Title: Comprehensive GO Terms Comparison Between Species
Version: 1.0.2.2
Description: Supports the assessment of functional enrichment analyses obtained for several lists of genes and provides a workflow to analyze them between two species via weighted graphs. Methods are described in Sosa et al. (2023) <doi:10.1016/j.ygeno.2022.110528>.
URL: https://github.com/ccsosa/GOCompare
BugReports: https://github.com/ccsosa/GOCompare/issues
Depends: R (≥ 4.0.0)
Imports: base (≥ 3.5), utils (≥ 3.5), methods (≥ 3.5), stats, grDevices, ape, vegan, ggplot2, ggrepel, igraph, parallel, stringr, mathjaxr,
RdMacros: mathjaxr
License: GPL (≥ 3)
LazyData: true
Encoding: UTF-8
RoxygenNote: 7.3.2
Suggests: testthat (≥ 3.0.0)
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-06-24 14:13:39 UTC; user
Author: Chrystian Camilo Sosa ORCID iD [aut, cre, cph], Diana Carolina Clavijo-Buriticá [aut], Mauricio Alberto Quimbaya [aut], Maria Victoria Diaz [ctb], Camila Riccio Rengifo [ctb], Nicolas López-Rozo [ctb], Victor Hugo García Merchán [aut, ctb], Arlen James Mosquera [ctb], Andrés Álvarez [ctb]
Maintainer: Chrystian Camilo Sosa <ccsosaa@javerianacali.edu.co>
Repository: CRAN
Date/Publication: 2025-06-25 12:30:12 UTC

GOCompare: An R package to compare GO terms of gene lists (categories) and their orthologs

Description

GOCompare is an R package used to compare Gene Ontology (GO) term enrichment results between two species. It facilitates comparative functional genomics by allowing researchers to analyze similarities and differences in enriched GO categories between orthologous gene sets.

Details

Version: 1.0.2.2 License: GPL-3 Date: 2022-12-02

Author(s)

Maintainer: Chrystian Camilo Sosa ccsosaa@javerianacali.edu.co (ORCID) [copyright holder]

Authors:

Other contributors:

See Also

Useful links:

A thaliana functional enrichment analysis of 2224 ortholog genes related to cancer-hallmarks

Description

This dataset is the original dataset obtained for Clavijo-Buriticá (In preparation)

Usage

A_thaliana

Format

A data frame with 4063 rows and 6 variables:

Enrichment_FDR

Numeric: False discovery rate values for the GO term

Genes_in_list

numeric: Number of genes in the list of genes for a given GO term

Total_genes

numeric: Number of genes in the genome of a species for a given GO term

Functional_Category

character: GO term name or GO term id

Genes

character: Genes found fot a given GO term

feature

character: A column representing the belonging of a group of comparison

Source

https://data.mendeley.com/datasets/myyy2wxd59/1

References

Clavijo-Buriticá, Sosa, C.C., Mosquera, A.J. Álvarez, A., Medina, J. Quimbaya, M.A. A systematic comparison of the molecular machinery associated with Cancer-Hallmarks between plants and humans reveals Arabidopsis thaliana as a useful model to understand specific carcinogenic events (to be submitted, Target journal: Plos Biology)

A thaliana functional enrichment analysis results for "AID","DCE","RCD","SPS" cancer-hallmarks

Description

This dataset is a subset of the original dataset obtained for Clavijo-Buriticá (In preparation)

Usage

A_thaliana_compress

Format

A data frame with 120 rows and 6 variables (30 GO terms per cancer hallmark):

Enrichment_FDR

Numeric: False discovery rate values for the GO term

Genes_in_list

numeric: Number of genes in the list of genes for a given GO term

Total_genes

numeric: Number of genes in the genome of a species for a given GO term

Functional_Category

character: GO term name or GO term id

Genes

character: Genes found fot a given GO term

feature

character: A column representing the belonging of a group of comparison

Source

https://data.mendeley.com/datasets/myyy2wxd59/1

References

Clavijo-Buriticá, Sosa, C.C., Mosquera, A.J. Álvarez, A., Medina, J. Quimbaya, M.A. A systematic comparison of the molecular machinery associated with Cancer-Hallmarks between plants and humans reveals Arabidopsis thaliana as a useful model to understand specific carcinogenic events (to be submitted, Target journal: Plos Biology)

H. sapiens functional enrichment analysis of 5494 genes related to cancer-hallmarks

Description

This dataset is a subset of the original dataset obtained for Clavijo-Buriticá (In preparation)

Usage

H_sapiens

Format

A data frame with 5000 rows and 6 variables:

Enrichment_FDR

Numeric: False discovery rate values for the GO term

Genes_in_list

numeric: Number of genes in the list of genes for a given GO term

Total_genes

numeric: Number of genes in the genome of a species for a given GO term

Functional_Category

character: GO term name or GO term id

Genes

character: Genes found fot a given GO term

feature

character: A column representing the belonging of a group of comparison

Source

https://data.mendeley.com/datasets/myyy2wxd59/1

References

Clavijo-Buriticá, Sosa, C.C., Mosquera, A.J. Álvarez, A., Medina, J. Quimbaya, M.A. A systematic comparison of the molecular machinery associated with Cancer-Hallmarks between plants and humans reveals Arabidopsis thaliana as a useful model to understand specific carcinogenic events (to be submitted, Target journal: Plos Biology)

H. sapiens functional enrichment analysis results for "AID","DCE","RCD","SPS" cancer-hallmarks

Description

This dataset is a subset of the original dataset obtained for Clavijo-Buriticá (In preparation)

Usage

H_sapiens_compress

Format

A data frame with 120 rows and 6 variables (30 GO terms per cancer hallmark):

Enrichment_FDR

Numeric: False discovery rate values for the GO term

Genes_in_list

numeric: Number of genes in the list of genes for a given GO term

Total_genes

numeric: Number of genes in the genome of a species for a given GO term

Functional_Category

character: GO term name or GO term id

Genes

character: Genes found fot a given GO term

feature

character: A column representing the belonging of a group of comparison

Source

https://data.mendeley.com/datasets/myyy2wxd59/1

References

Clavijo-Buriticá, Sosa, C.C., Mosquera, A.J. Álvarez, A., Medina, J. Quimbaya, M.A. A systematic comparison of the molecular machinery associated with Cancer-Hallmarks between plants and humans reveals Arabidopsis thaliana as a useful model to understand specific carcinogenic events (to be submitted, Target journal: Plos Biology)

Visual representation for the results of functional enrichment analysis to compare two species and a series of categories

Description

compareGOspecies function provides a simple workflow to compare results of functional enrichment analysis for two species.

To use this function you will need two matrices with a column which, represents the features to be compared (e.g.feature). This function will extract the unique GO terms for two matrices and it will generate a presence-absence matrix where rows will represent a combination of categories and species (e.g H.sapiens AID) and columns will represent the GO terms analyzed. Further, this function will calculate Jaccard distances and it will provide as outputs a list with four slots: 1.) A principal coordinates analysis (PCoA) 2.) The Jaccard distance matrix 3.) A list of shared GO terms between species 4.) Finally, a list of the unique GO terms and the belonging to the respective species.

Usage

compareGOspecies(
  df1,
  df2,
  GOterm_field,
  species1,
  species2,
  skipPCoA = FALSE,
  paired_lists = TRUE
)

Arguments

df1

A data frame with the results of a functional enrichment analysis for the species 1 with an extra column "feature" with the features to be compared

df2

A data frame with the results of a functional enrichment analysis for the species 2 with an extra column "feature" with the features to be compared

GOterm_field

This is a string with the column name of the GO terms (e.g; "Functional_Category")

species1

This is a string with the species name for species 1 (e.g; "H. sapiens")

species2

This is a string with the species name for species 2 (e.g; "A. thaliana")

skipPCoA

This is a boolean to indicate if the PCoA graphics can be skipped

paired_lists

This is a boolean to indicate if both species have same comparable categories (gene lists). If the paired_lists is FALSE the counts will be done only for species and categories will be kept in the outcomes. Please use carefully when paired_lists = FALSE.

Value

This function will return a list with four slots: graphics, distance shared_GO_list, and unique_GO_list

Note

Do not use "-" in the feature column. This will lead to wrong results!

Examples


#Loading example datasets
data(H_sapiens_compress)
data(A_thaliana_compress)
#Defining the column with the GO terms to be compared
GOterm_field <- "Functional_Category"
#Defining the species names
species1 <- "H. sapiens"
species2 <- "A. thaliana"

#Running function
x <- compareGOspecies(df1=H_sapiens_compress,
                      df2=A_thaliana_compress,
                      GOterm_field=GOterm_field,
                      species1=species1,
                      species2=species2,
                      skipPCoA=FALSE,
                      paired_lists=TRUE)

## Not run: 
#Displaying PCoA results
 x$graphics
# Checking shared GO terms between species
 print(tapply(x$shared_GO_list$feature,x$shared_GO_list$feature,length))
 
## End(Not run)

Functional enrichment analysis comparison between H. sapiens and A. thaliana for "AID","DCE","RCD","SPS" cancer-hallmarks

Description

This dataset is the results of running the compareGOspecies species and it is composed of four slots:

graphics

PCoA graphics

distance

numeric: Jaccard distance matrix

shared_GO_list

data.frame with shared GO terms between species

unique_GO_list

data.frame with unique GO terms and their belonging two each species

Usage

comparison_ex_compress

Format

An object of class list of length 4.

Source

https://data.mendeley.com/datasets/myyy2wxd59/1

References

Clavijo-Buriticá, Sosa, C.C., Mosquera, A.J. Álvarez, A., Medina, J. Quimbaya, M.A. A systematic comparison of the molecular machinery associated with Cancer-Hallmarks between plants and humans reveals Arabidopsis thaliana as a useful model to understand specific carcinogenic events (to be submitted, Target journal: Plos Biology)

Functional enrichment analysis comparison between H. sapiens and A. thaliana for "DCE", and "RCD" cancer-hallmarks. This dataset contains 10 GO terms per category to allow a fast run of the function graph_two_GOspecies.

Description

This dataset is the results of running the compareGOspecies species and it is composed of three slots:

distance

numeric: Jaccard distance matrix

shared_GO_list

data.frame with shared GO terms between species

unique_GO_list

data.frame with unique GO terms and their belonging two each species

Usage

comparison_ex_compress_CH

Format

An object of class list of length 3.

Source

https://data.mendeley.com/datasets/myyy2wxd59/1

References

Clavijo-Buriticá, Sosa, C.C., Mosquera, A.J. Álvarez, A., Medina, J. Quimbaya, M.A. A systematic comparison of the molecular machinery associated with Cancer-Hallmarks between plants and humans reveals Arabidopsis thaliana as a useful model to understand specific carcinogenic events (to be submitted, Target journal: Plos Biology)

Comprehensive comparison between species using categories and Pearson's Chi-squared Tests

Description

evaluateGO_species provides a simple function to compare results of functional enrichment analysis for two species through the use of proportion tests or Pearson's Chi-squared Tests and a False discovery rate correction

Usage

evaluateCAT_species(df1, df2, species1, species2, GOterm_field, test = "prop")

Arguments

df1

A data frame with the results of a functional enrichment analysis for the species 1 with an extra column "feature" with the features to be compared

df2

A data frame with the results of a functional enrichment analysis for the species 2 with an extra column "feature" with the features to be compared

species1

This is a string with the species name for the species 1 (e.g; "H. sapiens")

species2

This is a string with the species name for the species 2 (e.g; "A. thaliana")

GOterm_field

This is a string with the column name of the GO terms (e.g; "Functional_Category")

test

This is a string with the hypothesis test to be performed. Two options are provided, "prop" and "chi-squared" (default value="prop")

Value

This function will return a data.frame with the following fields:

CAT Category
pvalue p-value obtained through the use of Pearson's Chi-squared Test
FDR Multiple comparison correction for the p-value column

Examples


#Loading example datasets
data(H_sapiens)
data(A_thaliana)
#Defining the column with the GO terms to be compared
GOterm_field <- "Functional_Category"
#Defining the species names
species1 <- "H. sapiens"
species2 <- "A. thaliana"
#Running function
x <- evaluateCAT_species(df1= H_sapiens,
                       df2=A_thaliana,
                       species1=species1,
                       species2=species2,
                       GOterm_field=GOterm_field,
                         test="prop")
print(x)

Comprehensive comparison between species using GO terms and Pearson's Chi-squared Tests

Description

evaluateGO_species provides a simple function to compare results of functional enrichment analysis for two species through the use of proportion tests or Pearson's Chi-squared Tests and a False discovery rate correction

Usage

evaluateGO_species(df1, df2, species1, species2, GOterm_field, test = "prop")

Arguments

df1

A data frame with the results of a functional enrichment analysis for the species 1 with an extra column "feature" with the features to be compared

df2

A data frame with the results of a functional enrichment analysis for the species 2 with an extra column "feature" with the features to be compared

species1

This is a string with the species name for the species 1 (e.g; "H. sapiens")

species2

This is a string with the species name for the species 2 (e.g; "A. thaliana")

GOterm_field

This is a string with the column name of the GO terms (e.g; "Functional_Category")

test

This is a string with the hypothesis test to be performed. Two options are provided, "prop" and "chi-squared" (default value="prop")

Value

This function will return a data.frame with the following fields:

GO GO term analyzed
pvalue p-value obtained through the use of Pearson's Chi-squared Test
FDR Multiple comparison correction for the p-value column

Examples


#Loading example datasets
data(H_sapiens)
data(A_thaliana)
#Defining the column with the GO terms to be compared
GOterm_field <- "Functional_Category"
#Defining the species names
species1 <- "H. sapiens"
species2 <- "A. thaliana"
#Running function
x <- evaluateGO_species(df1= H_sapiens,
                        df2=A_thaliana,
                        species1=species1,
                        species2=species2,
                        GOterm_field=GOterm_field,
                        test="prop")
print(x)

Undirected network representation for the results of functional enrichment analysis for one species

Description

graphGOspecies is a function to create undirected graphs using two options:

Categories option:

The nodes \((V)\) represent groups of gene lists (categories), and the edges \((E)\) represent GO terms co-occurring between pairs of categories. More specifically, Two categories: \(u,v \epsilon V \) are connected by an edge \(e=(u,v)\).the edge weights \(w(e)\) are defined as the ratio of the number of GO terms co-occurring between two categories. Edge weights w(e) are defined as the ratio of the number of GO terms (e.g. biological processes) co-occurring between two categories \(BP_{u} \ n BP_{v}\) compared to the total number of GO terms available. A node weight \(K_{w}(u)\) is defined as the sum of the edge weights where the node u is a participant. Thus, the node weight represents how frequently GO terms are reported and expressed in a biological phenomenon.

\[w(e) = \frac{\mid BP_{u} n {BP_{v}}\mid}{\mid BP\mid}\]

(1)

\[K_{w} = \sum_{{v} \epsilon {V}}{w(u,v)}\]

(2)

GO option:

The nodes \({V}\) represent GO terms and the edges \({E}'\) represent categories where a pair of GO terms co-occur. More specifically, two GO terms are connected by an edge \({e}'=({u},{v}')\). the edge weight \({w}'({e}')\) corresponds to the number of categories co-occurring the GO terms \({u}\) and \({v}'\),compared with the total number of GO terms (Equation 3). A node weight \({K}'_w({u}')\) is defined,in this case the weight represents the importance of a GO term (more frequent co-occurring).(Please be patient, it requires a long time to finish).

\[{w}'({e}')=\frac{\mid{Cu}'\cap {Cv}'\mid}{\mid BP \mid}\]

(3)

\[{K}'_w({u}')=\sum_{{v}'\epsilon {V}'}{{w}'({u}',{v}')}\]

(4)

Usage

graphGOspecies(
  df,
  GOterm_field,
  option = "Categories",
  numCores = 2,
  saveGraph = FALSE,
  outdir = NULL,
  filename = NULL
)

Arguments

df

A data frame with the results of a functional enrichment analysis for a species with an extra column "feature" with the features to be compared

GOterm_field

This is a string with the column name of the GO terms (e.g: "Functional.Category")

option

(values: "GO" or "Categories"). This option allows create either a graph where nodes are GO terms and edges are features or alternatively a graph where nodes are features and edges are GO terms (default value="Categories")

numCores

numeric, Number of cores to use for the process (default value numCores=2). For the example below, only one core will be used

saveGraph

logical, if TRUE the function will allow save the graph in graphml format

outdir

This parameter will allow save the graph file in a folder described here (e.g: "D:").This parameter only works when saveGraph=TRUE

filename

The name of the graph filename to be saved in the outdir detailed by the user.This parameter only works when saveGraph=TRUE

Value

This function will return a list with two slots: edges and nodes.

(Categories): Edges list columns:

Column Description
SOURCE and TARGET The source and target categories (Nodes in the edge)
FEATURES_N The number of GO terms between the categories
WEIGHT Edge weight
FEATURES GO terms available for both nodes

Node list columns:

Column Description
feature Category name
GO_count GO terms counts for the node
WEIGHT Node weight

(GO):

Edges list columns:

Column Description
SOURCE and TARGET The source and target GO terms (Nodes in the edge)
FEATURE The number of Categories where both GO Terms were found
WEIGHT Edge weight

Node list columns:

Column Description
GO GO term node name
GO_WEIGHT Node weight

Examples


#Loading example datasets
data(H_sapiens_compress)

GOterm_field <- "Functional_Category"

#Running function
x <- graphGOspecies(df=H_sapiens_compress,
                     GOterm_field=GOterm_field,
                     option = "Categories",
                     numCores=1,
                     saveGraph=FALSE,
                     outdir = NULL,
                     filename=NULL)

Undirected network representation for the results of functional enrichment analysis to compare two species and a series of categories

Description

graph_two_GOspecies is a function to create undirected graphs

The graph_two_GOspecies is an analog of the graphGOspecies function, and it has the same options (" Categories " and " GO "). Nevertheless, the way in which the edge and node weights are calculated is slightly different. Since two species are compared, three possible graphs are available \({G}_1,\, {G}_2\), and \({G}_3 \). \({G}_1\), and \({G}_2 \) represent each of the species analyzed and \({G}_3\) is a subgraph of \({G}_1,\, {G}_2\), which contains the GO terms or Categories co-ocurring between both species.

Categories option: (Weight): The nodes \((V)\) represent groups of gene lists (categories), and the edges \((E)\) represent GO terms co-occurring between pairs of categories and the weight of the nodes provides a measure of how a GO term is conserved between two species and a series of categories but it is biased to categories.

\[\widehat{K}_w(u)=\sum_{v \epsilon V_1}^{}w(u,v) + \sum_{v \epsilon V_2}^{}w(u,v)\]

(5)

(shared weight): The nodes \((V)\) represent groups of gene lists (categories), and the edges \((E)\) represent GO terms co-occurring between pairs of categories that are only shared between species. This node weight \({K}_s\) is computed from a shared weight of edges \({s}\), where \({N}1\) and \({N}2\) are the set of GO terms associated with the edge \(e = (u,v) \) for species 1 and 2, respectively. Therefore the node shared weight \({K}_s(u)\) is the sum of \({s}\).

\[s(e) = \frac{\mid {N1} \ n \ {N2} \mid}{\mid {N1} \bigcup {N2} \mid}\]

(6)

\[{K}_s(u)=\sum_{v \epsilon (V_1 \bigcup V_2) }^{}{s(u,v)}\]

(7)

(combined weight): This node weight \({K}_c(u)\) is a combination of the weight and the shared weight. The idea of this combined weight is to find categories with more frequent GO terms co-ocurring in order to observe functional similarities between two species with a balance of GO terms co-occurring among gene lists (categories) and the two species. This node weight varies from -1 (categories with GO terms found only in one species and few categories) to 1 (categories with GO terms shared widely between species and among other categories). the combined node weight \({K}_c\) is defined as the sum of the min-max normalized weights \(\widehat{K}_w\) and \({K}_s\) minus 1.

\[minmax(y)=\frac{y-min(y)}{max(y)-min(y)}\]

(8) \[{K}_c(u)= minmax(\widehat{K}_w(u)) + minmax({K}_s(u)) - 1 \] (9)

GO option: Given there are three possible graphs are available \({G}_1,\, {G}_2\), and \({G}_3\). \({G}_1\), and \({G}_2\) represent each of the species analyzed and \({G}_3\) is a subgraph of \({G}_1,\, {G}_2\), which contains the GO terms or Categories co-ocurring between both species. For this case, Nodes are GO terms and edges are categories where a GO terms is co-ocurring. This weight is similar to the GO weight calculated for graphGOspecies function. it is calculated as the equation 5.

\[\widehat{K}_w(u)=\sum_{v \epsilon V_1}^{}w(u,v) + \sum_{v \epsilon V_2}^{}w(u,v)\]

(5)

Usage

graph_two_GOspecies(
  x,
  species1,
  species2,
  GOterm_field,
  saveGraph = FALSE,
  option = "Categories",
  numCores = 2,
  outdir = NULL,
  filename = NULL
)

Arguments

x

is a list obtained as output of the comparegOspecies function

species1

This is a string with the species name for species 1 (e.g; "H. sapiens")

species2

This is a string with the species name for species 2 (e.g; "A. thaliana")

GOterm_field

This is a string with the column name of the GO terms (e.g; "Functional_Category")

saveGraph

logical, if TRUE the function will allow save the graph in graphml format

option

(values: "Categories or "GO"). This option allows create either a graph where nodes are GO terms and edges are features and GO as well as species belonging are edges attributes or a graph where nodes are GO terms and edges are species belonging (default value="Categories")

numCores

numeric, Number of cores to use for the process (default value numCores=2). For the example below, only one core will be used

outdir

This parameter will allow save the graph file in a folder described here (e.g: "D:").This parameter only works when saveGraph=TRUE

filename

The name of the graph filename to be saved in the outdir detailed by the user.This parameter only works when saveGraph=TRUE

Value

This function will return a list with two slots: edges and nodes. (Categories): Edges list columns:

Column Description
SOURCE and TARGET The source and target categories (Nodes in the edge)
GO_N The number of GO terms between the categories
WEIGHT Edge weight
GO GO terms available for both nodes
SP1 Number of GO terms for the species 1
SP2 Number of GO terms for the species 2
SHARED Number of GO terms shared or co-ocurring between the categories
SHARED_WEIGHT Shared weight for the edge

Node list columns:

Column Description
CAT Category name
CAT_WEIGHT Node weight
SHARED_WEIGHT Shared weight for the node
COMBINED_WEIGHT Combined weight for the node

(GO):

Edges list columns:

Column Description
SOURCE and TARGET The source and target GO terms (Nodes in the edge)
FEATURE The number of Categories where both GO Terms were found
SP Species where the GO terms was found (Species 1, Species 2 or Shared)
WEIGHT Edge weight

Node list columns:

Column Description
GO GO term node name
GO_WEIGHT Node weight

Examples


GOterm_field <- "Functional_Category"
data(comparison_ex_compress_CH)
#Defining the species names
species1 <- "H. sapiens"
species2 <- "A. thaliana"
x_graph <- graph_two_GOspecies(x=comparison_ex_compress_CH,
          species1=species1,
          species2=species2,
          GOterm_field=GOterm_field,
          numCores=1,
          saveGraph = FALSE,
          option= "Categories",
          outdir = NULL,
          filename= NULL)

Most frequent GO terms among groups for a data.frame

Description

Provides an easy way to get the frequency of GO terms such as biological processes for a data frame and a series of features

Usage

mostFrequentGOs(df, GOterm_field)

Arguments

df

A data frame with the results of a functional enrichment analysis for a species with an extra column "feature" with the features to be compared

GOterm_field

This is a string with the column name of the GO terms (e.g; "Functional.Category")

Value

This function will return a table with the frequency of GO terms per feature

Examples


#Loading example datasets
data(H_sapiens)
#Defining the column with the GO terms to be compared
GOterm_field <- "Functional_Category"
#Running function
x <- mostFrequentGOs(df=H_sapiens, GOterm_field=GOterm_field)
#Displaying results
head(x)

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