
Analysing Entry Distribution:
Let’s examine the distribution of the number of gene symbols in the list_of_values column to identify entries requiring exclusion:
Nr_of_elements_in_ontology <- tf_gmt$list_of_values %>%
map_dbl(length)
ggplot(mapping = aes(Nr_of_elements_in_ontology)) +
geom_bar() +
theme_minimal()
This plot reveals entries containing over 200 gene symbols, indicating these transcription factors regulate too many genes, making them overly broad. We’ll exclude them from the analysis.
Conversely, some entries hold a very small number of elements. Let’s zoom in:
ggplot(mapping = aes(Nr_of_elements_in_ontology)) +
geom_bar() +
xlim(0, 15) +
theme_minimal()
Filtering Entries:
Based on our observations, we’ll exclude entries with less than 3 or more than 400 gene symbols. Let’s check the remaining number of transcription factors:
tf_gmt_filtered <- filter_ontology(gmt = tf_gmt,
min_nr_of_elements = 3,
max_nr_of_elements = 400)Results:
We can now determine the number of transcription factors remaining in the filtered dataset:
nrow(tf_gmt_filtered)
#> [1] 154It is possible to write the filtered ontology as a GMT file using the write_gmt function.
write_gmt(gmt = tf_gmt_filtered,
file = "Filtered.gmt")The mulea package provides a function to convert a list of gene sets to an ontology (GMT) object. This function is called list_to_gmt. The following example demonstrates how to use this function:
# creating a list of gene sets
ontology_list <- list(gene_set1 = c("gene1", "gene2", "gene3"),
gene_set2 = c("gene4", "gene5", "gene6"))
# converting the list to a ontology (GMT) object
new_ontology_object <- list_to_gmt(ontology_list)This approach analyses groups of genes (sets) to identify if they are enriched in specific categories – transcription factors – within a reference set. It requires two key elements:
-
Target set: This contains genes of interest, such as significantly overexpressed genes in our experiment.
-
Background set: This represents the broader context, often including all genes investigated in our study.
To ensure meaningful results, a clear threshold needs to be applied beforehand. This could involve filtering genes based on corrected p-values, z-scores (commonly set at 0.05), or fold-change values (e.g., a minimum 2-fold change).
mulea utilizes the hypergeometric test to assess overrepresentation within categories. This test is similar to the lower-tailed Fisher’s exact test and helps determine if the observed enrichment is statistically significant by considering both the target and background sets.
A vector containing the gene symbols of significantly overexpressed (adjusted p-value < 0.05) genes with greater than 2 fold-change (logFC > 1).
sign_genes <- geo2r_result_tab %>%
# filtering for adjusted p-value < 0.05 and logFC > 1
filter(adj.P.Val < 0.05 & logFC > 1) %>%
# selecting the Gene.symbol column
select(Gene.symbol) %>%
# converting the tibble to a vector
pull() %>%
# removing duplicates
unique()The first 10 elements of the target set:
sign_genes %>%
head(10)
#> [1] "gnsB" "sulA" "recN" "c4435" "dinI" "c2757" "c1431"
#> [8] "gabP" "recA" "ECs5456"The number of genes in the target set:
sign_genes %>%
length()
#> [1] 241A vector containing the gene symbols of all genes were included in the differential expression analysis.
background_genes <- geo2r_result_tab %>%
# selecting the Gene.symbol column
select(Gene.symbol) %>%
# convertin the tibble to a vector
pull() %>%
# removing duplicates
unique()The number of genes in the background set:
background_genes %>%
length()
#> [1] 7381To perform the analysis, we will first establish a model using the ora function. This model defines the parameters for the enrichment analysis. Subsequently, we will execute the test itself using the run_test function. It is important to note that for this example, we will employ 10,000 permutations for the empirical false discovery rate correction, which is the recommended minimum, to ensure robust correction for multiple testing.
# creating the ORA model using the GMT variable
ora_model <- ora(gmt = tf_gmt_filtered,
# the test set variable
element_names = sign_genes,
# the background set variable
background_element_names = background_genes,
# the p-value adjustment method
p_value_adjustment_method = "eFDR",
# the number of permutations
number_of_permutations = 10000,
# the number of processor threads to use
nthreads = 2)
# running the ORA
ora_results <- run_test(ora_model)The ora_results data frame summarizes the enrichment analysis, listing enriched ontology entries – in our case transcription factors – alongside their associated p-values and empirical FDR values. We can now determine the number of transcription factors classified as “enriched” based on these statistical measures (eFDR < 0.05).
ora_results %>%
# rows where the eFDR < 0.05
filter(eFDR < 0.05) %>%
# the number of such rows
nrow()
#> [1] 10And inspect the significant results:
ora_results %>%
# arrange the rows by the eFDR values
arrange(eFDR) %>%
# rows where the eFDR < 0.05
filter(eFDR < 0.05)| ontology_id | ontology_name | nr_common_with_tested_elements | nr_common_with_background_elements | p_value | eFDR |
|---|---|---|---|---|---|
| LexA | LexA | 14 | 53 | 0.0000000 | 0.0000000 |
| FNR | FNR | 26 | 259 | 0.0000003 | 0.0000500 |
| SoxS | SoxS | 7 | 37 | 0.0001615 | 0.0026000 |
| Rob | Rob | 5 | 21 | 0.0004717 | 0.0049200 |
| DnaA | DnaA | 4 | 13 | 0.0006281 | 0.0050500 |
| FadR | FadR | 5 | 20 | 0.0003692 | 0.0053500 |
| NsrR | NsrR | 8 | 64 | 0.0010478 | 0.0070571 |
| ArcA | ArcA | 12 | 148 | 0.0032001 | 0.0195125 |
| IHF | IHF | 14 | 205 | 0.0070758 | 0.0449800 |
| MarA | MarA | 5 | 37 | 0.0066068 | 0.0475111 |
For a more comprehensive understanding of the enriched transcription factors, mulea provides diverse visualization tools, including lollipop charts, networks, and heatmaps. These visualizations can effectively reveal patterns and relationships among the enriched factors.
Initializing the visualization with the reshape_results function:
ora_reshaped_results <- reshape_results(model = ora_model,
model_results = ora_results,
# choosing which column to use for the
# indication of significance
p_value_type_colname = "eFDR")Visualizing the spread of eFDR values: Lollipop plot
Lollipop charts offer a graphical representation of the distribution of enriched transcription factors. The y-axis displays the transcription factors, while the x-axis represents their corresponding eFDR values. The dots are is coloured based on their significance level. This visualization helps us examine the spread of eFDRs and identify factors exceeding the commonly used significance threshold of 0.05.
plot_lollipop(reshaped_results = ora_reshaped_results,
# the column containing the names we wish to plot
ontology_id_colname = "ontology_id",
# upper threshold for the value indicating the significance
p_value_max_threshold = 0.05,
# column that indicates the significance values
p_value_type_colname = "eFDR")
Visualizing the spread of eFDR values: Bar plot
Bar charts offer very similar graphical representation of the distribution of enriched transcription factors as the lollipop plot. The y-axis displays the transcription factors, while the x-axis represents their corresponding eFDR values. The bars are is coloured based on their significance level. This visualization helps us examine the spread of eFDRs and identify factors exceeding the commonly used significance threshold of 0.05.
plot_barplot(reshaped_results = ora_reshaped_results,
# the column containing the names we wish to plot
ontology_id_colname = "ontology_id",
# upper threshold for the value indicating the significance
p_value_max_threshold = 0.05,
# column that indicates the significance values
p_value_type_colname = "eFDR")
Visualizing Relationships: Network Plot
This function generates a network visualization of the enriched transcription factors. Each node represents a transcription factor and is coloured based on its significance level. A connection (edge) is drawn between two nodes if they share at least one common gene belonging to the target set, meaning that both transcription factors regulate the expression of the same target gene. The thickness of the edge reflects the number of shared genes belonging to the target set.
plot_graph(reshaped_results = ora_reshaped_results,
# the column containing the names we wish to plot
ontology_id_colname = "ontology_id",
# upper threshold for the value indicating the significance
p_value_max_threshold = 0.05,
# column that indicates the significance values
p_value_type_colname = "eFDR")
Heatmap
The heatmap displays the genes associated with the enriched transcription factors. Each row represents a transcription factor and is coloured based on its significance level. Each column represents a target gene belonging to the target set that is potentially regulated by one or more of the enriched transcription factors.
plot_heatmap(reshaped_results = ora_reshaped_results,
# the column containing the names we wish to plot
ontology_id_colname = "ontology_id",
# column that indicates the significance values
p_value_type_colname = "eFDR")
To perform enrichment analysis using ranked lists, you need to provide an ordered list of elements, such as genes, transcripts, or proteins. This ranking is typically determined by the results of your prior analysis, potentially based on factors like p-values, z-scores, fold-changes, or others. Crucially, the ranked list should include all elements involved in your analysis. For instance, in a differential expression study, it should encompass all genes that were measured.
mulea utilizes the Kolmogorov-Smirnov approach with a permutation test (developed by (Subramanian et al. 2005)) to calculate gene set enrichment analyses. This functionality is implemented through the integration of the fgsea Bioconductor R package (created by (Korotkevich et al. 2021)).
GSEA requires input data about the genes analysed in our experiment. This data can be formatted in two ways:
-
Data frame: This format should include all genes investigated and their respective log fold change values (or other values for ordering the genes) obtained from the differential expression analysis.
-
Two vectors: Alternatively, you can provide two separate vectors. One vector should contain the gene symbols (or IDs), and the other should hold the corresponding log fold change values (or other values for ordering the genes) for each gene.
# if there are duplicated Gene.symbols keep the first one only
geo2r_result_tab_filtered <- geo2r_result_tab %>%
# grouping by Gene.symbol to be able to filter
group_by(Gene.symbol) %>%
# keeping the first row for each Gene.symbol from rows with the same
# Gene.symbol
filter(row_number()==1) %>%
# ungrouping
ungroup() %>%
# arranging by logFC in descending order
arrange(desc(logFC)) %>%
select(Gene.symbol, logFC)The number of gene symbols in the geo2r_result_tab_filtered vector:
geo2r_result_tab_filtered %>%
nrow()
#> [1] 7381To perform the analysis, we will first establish a model using the gsea function. This model defines the parameters for the enrichment analysis. Subsequently, we will execute the test itself using the run_test function. We will employ 10,000 permutations for the false discovery rate correction, to ensure robust correction for multiple testing.
# creating the GSEA model using the GMT variable
gsea_model <- gsea(gmt = tf_gmt_filtered,
# the names of elements to test
element_names = geo2r_result_tab_filtered$Gene.symbol,
# the logFC-s of elements to test
element_scores = geo2r_result_tab_filtered$logFC,
# consider elements having positive logFC values only
element_score_type = "pos",
# the number of permutations
number_of_permutations = 10000)
# running the GSEA
gsea_results <- run_test(gsea_model)The gsea_results data frame summarizes the enrichment analysis, listing enriched ontology entries – in our case transcription factors – alongside their associated p-values and adjusted p-value values. We can now determine the number of transcription factors classified as “enriched” based on these statistical measures (adjusted p-value < 0.05).
gsea_results %>%
# rows where the adjusted_p_value < 0.05
filter(adjusted_p_value < 0.05) %>%
# the number of such rows
nrow()
#> [1] 8And inspect the significant results:
gsea_results %>%
# arrange the rows by the adjusted_p_value values
arrange(adjusted_p_value) %>%
# rows where the adjusted_p_value < 0.05
filter(adjusted_p_value < 0.05)| ontology_id | ontology_name | nr_common_with_tested_elements | p_value | adjusted_p_value |
|---|---|---|---|---|
| LexA | LexA | 53 | 0.0000000 | 0.0000058 |
| FNR | FNR | 259 | 0.0000874 | 0.0066880 |
| ArcA | ArcA | 148 | 0.0003122 | 0.0108648 |
| GlaR | GlaR | 3 | 0.0003668 | 0.0108648 |
| ModE | ModE | 45 | 0.0002575 | 0.0108648 |
| SoxS | SoxS | 37 | 0.0004261 | 0.0108648 |
| DnaA | DnaA | 13 | 0.0013530 | 0.0295728 |
| PspF | PspF | 7 | 0.0020534 | 0.0392715 |
Initializing the visualization with the reshape_results function:
gsea_reshaped_results <- reshape_results(model = gsea_model,
model_results = gsea_results,
# choosing which column to use for the
# indication of significance
p_value_type_colname = "adjusted_p_value")Visualizing the spread of adjusted p-values: Lollipop plot
Lollipop charts offer a graphical representation of the distribution of enriched transcription factors. The y-axis displays the transcription factors, while the x-axis represents their corresponding adjusted p-values. The dots are is coloured based on their significance level. This visualization helps us examine the spread of adjusted p-values and identify factors exceeding the commonly used significance threshold of 0.05.
plot_lollipop(reshaped_results = gsea_reshaped_results,
# the column containing the names we wish to plot
ontology_id_colname = "ontology_id",
# upper threshold for the value indicating the significance
p_value_max_threshold = 0.05,
# column that indicates the significance values
p_value_type_colname = "adjusted_p_value")
Visualizing the spread of adjusted p-values: Bar plot
Bar charts offer very similar graphical representation of the distribution of enriched transcription factors as the lollipop plot. The y-axis displays the transcription factors, while the x-axis represents their corresponding adjusted p-values. The bars are is coloured based on their significance level. This visualization helps us examine the spread of adjusted p-values and identify factors exceeding the commonly used significance threshold of 0.05.
plot_barplot(reshaped_results = gsea_reshaped_results,
# the column containing the names we wish to plot
ontology_id_colname = "ontology_id",
# upper threshold for the value indicating the significance
p_value_max_threshold = 0.05,
# column that indicates the significance values
p_value_type_colname = "adjusted_p_value")
Visualizing Relationships: Network Plot
This function generates a network visualization of the enriched transcription factors. Each node represents a transcription factor and is coloured based on its significance level. A connection (edge) is drawn between two nodes if they share at least one common gene belonging to the ranked list, meaning that both transcription factors regulate the expression of the same target gene. The thickness of the edge reflects the number of shared genes belonging to the ranked list.
plot_graph(reshaped_results = gsea_reshaped_results,
# the column containing the names we wish to plot
ontology_id_colname = "ontology_id",
# upper threshold for the value indicating the significance
p_value_max_threshold = 0.05,
# column that indicates the significance values
p_value_type_colname = "adjusted_p_value")
Heatmap
The heatmap displays the genes associated with the enriched transcription factors. Each row represents a transcription factor and is coloured based on its significance level. Each column represents a target gene belonging to the ranked list that is potentially regulated by one or more of the enriched transcription factors. There are too many genes belonging to each transcription factor, therefore heatmap visualization is less optimal in this case.
plot_heatmap(reshaped_results = gsea_reshaped_results,
# the column containing the names we wish to plot
ontology_id_colname = "ontology_id",
# column that indicates the significance values
p_value_type_colname = "adjusted_p_value")
sessionInfo()
#> R version 4.3.2 (2023-10-31)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.4 LTS
#>
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#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
#>
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#>
#> time zone: Europe/Budapest
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] mulea_0.99.10 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
#> [5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
#> [9] tibble_3.2.1 ggplot2_3.5.0 tidyverse_2.0.0
#>
#> loaded via a namespace (and not attached):
#> [1] fastmatch_1.1-4 gtable_0.3.4 xfun_0.42
#> [4] ggrepel_0.9.5 lattice_0.22-5 tzdb_0.4.0
#> [7] vctrs_0.6.5 tools_4.3.2 generics_0.1.3
#> [10] parallel_4.3.2 fansi_1.0.6 highr_0.10
#> [13] pkgconfig_2.0.3 Matrix_1.6-5 data.table_1.15.2
#> [16] lifecycle_1.0.4 compiler_4.3.2 farver_2.1.1
#> [19] tictoc_1.2 munsell_0.5.0 ggforce_0.4.2
#> [22] fgsea_1.28.0 graphlayouts_1.1.0 codetools_0.2-19
#> [25] htmltools_0.5.7 yaml_2.3.8 pillar_1.9.0
#> [28] crayon_1.5.2 MASS_7.3-60.0.1 BiocParallel_1.36.0
#> [31] cachem_1.0.8 viridis_0.6.5 tidyselect_1.2.0
#> [34] digest_0.6.34 stringi_1.8.3 labeling_0.4.3
#> [37] cowplot_1.1.3 polyclip_1.10-6 fastmap_1.1.1
#> [40] grid_4.3.2 colorspace_2.1-0 cli_3.6.2
#> [43] magrittr_2.0.3 ggraph_2.2.0 tidygraph_1.3.1
#> [46] utf8_1.2.4 withr_3.0.0 scales_1.3.0
#> [49] bit64_4.0.5 timechange_0.3.0 rmarkdown_2.25
#> [52] igraph_2.0.2 bit_4.0.5 gridExtra_2.3
#> [55] hms_1.1.3 memoise_2.0.1 evaluate_0.23
#> [58] knitr_1.45 viridisLite_0.4.2 rlang_1.1.3
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#> [64] rstudioapi_0.15.0 vroom_1.6.5 R6_2.5.1
#> [67] plyr_1.8.9Korotkevich, Gennady, Vladimir Sukhov, Nikolay Budin, Boris Shpak, Maxim N. Artyomov, and Alexey Sergushichev. 2021. “Fast Gene Set Enrichment Analysis,” February. https://doi.org/10.1101/060012.
Subramanian, Aravind, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, et al. 2005. “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.” Proceedings of the National Academy of Sciences 102 (43): 15545–50. https://doi.org/10.1073/pnas.0506580102.
To cite package mulea in publications use:
C. Turek, M. Olbei, T. Stirling, G. Fekete, E. Tasnadi, L. Gul, B. Bohar, B. Papp, W. Jurkowski, E. Ari: mulea - an R package for enrichment analysis using multiple ontologies and empirical FDR correction. bioRxiv (2024), doi:10.1101/2024.02.28.582444.
Please note that the mulea project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.