GOCompare
1.0.0
GOCompare kann wie folgt installiert werden
# CRAN
install.packages( " GOCompare " )
# Alternative: GitHub
library( devtools )
remotes :: install_github( " ccsosa/GOCompare " )Eine vollständige Liste der für das Paket benötigten Bibliotheken finden Sie unten.
Abhängigkeiten: R (>= 4.0.0)
Importe: base, utils, methods, stats, grDevices, ape, vegan, ggplot2, ggrepel, igraph, parallel, stringr
Schlägt vor: testthat
Dieses R-Paket bietet sechs Funktionen, um einen einfachen Arbeitsablauf zum Vergleichen der Ergebnisse der funktionalen Anreicherungsanalyse bereitzustellen:
Funktionen: mostFrequentGOs. graphGOspecies dienen der Analyse einer einzelnen Art.
Funktionen: compareGOspecies graph_two_GOspecies evaluateCAT_species evaluateGO_species ermöglichen den Vergleich zweier Arten GO-Begriffsliste, die zu den vom Benutzer benötigten Kategorien gehört.
Schließlich wird im Paket ein Satz von vier Datensätzen zum Testen bereitgestellt: A_thaliana, A_thaliana_compress, H_sapiens, H_sapiens_compress, comparison_example
Funktionelle Anreicherung analysiert Ergebnisse
Als Haupteingaben benötigen Sie zwei Datenrahmen mit den Ergebnissen der Funktionsanreicherungsanalyse aus Ihrer Lieblingsressource wie BinGO, AmiGO, ShinnyGO oder TopGO. Jede Datei muss diese Struktur haben:
Ein data.frame mit Ergebnissen einer funktionalen Anreicherungsanalyse mit einer Spalte mit den zu analysierenden GO-Begriffen und einer Spalte mit der zu vergleichenden Kategorie
Abhängig von der Funktion müssen Sie den Artnamen angeben: Species1 = „H_sapiens“ und Species2 = „A_thaliana“
Es muss ein Feld mit dem Spaltennamen bereitgestellt werden, in dem Ihre zu analysierenden GO-Begriffe vorhanden sind (z. B.: GOterm_field <- „Functional_Category“).
| Functional_Category | Besonderheit |
|---|---|
| Reaktion auf Stress | HILFE |
| Verteidigungsreaktion | HILFE |
| Regulierung der Zellgröße | HILFE |
| Verteidigungsreaktion | ZIEL |
| Reaktion auf äußere biotische Reize | DCE |
gorth -Funktion des gprofiler2 R-Pakets bereit.require( gprofiler2 );require( stringr );require( GOCompare )
url_file = " https://raw.githubusercontent.com/ccsosa/R_Examples/master/Hallmarks_of_Cancer_AT.csv "
x <- read.csv( url_file )
x [, 1 ] <- NULL
CH <- c( " AID " , " AIM " , " DCE " , " ERI " , " EGS " , " GIM " , " IA " , " RCD " , " SPS " , " TPI " )
x_Hsap <- lapply(seq_len(length( CH )), function ( i ){
x_unique <- unique(na.omit( x [, i ]))
x_unique <- x_unique [which( x_unique != " " )]
x_unique <- as.list( x_unique )
return ( x_unique )
})
names( x_Hsap ) <- CH
# Using as background the unique genes for the ten CH.
GOterm_field <- " term_name "
x_s <- gprofiler2 :: gost( query = x_Hsap ,
organism = " hsapiens " , ordered_query = FALSE ,
multi_query = FALSE , significant = TRUE , exclude_iea = FALSE ,
measure_underrepresentation = FALSE , evcodes = FALSE ,
user_threshold = 0.05 , correction_method = " g_SCS " ,
domain_scope = " annotated " , custom_bg = unique(unlist( x_Hsap )),
numeric_ns = " " , sources = " GO:BP " , as_short_link = FALSE )
colnames( x_s $ result )[ 1 ] <- " feature "
# Check number of enriched terms per category
tapply( x_s $ result $ feature , x_s $ result $ feature , length )
# Running function to get graph of a list of features and GO terms
x <- graphGOspecies( df = x_s $ result ,
GOterm_field = GOterm_field ,
option = " Categories " ,
numCores = 1 ,
saveGraph = FALSE ,
outdir = NULL ,
filename = NULL )
# visualize nodes
View( x $ nodes )
# Get nodes with values greater than 95%
perc <- x $ nodes [which( x $ nodes $ WEIGHT > quantile( x $ nodes $ WEIGHT , probs = 0.95 )),]
# visualize nodes filtered
View( perc )
# ########
# Running function to get graph of a list of GO terms and categories
x_GO <- graphGOspecies( df = x_s $ result ,
GOterm_field = GOterm_field ,
option = " GO " ,
numCores = 1 ,
saveGraph = FALSE ,
outdir = NULL ,
filename = NULL )
# visualize nodes
View( x_GO $ nodes )
# Get GO terms nodes with values greater than 95%
perc_GO <- x_GO $ nodes [which( x_GO $ nodes $ GO_WEIGHT > quantile( x_GO $ nodes $ GO_WEIGHT , probs = 0.95 )),]
# visualize GO terms nodes filtered
View( perc_GO )
# #######################################################################################################
# two species comparison assuming they are the same genes in Drosophila melanogaster
orth_genes <- gprofiler2 :: gorth( query = unique(unlist( x_Hsap )), source_organism = " hsapiens " , target_organism = " dmelanogaster " )
# assigning genes
x_Dmap <- list ()
for ( i in 1 : length( x_Hsap )){
D_list <- list ()
for ( j in 1 : length( x_Hsap [[ i ]])){
x_orth <- orth_genes [ orth_genes $ input == x_Hsap [[ i ]][ j ],]
if (nrow( x_orth ) > 0 ){
D_list [[ j ]] <- data.frame ( orth = x_orth $ ortholog_name )
} else {
D_list [[ j ]] <- NULL
}
rm( x_orth )
};rm( j )
D_list <- unique(do.call( rbind , D_list ))
D_list <- D_list [which( ! is.null( D_list ))]
x_Dmap [[ i ]] <- D_list
rm( D_list )
};rm( i )
names( x_Dmap ) <- CH
GOterm_field <- " term_name "
x_s2 <- gprofiler2 :: gost( query = x_Dmap ,
organism = " dmelanogaster " , ordered_query = FALSE ,
multi_query = FALSE , significant = TRUE , exclude_iea = FALSE ,
measure_underrepresentation = FALSE , evcodes = FALSE ,
user_threshold = 0.05 , correction_method = " g_SCS " ,
domain_scope = " annotated " , custom_bg = unique(unlist( x_Dmap )),
numeric_ns = " " , sources = " GO:BP " , as_short_link = FALSE )
colnames( x_s2 $ result )[ 1 ] <- " feature "
# preparing input for compare two species
x_input <- GOCompare :: compareGOspecies( x_s $ result , x_s2 $ result , GOterm_field , species1 = " H. sapiens " , species2 = " D. melanogaster " , paired_lists = T )
# try to test similarities using clustering
plot(hclust( x_input $ distance , method = " ward.D " ))
# Comparing species results
comp_species_graph <- GOCompare :: graph_two_GOspecies( x_input , species1 = " H. sapiens " , species2 = " D. melanogaster " , option = " Categories " )
# View nodes order by combined weight (SPS and GIM categories have more frequent GO terms co-occurring)
View( comp_species_graph $ nodes [order( comp_species_graph $ nodes $ COMBINED_WEIGHT , decreasing = T ),])
comp_species_graph_GO <- GOCompare :: graph_two_GOspecies( x_input , species1 = " H. sapiens " , species2 = " D. melanogaster " , option = " GO " )
# Get GO terms nodes with values greater than 95%
perc_GO_two <- comp_species_graph_GO $ nodes [which( comp_species_graph_GO $ nodes $ GO_WEIGHT > quantile( comp_species_graph_GO $ nodes $ GO_WEIGHT , probs = 0.95 )),]
# visualize GO terms nodes filtered and ordered (more frequent GO terms in both species and categories)
View( perc_GO_two [order( perc_GO_two $ GO_WEIGHT , decreasing = T ),])
# evaluating if there are different in proportions of GO terms for each category
x_CAT <- GOCompare :: evaluateCAT_species( x_s $ result , x_s2 $ result , species1 = " H. sapiens " , species2 = " D. melanogaster " , GOterm_field = " term_name " , test = " prop " )
x_CAT <- x_CAT [which( x_CAT $ FDR < = 0.05 ),]
# View Categories with FDR <0.05 (RCD,SPS,GIM, AIM,ERI,DCE)
View( x_CAT )
# evaluating if there are different in proportions of categories for GO terms
x_GO <- GOCompare :: evaluateGO_species( x_s $ result , x_s2 $ result , species1 = " H. sapiens " , species2 = " D. melanogaster " , GOterm_field = " term_name " , test = " prop " )
x_GO <- x_GO [which( x_GO $ FDR < = 0.05 ),]
# View Categories with FDR <0.05 (No significant results in proportions)
View( x_GO )
# #Optional plots (omit # symbol and run)
# source("https://raw.githubusercontent.com/ccsosa/Supplementary-information/refs/heads/main/CHAPTER3/PLOT_TWO_SP_GRAPH_CAT.R")
# source("https://raw.githubusercontent.com/ccsosa/Supplementary-information/refs/heads/main/CHAPTER3/PLOT_TWO_SP_GRAPH_GO.R")
# plot_twosp_CAT("D:/",comp_species_graph)
# plot_twosp_GO("D:/",comp_species_graph_GO)
Hauptdarsteller: Chrystian C. Sosa, Diana Carolina Clavijo-Buriticá, Mauricio Quimbaya, Victor Hugo García-Merchán
Weitere Mitwirkende: Nicolas Lopéz-Rozo, Camila Riccio Rengifo, David Arango Londoño, Maria Victoria Diaz
Sosa, Chrystian C., Diana Carolina Clavijo-Buriticá, Victor Hugo García-Merchán, Nicolas López-Rozo, Camila Riccio-Rengifo, Maria Victoria Diaz, David Arango Londoño und Mauricio Alberto Quimbaya. „GOCompare: Ein R-Paket zum Vergleich der funktionellen Anreicherungsanalyse zwischen zwei Arten“. Genomics 115, Nr. 1 (Januar 2023): 110528. https://doi.org/10.1016/j.ygeno.2022.110528.
GNU GENERAL PUBLIC LICENSE Version 3
