GitHub - ELTEbioinformatics/mulea: mulea - an R package for enrichment analysis using various ontologies and empirical false discovery rate (original) (raw)

mulea - an R Package for Enrichment Analysis Using Multiple Ontologies and Empirical False Discovery Rate

• Introduction
• Installation
• Usage
  • Importing the Ontology
  • The Differential Expression Dataset to Analyse
  • The Unordered Set-Based Overrepresentation Analysis (ORA)
  • Gene Set Enrichment Analysis (GSEA)
  • Formatting the Results of a Differential Expression Analysis
• Session Info
• How to Cite the mulea Package?
• References

Introduction

The mulea R package (Turek et al. 2024) is a comprehensive tool for functional enrichment analysis. It provides two different approaches:

1. For unranked sets of elements, such as significantly up- or down-regulated genes, mulea employs the set-basedoverrepresentation analysis (ORA).
2. Alternatively, if the data consists of ranked elements, for instance, genes ordered by _p_-value or log fold-change calculated by the differential expression analysis, mulea offers the gene set enrichment (GSEA) approach.

For the overrepresentation analysis, mulea employs a progressiveempirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies.

mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in 879 files available at theELTEbioinformatics/GMT_files_for_muleaGitHub repository and through themuleaDataExperimentData Bioconductor package.

Installation

Install the dependency fgsea BioConductor package:

Installing the BiocManager package if needed

if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")

Installing the fgsea package with the BiocManager

BiocManager::install("fgsea")

To install mulea fromCRAN:

install.packages("mulea")

To install the development version of mulea from GitHub:

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")

Usage

First, load the mulea and dplyr libraries. The **dplyr**library is not essential but is used here to facilitate data manipulation and inspection.

library(mulea) library(tidyverse)

Importing the Ontology

This section demonstrates how to import the desired ontology, such as transcription factors and their target genes downloaded from the database, into a data frame suitable for enrichment analysis. We present multiple methods for importing the ontology. Ensure that the identifier type (e.g.,UniProt protein ID, Entrez ID, Gene Symbol, Ensembl gene ID) matches between the ontology and the elements you wish to investigate.

Alternative 1: Importing the Ontology from a GMT File

The GMT (Gene Matrix Transposed)format contains collections of genes or proteins associated with specific ontology terms in a tab-delimited text file. The GMT file can be read into R as a data frame using the read_gmt function from themulea package. Each term is represented by a single row in both the GMT file and the data frame. Each row includes three types of elements:

1. Ontology identifier (“_ontology_id_”): This uniquely identifies each term within the file or data frame.
2. Ontology name or description (“ontology_name”): This provides a user-friendly label or textual description for each term.
3. Associated gene or protein identifiers: These are listed in the_“list_of_values”_ column, with identifiers separated by spaces, and belong to each term.1

A) mulea GMT File

Alongside with the mulea package we provide ontologies collected from 16 publicly available databases, in a standardised GMT format for 27 model organisms, from Bacteria to human. These files are available at theELTEbioinformatics/GMT_files_for_muleaGitHub repository.

To read a downloaded GMT file locally:

Reading the mulea GMT file locally

tf_ontology <- read_gmt("Transcription_factor_RegulonDB_Escherichia_coli_GeneSymbol.gmt")

Alternatively, one can read it directly from the GitHub repository:

Reading the GMT file from the GitHub repository

tf_ontology <- read_gmt("https://raw.githubusercontent.com/ELTEbioinformatics/GMT_files_for_mulea/main/GMT_files/Escherichia_coli_83333/Transcription_factor_RegulonDB_Escherichia_coli_GeneSymbol.gmt")

B) Enrichr GMT File

mulea is compatible with GMT files provided with theEnricher R package (Kuleshov et al. 2016). Download and read such a GMT file (e. g. TRRUST_Transcription_Factors_2019.txt) locally.Note that this ontology is not suitable for analyzing the Escherichia coli differential expression data described in the section The Differential Expression Dataset to Analyse.

Reading the Enrichr GMT file locally

tf_enrichr_ontology <- read_gmt("TRRUST_Transcription_Factors_2019.txt")

The ontology_name is empty, therefore we need to fill it with the ontology_id

tf_enrichr_ontology$ontology_name <- tf_enrichr_ontology$ontology_id

C) MsigDB GMT File

mulea is compatible with the MsigDB (Subramanian et al. 2005) GMT files. Download and read such a GMT file (e. g. c3.tft.v2023.2.Hs.symbols.gmt) locally. Note that this ontology is not suitable for analyzing the Escherichia coli differential expression data described in the section The Differential Expression Dataset to Analyse.

Reading the MsigDB GMT file locally

tf_msigdb_ontology <- read_gmt("c3.tft.v2023.2.Hs.symbols.gmt")

Alternative 2: Importing the Ontology with the muleaData Package

Alternatively, you can retrieve the ontology using themuleaDataExperimentData Bioconductor package:

Installing the ExperimentHub package from Bioconductor

BiocManager::install("ExperimentHub")

Calling the ExperimentHub library.

library(ExperimentHub)

Downloading the metadata from ExperimentHub.

eh <- ExperimentHub()

Creating the muleaData variable.

muleaData <- query(eh, "muleaData")

Looking for the ExperimentalHub ID of the ontology.

EHID <- mcols(muleaData) %>% as.data.frame() %>% dplyr::filter(title == "Transcription_factor_RegulonDB_Escherichia_coli_GeneSymbol.rds") %>% rownames()

Reading the ontology from the muleaData package.

tf_ontology <- muleaData[[EHID]]

Change the header

tf_ontology <- tf_ontology %>% rename(ontology_id = "ontologyId", ontology_name = "ontologyName", list_of_values = "listOfValues")

Filtering the Ontology

Enrichment analysis results can sometimes be skewed by overly specific or broad entries. mulea allows you to customise the size of ontology entries – the number of genes or proteins belonging to a term – ensuring your analysis aligns with your desired scope.

Let’s exclude ontology entries with less than 3 or more than 400 gene symbols.

Filtering the ontology

tf_ontology_filtered <- filter_ontology(gmt = tf_ontology, min_nr_of_elements = 3, max_nr_of_elements = 400)

Saving the Ontology as a GMT file

You can save the ontology as a GMT file using the **write_gmt**function.

Saving the ontology to GMT file

write_gmt(gmt = tf_ontology_filtered, file = "Filtered.gmt")

Converting a List to an Ontology Object

The mulea package provides the list_to_gmt function to convert a list of gene sets into an ontology data frame. 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_df <- list_to_gmt(ontology_list)

The Differential Expression Dataset to Analyse

For further steps we will analyse a dataset from a microarray experiment (GSE55662) in the NCBI Gene Expression Omnibus. The study by Méhi et al. (2014) investigated antibiotic resistance evolution in_Escherichia coli_. Gene expression changes were compared between_ciprofloxacin_ antibiotic-treated Escherichia coli bacteria and non-treated controls.

The expression levels of these groups were compared with theGEO2R tool:

• Non-treated control samples (2 replicates):WT_noCPR_1,WT_noCPR_2
• _Ciprofloxacin_-treated samples (2 replicates):WT_CPR_1,WT_CPR_2

To see how the dataset were prepared go to the Formatting the Results of a Differential Expression Analysissection.

The Unordered Set-Based Overrepresentation Analysis (ORA)

The mulea package implements a set-based enrichment analysis approach using the hypergeometric test, which is analogous to the one-tailed Fisher’s exact test. This method identifies statistically significant overrepresentation of elements from a target set (e.g., significantly up- or downregulated genes) within a background set (e.g., all genes that were investigated in the experiment). Therefore, a predefined threshold value, such as 0.05 for the corrected _p_-value or 2-fold change, should be used in the preceding analysis.

The overrepresentation analysis is implemented in the ora function which requires three inputs:

1. Ontology data frame: Fits the investigated taxa and the applied gene or protein identifier type, such as GO, pathway, transcription factor regulation, microRNA regulation, gene expression data, genomic location data, or protein domain content.
2. Target set: A vector of elements to investigate, containing genes or proteins of interest, such as significantly overexpressed genes in the experiment.
3. Background set: A vector of background elements representing the broader context, often including all genes investigated in the study.

Reading the Target and the Background Sets from Text Files

Let’s read the text files containing the identifiers (gene symbols) of the target and the background gene set directly from the GitHub website. To see how these files were prepared, refer to the section onFormatting the Results of a Differential Expression Analysis.

Taget set

target_set <- readLines("https://raw.githubusercontent.com/ELTEbioinformatics/mulea/master/inst/extdata/target_set.txt")

Background set

background_set <- readLines("https://raw.githubusercontent.com/ELTEbioinformatics/mulea/master/inst/extdata/background_set.txt")

Performing the OverRepresentation Analysis

To perform the analysis, we will first establish a model using the orafunction. This model defines the parameters for the enrichment analysis. We then 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 (eFDR), 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_ontology_filtered, # Test set variable element_names = target_set, # Background set variable background_element_names = background_set, # p-value adjustment method p_value_adjustment_method = "eFDR", # Number of permutations number_of_permutations = 10000, # Number of processor threads to use nthreads = 2, # Setting a random seed for reproducibility random_seed = 1)

Running the ORA

ora_results <- run_test(ora_model)

Examining the ORA Result

The ora_results data frame summarises the enrichment analysis, listing enriched ontology entries – in our case transcription factors – alongside their associated _p_-values and eFDR 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) %>%

Number of such rows

nrow() #> [1] 10

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)

Visualising the ORA Result

To gain a comprehensive understanding of the enriched transcription factors, mulea offers diverse visualisation tools, including lollipop charts, bar plots, networks, and heatmaps. These visualisations effectively reveal patterns and relationships among the enriched factors.

Initialising the visualisation with the reshape_results function:

Reshapeing the ORA results for visualisation

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")

Visualising the Spread of eFDR Values: Lollipop Plot

Lollipop charts provide 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 coloured based on their eFDR values. This visualisation 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, # 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")

Visualising the Spread of eFDR Values: Bar Plot

Bar charts offer a graphical representation similar to lollipop plots. The _y_-axis displays the enriched ontology categories (e.g., transcription factors), while the _x_-axis represents their corresponding eFDR values. The bars are coloured based on their _eFDR_values, aiding in examining the spread of eFDRs and identifying factors exceeding the significance threshold of 0.05.

plot_barplot(reshaped_results = ora_reshaped_results, # 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")

Visualising the Associations: Graph Plot

This function generates a network visualisation of the enriched ontology categories (e.g., transcription factors). Each node represents an eriched ontology category, coloured based on its eFDR value. An edge is drawn between two nodes if they share at least one common gene belonging to the target set, indicating co-regulation. The thickness of the edge reflects the number of shared genes.

plot_graph(reshaped_results = ora_reshaped_results, # 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")

Visualising the Associations: Heatmap

The heatmap displays the genes associated with the enriched ontology categories (e.g., transcription factors). Each row represents a category, coloured based on its eFDR value. Each column represents a gene from the target set belonging to the enriched ontology category, indicating potential regulation by one or more enriched transcription factors.

plot_heatmap(reshaped_results = ora_reshaped_results, # Column containing the names we wish to plot ontology_id_colname = "ontology_id", # Column that indicates the significance values p_value_type_colname = "eFDR")

Comparing the significant results when applying the eFDR to the Benjamini-Hochberg and the Bonferroni corrections

The ora function allows you to choose between different methods for calculating the FDR and adjusting the p_-values: eFDR, and allmethod options from the stats::p.adjust documentation (holm, hochberg, hommel, bonferroni, BH, BY, and fdr). The following code snippet demonstrates how to perform the analysis using the_Benjamini-Hochberg and Bonferroni corrections:

Creating the ORA model using the Benjamini-Hochberg p-value correction method

BH_ora_model <- ora(gmt = tf_ontology_filtered, # Test set variable element_names = target_set, # Background set variable background_element_names = background_set, # p-value adjustment method p_value_adjustment_method = "BH")

Running the ORA

BH_results <- run_test(BH_ora_model)

Creating the ORA model using the Bonferroni p-value correction method

Bonferroni_ora_model <- ora(gmt = tf_ontology_filtered, # Test set variable element_names = target_set, # Background set variable background_element_names = background_set, # p-value adjustment method p_value_adjustment_method = "bonferroni")

Running the ORA

Bonferroni_results <- run_test(Bonferroni_ora_model)

To compare the significant results (using the conventional < 0.05 threshold) of the eFDR, Benjamini-Hochberg, and _Bonferroni_corrections, we can merge and filter the result tables:

Merging the Benjamini-Hochberg and eFDR results

merged_results <- BH_results %>%

Renaming the column

rename(BH_adjusted_p_value = adjusted_p_value) %>%

Selecting the necessary columns

select(ontology_id, BH_adjusted_p_value) %>%

Joining with the eFDR results

left_join(ora_results, ., by = "ontology_id") %>%

Converting the data.frame to a tibble

tibble()

Merging the Bonferroni results with the merged results

merged_results <- Bonferroni_results %>%

Renaming the column

rename(Bonferroni_adjusted_p_value = adjusted_p_value) %>%

Selecting the necessary columns

select(ontology_id, Bonferroni_adjusted_p_value) %>%

Joining with the eFDR results

left_join(merged_results, ., by = "ontology_id") %>%

Arranging by the p-value

arrange(p_value)

filter the p-value < 0.05 results

merged_results_filtered <- merged_results %>% filter(p_value < 0.05) %>%

remove the unnecessary columns

select(-ontology_id, -nr_common_with_tested_elements, -nr_common_with_background_elements)

A comparison of the significant results revealed that conventional_p_-value corrections (Benjamini-Hochberg and Bonferroni) tend to be overly conservative, leading to a reduction in the number of significant transcription factors compared to the eFDR. As illustrated in the below figure, by applying the eFDR we were able to identify 10 significant transcription factors, while with the Benjamini-Hochberg and Bonferroni corrections only 7 and 3, respectively. This suggests that the eFDR may be a more suitable approach for controlling false positives in this context.

Gene Set Enrichment Analysis (GSEA)

To perform enrichment analysis using ranked lists, you need to provide an ordered list of elements, such as genes or proteins. This ranking is typically based on the results of your prior analysis, using metrics like _p_-values, _z_-scores, fold-changes, or others. Crucially, the ranked list should include all elements involved in your analysis. For example, in a differential expression study, it should encompass all genes that were measured.

mulea utilises 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 thefgseaBioconductor 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:

1. 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.
2. 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.

Reading the Tab Delimited File Containing the Ordered Set

Let’s read the TSV file containing the identifiers (gene symbols) and the log fold change values of the investigated set directly from the GitHub website. For details on how this file was prepared, please refer to the Formatting the Results of a Differential Expression Analysissection.

Reading the tsv containing the ordered set

ordered_set <- read_tsv("https://raw.githubusercontent.com/ELTEbioinformatics/mulea/master/inst/extdata/ordered_set.tsv")

Performing the Gene Set Enrichment Analysis

To perform the analysis, we will first establish a model using thegsea function. This model defines the parameters for the enrichment analysis. Subsequently, we will execute the test itself using therun_test function. We will employ 10,000 permutations for the false discovery rate, to ensure robust correction for multiple testing.

Creating the GSEA model using the GMT variable

gsea_model <- gsea(gmt = tf_ontology_filtered, # Names of elements to test element_names = ordered_set$Gene.symbol, # LogFC-s of elements to test element_scores = ordered_set$logFC, # Consider elements having positive logFC values only element_score_type = "pos", # Number of permutations number_of_permutations = 10000)

Running the GSEA

gsea_results <- run_test(gsea_model)

Examining the GSEA Results

The gsea_results data frame summarises 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] 10

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)

Visualising the GSEA Results

Initializing the visualisation with the reshape_results function:

Reshaping the GSEA results for visualisation

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")

Visualising Relationships: Graph Plot

This function generates a network visualisation of the enriched ontology categories (e.g., transcription factors). Each node represents a category 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.

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")

Other plot types such as lollipop plots, bar plots, and heatmaps can also be used to investigate the GSEA results.

Formatting the Results of a Differential Expression Analysis

Understanding the Differential Expression Results Table

This section aims to elucidate the structure and essential components of the provided DE results table. It offers guidance to users on interpreting the data effectively for subsequent analysis with mulea.

Let’s read the differential expression result file namedGSE55662.table_wt_non_vs_cipro.tsvlocated in theinst/extdata/folder directly from the GitHub website.

Importing necessary libraries and reading the DE results table

geo2r_result_tab <- read_tsv("https://raw.githubusercontent.com/ELTEbioinformatics/mulea/master/inst/extdata/GSE55662.table_wt_non_vs_cipro.tsv")

Let’s delve into the geo2r_result_tab data frame by examining its initial rows:

Printing the first few rows of the data frame

geo2r_result_tab %>%
head(3)

Data Preparation:

Preparing the data frame appropriately for enrichment analysis is crucial. This involves specific steps tailored to the microarray experiment type. Here, we undertake the following transformations:

• Gene Symbol Extraction: We isolate the primary gene symbol from the Gene.symbol column, eliminating any extraneous information.
• Handling Missing Values: Rows with missing gene symbols (NA) are excluded.
• Sorting by Fold Change: The data frame is sorted by log-fold change (logFC) in descending order, prioritizing genes with the most significant expression alterations.

Formatting the data frame

geo2r_result_tab <- geo2r_result_tab %>%

Extracting the primary gene symbol and removing extraneous information

mutate(Gene.symbol = str_remove(string = Gene.symbol, pattern = "\/.*")) %>%

Filtering out rows with NA gene symbols

filter(!is.na(Gene.symbol)) %>%

Sorting by logFC

arrange(desc(logFC))

Before proceeding with enrichment analysis, let’s examine the initial rows of the formatted geo2r_result_tab data frame:

Printing the first few rows of the formatted data frame

geo2r_result_tab %>%
head(3)

Following these formatting steps, the data frame is primed for further analysis.

Preparing Input Data for the ORA

Target Set

A vector containing the gene symbols of significantly overexpressed (adjusted _p_-value < 0.05) genes with greater than 2 fold-change (logFC > 1).

target_set <- 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:

target_set %>% head(10) #> [1] "gnsB" "sulA" "recN" "c4435" "dinI" "c2757" "c1431"
#> [8] "gabP" "recA" "ECs5456"

The number of genes in the target set:

target_set %>% length() #> [1] 241

Background Set

A vector containing the gene symbols of all genes were included in the differential expression analysis.

background_set <- geo2r_result_tab %>%

Selecting the Gene.symbol column

select(Gene.symbol) %>%

Converting the tibble to a vector

pull() %>%

Removing duplicates

unique()

The number of genes in the background set:

background_set %>% length() #> [1] 7381

Save the target and the background set vectors to text file:

Save taget set to text file

target_set %>% writeLines("target_set.txt")

Save background set to text file

background_set %>% writeLines("inst/extdata/background_set.txt")

Preparing Input Data for the GSEA

If there are duplicated Gene.symbols keep the first one only

ordered_set <- 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 ordered_set vector:

ordered_set %>% nrow() #> [1] 7381

Save the ordered set data frame to tab delimited file:

Save ordered set to text file

ordered_set %>% write_tsv("ordered_set.tsv")

Session Info

sessionInfo() #> R version 4.4.2 (2024-10-31) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 22.04.5 LTS #> #> Matrix products: default #> 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 #> #> locale: #> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=hu_HU.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=hu_HU.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=hu_HU.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=hu_HU.UTF-8 LC_IDENTIFICATION=C
#> #> time zone: Europe/Budapest #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base
#> #> other attached packages: #> [1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
#> [5] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
#> [9] ggplot2_3.5.1 tidyverse_2.0.0 mulea_1.0.2
#> #> loaded via a namespace (and not attached): #> [1] fastmatch_1.1-4 gtable_0.3.5 xfun_0.47
#> [4] ggrepel_0.9.6 lattice_0.22-6 tzdb_0.4.0
#> [7] vctrs_0.6.5 tools_4.4.2 generics_0.1.3
#> [10] curl_5.2.2 parallel_4.4.2 fansi_1.0.6
#> [13] highr_0.11 pkgconfig_2.0.3 Matrix_1.7-0
#> [16] data.table_1.16.0 lifecycle_1.0.4 compiler_4.4.2
#> [19] farver_2.1.2 munsell_0.5.1 ggforce_0.4.2
#> [22] fgsea_1.30.0 graphlayouts_1.2.0 codetools_0.2-20
#> [25] htmltools_0.5.8.1 yaml_2.3.10 crayon_1.5.3
#> [28] pillar_1.9.0 MASS_7.3-61 BiocParallel_1.38.0 #> [31] cachem_1.1.0 viridis_0.6.5 tidyselect_1.2.1
#> [34] digest_0.6.37 stringi_1.8.4 labeling_0.4.3
#> [37] cowplot_1.1.3 polyclip_1.10-7 fastmap_1.2.0
#> [40] grid_4.4.2 colorspace_2.1-1 cli_3.6.3
#> [43] magrittr_2.0.3 ggraph_2.2.1 tidygraph_1.3.1
#> [46] utf8_1.2.4 withr_3.0.1 scales_1.3.0
#> [49] bit64_4.0.5 timechange_0.3.0 rmarkdown_2.28
#> [52] bit_4.0.5 igraph_2.0.3 gridExtra_2.3
#> [55] hms_1.1.3 memoise_2.0.1 evaluate_0.24.0
#> [58] knitr_1.48 viridisLite_0.4.2 rlang_1.1.4
#> [61] Rcpp_1.0.13 glue_1.7.0 tweenr_2.0.3
#> [64] vroom_1.6.5 rstudioapi_0.16.0 R6_2.5.1
#> [67] plyr_1.8.9

How to Cite the mulea Package?

To cite package mulea in publications use:

Turek, Cezary, Márton Ölbei, Tamás Stirling, Gergely Fekete, Ervin Tasnádi, Leila Gul, Balázs Bohár, Balázs Papp, Wiktor Jurkowski, and Eszter Ari. 2024. “mulea: An R Package for Enrichment Analysis Using Multiple Ontologies and Empirical False Discovery Rate.” BMC Bioinformatics 25 (1): 334.https://doi.org/10.1186/s12859-024-05948-7.

References

Korotkevich, Gennady, Vladimir Sukhov, Nikolay Budin, Boris Shpak, Maxim N. Artyomov, and Alexey Sergushichev. 2021. “Fast Gene Set Enrichment Analysis.” bioRxiv, February. https://doi.org/10.1101/060012.

Kuleshov, Maxim V., Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, et al. 2016. “Enrichr: A Comprehensive Gene Set Enrichment Analysis Web Server 2016 Update.” Nucleic Acids Research 44 (W1): W90–97.https://doi.org/10.1093/nar/gkw377.

Méhi, Orsolya, Balázs Bogos, Bálint Csörgő, Ferenc Pál, Ákos Nyerges, Balázs Papp, and Csaba Pál. 2014. “Perturbation of Iron Homeostasis Promotes the Evolution of Antibiotic Resistance.” Molecular Biology and Evolution 31 (10): 2793–2804. https://doi.org/10.1093/molbev/msu223.

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.

Turek, Cezary, Márton Ölbei, Tamás Stirling, Gergely Fekete, Ervin Tasnádi, Leila Gul, Balázs Bohár, Balázs Papp, Wiktor Jurkowski, and Eszter Ari. 2024. “mulea: An R Package for Enrichment Analysis Using Multiple Ontologies and Empirical False Discovery Rate.” BMC Bioinformatics 25 (1): 334.https://doi.org/10.1186/s12859-024-05948-7.

1. The format of the actually used ontology slightly deviates from standard GMT files. In tf_ontology, both the ontology_id andontology_name columns contain gene symbols of the transcription factors, unlike other ontologies such as GO, where these columns hold specific identifiers and corresponding names. ↩