mulea - an R Package for Enrichment Analysis Using Multiple Ontologies and Empirical False Discovery Rate
The mulea R package (Turek et al. 2024) is a comprehensive tool for
functional enrichment analysis. It provides two different approaches:
-
For unranked sets of elements, such as significantly up- or down-regulated genes,
muleaemploys the set-based overrepresentation analysis (ORA). -
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,
muleaoffers the gene set enrichment (GSEA) approach.
For the overrepresentation analysis, mulea employs a progressive
empirical 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 the
ELTEbioinformatics/GMT_files_for_mulea
GitHub repository and through the
muleaData
ExperimentData Bioconductor package.
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 from
CRAN:
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")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)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.
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 the
mulea 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:
-
Ontology identifier (“ontology_id”): This uniquely identifies each term within the file or data frame.
-
Ontology name or description (“ontology_name”): This provides a user-friendly label or textual description for each term.
-
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
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
the
ELTEbioinformatics/GMT_files_for_mulea
GitHub 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")mulea is compatible with GMT
files provided with the
Enricher 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_idmulea 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")Alternatively, you can retrieve the ontology using the
muleaData
ExperimentData 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")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)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")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)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 the GEO2R 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 Analysis section.
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:
-
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.
-
Target set: A vector of elements to investigate, containing genes or proteins of interest, such as significantly overexpressed genes in the experiment.
-
Background set: A vector of background elements representing the broader context, often including all genes investigated in the study.
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 on Formatting 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")To perform the analysis, we will first establish a model using the ora
function. 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)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] 10Inspect 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 |
|---|---|---|---|---|---|
| FNR | FNR | 26 | 259 | 0.0000003 | 0.0000000 |
| LexA | LexA | 14 | 53 | 0.0000000 | 0.0000000 |
| SoxS | SoxS | 7 | 37 | 0.0001615 | 0.0036667 |
| Rob | Rob | 5 | 21 | 0.0004717 | 0.0051200 |
| DnaA | DnaA | 4 | 13 | 0.0006281 | 0.0052000 |
| FadR | FadR | 5 | 20 | 0.0003692 | 0.0056000 |
| NsrR | NsrR | 8 | 64 | 0.0010478 | 0.0073714 |
| ArcA | ArcA | 12 | 148 | 0.0032001 | 0.0197500 |
| IHF | IHF | 14 | 205 | 0.0070758 | 0.0458600 |
| MarA | MarA | 5 | 37 | 0.0066068 | 0.0483111 |
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 all
method 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)| ontology_name | p_value | eFDR | BH_adjusted_p_value | Bonferroni_adjusted_p_value |
|---|---|---|---|---|
| LexA | 0.0000000 | 0.0000000 | 0.0000001 | 0.0000001 |
| FNR | 0.0000003 | 0.0000000 | 0.0000208 | 0.0000416 |
| SoxS | 0.0001615 | 0.0036667 | 0.0082880 | 0.0248641 |
| FadR | 0.0003692 | 0.0056000 | 0.0142127 | 0.0568507 |
| Rob | 0.0004717 | 0.0051200 | 0.0145296 | 0.0726479 |
| DnaA | 0.0006281 | 0.0052000 | 0.0161218 | 0.0967306 |
| NsrR | 0.0010478 | 0.0073714 | 0.0230517 | 0.1613622 |
| ArcA | 0.0032001 | 0.0197500 | 0.0616014 | 0.4928114 |
| MarA | 0.0066068 | 0.0483111 | 0.1089670 | 1.0000000 |
| IHF | 0.0070758 | 0.0458600 | 0.1089670 | 1.0000000 |
| NarL | 0.0096065 | 0.0534000 | 0.1276532 | 1.0000000 |
| NikR | 0.0099470 | 0.0615833 | 0.1276532 | 1.0000000 |
| OxyR | 0.0174505 | 0.0786923 | 0.2067212 | 1.0000000 |
| ExuR | 0.0261046 | 0.1051867 | 0.2680073 | 1.0000000 |
| UxuR | 0.0261046 | 0.1051867 | 0.2680073 | 1.0000000 |
| NrdR | 0.0328500 | 0.1232750 | 0.3161817 | 1.0000000 |
| IscR | 0.0376038 | 0.1249412 | 0.3406459 | 1.0000000 |
| Nac | 0.0419701 | 0.1487556 | 0.3590774 | 1.0000000 |
| Fis | 0.0457307 | 0.1433053 | 0.3706596 | 1.0000000 |
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.
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 the
fgsea
Bioconductor 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.
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 Analysis section.
# Reading the tsv containing the ordered set
ordered_set <- read_tsv("https://raw.githubusercontent.com/ELTEbioinformatics/mulea/master/inst/extdata/ordered_set.tsv")To 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, 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)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] 10Inspect 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 | 0.0000047 |
| FNR | FNR | 259 | 0.0000660 | 0.0050484 |
| ArcA | ArcA | 148 | 0.0003076 | 0.0079598 |
| GlaR | GlaR | 3 | 0.0002188 | 0.0079598 |
| ModE | ModE | 45 | 0.0003122 | 0.0079598 |
| SoxS | SoxS | 37 | 0.0002848 | 0.0079598 |
| DnaA | DnaA | 13 | 0.0010217 | 0.0223306 |
| PaaX | PaaX | 14 | 0.0017028 | 0.0325652 |
| PspF | PspF | 7 | 0.0023494 | 0.0399397 |
| FadR | FadR | 20 | 0.0028304 | 0.0433046 |
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.
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 named GSE55662.table_wt_non_vs_cipro.tsv located in the inst/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)| 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 |
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.symbolcolumn, 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)| 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 |
Following these formatting steps, the data frame is primed for further analysis.
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] 241A 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] 7381Save 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")# 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] 7381Save the ordered set data frame to tab delimited file:
# Save ordered set to text file
ordered_set %>%
write_tsv("ordered_set.tsv")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_1.8.9To 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.
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.
Footnotes
-
The format of the actually used ontology slightly deviates from standard GMT files. In
tf_ontology, both theontology_idandontology_namecolumns contain gene symbols of the transcription factors, unlike other ontologies such as GO, where these columns hold specific identifiers and corresponding names. ↩