| Title: | Analyzing and visualizing KEGG information using the grammar of graphics |
|---|---|
| Description: | This package aims to import, parse, and analyze KEGG data such as KEGG PATHWAY and KEGG MODULE. The package supports visualizing KEGG information using ggplot2 and ggraph through using the grammar of graphics. The package enables the direct visualization of the results from various omics analysis packages. |
| Authors: | Noriaki Sato [cre, aut] |
| Maintainer: | Noriaki Sato <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.5.0 |
| Built: | 2024-10-30 08:06:16 UTC |
| Source: | https://github.com/bioc/ggkegg |
Add the title to the image produced by output_overlay_image using magick.
add_title( out, title = NULL, size = 20, height = 30, color = "white", titleColor = "black", gravity = "west" )add_title( out, title = NULL, size = 20, height = 30, color = "white", titleColor = "black", gravity = "west" )
out |
the image |
title |
the title |
size |
the size |
height |
title height |
color |
bg color |
titleColor |
title color |
gravity |
positioning of the title in the blank image |
output the image
append clusterProfiler results to graph
append_cp( res, how = "any", name = "name", pid = NULL, infer = FALSE, sep = " ", remove_dot = TRUE )append_cp( res, how = "any", name = "name", pid = NULL, infer = FALSE, sep = " ", remove_dot = TRUE )
res |
enrichResult class |
how |
how to determine whether the nodes is in enrichment results |
name |
name column to search for query |
pid |
pathway ID, if NULL, try to infer from graph attribute |
infer |
if TRUE, append the prefix to queried IDs based on pathway ID |
sep |
separater for name |
remove_dot |
remove dots in the name |
enrich_attribute column in node
graph <- create_test_pathway() nodes <- graph |> data.frame() if (require("clusterProfiler")) { cp <- enrichKEGG(nodes$name |> strsplit(":") |> vapply("[", 2, FUN.VALUE="character")) ## This append graph node logical value whether the ## enriched genes are in pathway graph <- graph |> mutate(cp=append_cp(cp, pid="hsa05322")) }graph <- create_test_pathway() nodes <- graph |> data.frame() if (require("clusterProfiler")) { cp <- enrichKEGG(nodes$name |> strsplit(":") |> vapply("[", 2, FUN.VALUE="character")) ## This append graph node logical value whether the ## enriched genes are in pathway graph <- graph |> mutate(cp=append_cp(cp, pid="hsa05322")) }
Append the label position at center of edges in global map like ko01100 where line type nodes are present in KGML. Add 'center' column to graph edge.
append_label_position(g)append_label_position(g)
g |
graph |
tbl_graph
## Simulate nodes containing `graphics_type` of line and `coords` gm_test <- data.frame(name="ko:K00112",type="ortholog",reaction="rn:R00112", graphics_name="K00112",fgcolor="#ff0000",bgcolor="#ffffff", graphics_type="line",coords="1,2,3,4",orig.id=1,pathway_id="test") gm_test <- tbl_graph(gm_test) test <- process_line(gm_test) |> append_label_position()## Simulate nodes containing `graphics_type` of line and `coords` gm_test <- data.frame(name="ko:K00112",type="ortholog",reaction="rn:R00112", graphics_name="K00112",fgcolor="#ff0000",bgcolor="#ffffff", graphics_type="line",coords="1,2,3,4",orig.id=1,pathway_id="test") gm_test <- tbl_graph(gm_test) test <- process_line(gm_test) |> append_label_position()
assign DESeq2 numerical values to nodes
assign_deseq2( res, column = "log2FoldChange", gene_type = "SYMBOL", org_db = NULL, org = "hsa", numeric_combine = mean, name = "name", sep = " ", remove_dot = TRUE )assign_deseq2( res, column = "log2FoldChange", gene_type = "SYMBOL", org_db = NULL, org = "hsa", numeric_combine = mean, name = "name", sep = " ", remove_dot = TRUE )
res |
The result() of DESeq() |
column |
column of the numeric attribute, default to log2FoldChange |
gene_type |
default to SYMBOL |
org_db |
organism database to convert ID to ENTREZID |
org |
organism ID in KEGG |
numeric_combine |
how to combine multiple numeric values |
name |
column name for ID in tbl_graph nodes |
sep |
for node name |
remove_dot |
remove dot in the name |
numeric vector
graph <- create_test_pathway() res <- data.frame(row.names="6737",log2FoldChange=1.2) graph <- graph |> mutate(num=assign_deseq2(res, gene_type="ENTREZID"))graph <- create_test_pathway() res <- data.frame(row.names="6737",log2FoldChange=1.2) graph <- graph |> mutate(num=assign_deseq2(res, gene_type="ENTREZID"))
make closed type arrow
carrow(length = unit(2, "mm"))carrow(length = unit(2, "mm"))
length |
arrow length in unit() |
arrow()
carrow()carrow()
combine the reference KEGG pathway graph with bnlearn boot.strength output
combine_with_bnlearn(pg, str, av, prefix = "ko:", how = "any")combine_with_bnlearn(pg, str, av, prefix = "ko:", how = "any")
pg |
reference graph (output of 'pathway') |
str |
strength data.frame |
av |
averaged network to plot |
prefix |
add prefix to node name of original averaged network like, 'hsa:' or 'ko:'. |
how |
'any' or 'all' |
tbl_graph
if (requireNamespace("bnlearn", quietly=TRUE)) { ## Simulating boot.strength() results av <- bnlearn::model2network("[6737|51428][51428]") str <- data.frame(from="51428",to="6737",strength=0.8,direction=0.7) graph <- create_test_pathway() combined <- combine_with_bnlearn(graph, str, av, prefix="hsa:") }if (requireNamespace("bnlearn", quietly=TRUE)) { ## Simulating boot.strength() results av <- bnlearn::model2network("[6737|51428][51428]") str <- data.frame(from="51428",to="6737",strength=0.8,direction=0.7) graph <- create_test_pathway() combined <- combine_with_bnlearn(graph, str, av, prefix="hsa:") }
convert the identifier using retrieved information
convert_id( org = NULL, name = "name", file = NULL, query_column = 1, convert_column = NULL, colon = TRUE, first_arg_comma = TRUE, remove_dot = TRUE, pref = NULL, sep = " ", first_arg_sep = TRUE, divide_semicolon = TRUE, edge = FALSE )convert_id( org = NULL, name = "name", file = NULL, query_column = 1, convert_column = NULL, colon = TRUE, first_arg_comma = TRUE, remove_dot = TRUE, pref = NULL, sep = " ", first_arg_sep = TRUE, divide_semicolon = TRUE, edge = FALSE )
org |
which identifier to convert |
name |
which column to convert in edge or node table |
file |
specify the file for conversion. The column in 'query_column' will be used for querying the ID in the graph. |
query_column |
default to 1. |
convert_column |
which column is parsed in obtained data frame from KEGG REST API or local file |
colon |
whether the original ids include colon (e.g. 'ko:') If 'NULL', automatically set according to 'org' |
first_arg_comma |
take first argument of comma-separated string, otherwise fetch all strings |
remove_dot |
remove dots in the name |
pref |
prefix for the query identifiers |
sep |
separater to separate node names, defaul to space |
first_arg_sep |
take first argument if multiple identifiers are in the node name, otherwise parse all identifiers |
divide_semicolon |
whether to divide string by semicolon, and take the first value |
edge |
if converting edges |
vector containing converted IDs
library(tidygraph) graph <- create_test_pathway() graph <- graph %>% mutate(conv=convert_id("hsa"))library(tidygraph) graph <- create_test_pathway() graph <- graph %>% mutate(conv=convert_id("hsa"))
Test kegg_module for examples and vignettes. The module has no biological meanings.
create_test_module()create_test_module()
return a test module to use in examples
create_test_module()create_test_module()
create_test_network
create_test_network()create_test_network()
test network
create_test_network()create_test_network()
As downloading from KEGG API is not desirable in vignettes or examples, return the 'tbl_graph' with two nodes and two edges.
create_test_pathway(line = FALSE)create_test_pathway(line = FALSE)
line |
return example containing graphics type line |
tbl_graph
create_test_pathway()create_test_pathway()
given the matrix representing gene as row and sample as column, append the edge value (sum of values of connecting nodes) to edge matrix and return tbl_graph object. The implementation is based on the paper by Adnan et al. 2020 (https://doi.org/10.1186/s12859-020-03692-2).
edge_matrix( graph, mat, gene_type = "SYMBOL", org = "hsa", org_db = NULL, num_combine = mean, name = "name", sep = " ", remove_dot = TRUE )edge_matrix( graph, mat, gene_type = "SYMBOL", org = "hsa", org_db = NULL, num_combine = mean, name = "name", sep = " ", remove_dot = TRUE )
graph |
tbl_graph to append values to |
mat |
matrix representing gene as row and sample as column |
gene_type |
gene ID of matrix row |
org |
organism ID to convert ID |
org_db |
organism database to convert ID |
num_combine |
function to combine multiple numeric values |
name |
name column in node data, default to node |
sep |
separater of name, default to " " |
remove_dot |
remove "..." in node name |
tbl_graph
graph <- create_test_pathway() num_df <- data.frame(row.names=c("6737","51428"), "sample1"=c(1.1,1.2), "sample2"=c(1.1,1.2), check.names=FALSE) graph <- graph %>% edge_matrix(num_df, gene_type="ENTREZID")graph <- create_test_pathway() num_df <- data.frame(row.names=c("6737","51428"), "sample1"=c(1.1,1.2), "sample2"=c(1.1,1.2), check.names=FALSE) graph <- graph %>% edge_matrix(num_df, gene_type="ENTREZID")
add numeric attribute to edge of tbl_graph
edge_numeric( num, num_combine = mean, how = "any", name = "name", sep = " ", remove_dot = TRUE )edge_numeric( num, num_combine = mean, how = "any", name = "name", sep = " ", remove_dot = TRUE )
num |
named vector or tibble with id and value column |
num_combine |
how to combine number when multiple hit in the same node |
how |
'any' or 'all' |
name |
name of column to match for |
sep |
separater for name, default to " " |
remove_dot |
remove "..." in the name |
numeric vector
graph <- create_test_pathway() graph <- graph |> activate("edges") |> mutate(num=edge_numeric(c(1.1) |> setNames("degradation"), name="subtype_name"))graph <- create_test_pathway() graph <- graph |> activate("edges") |> mutate(num=edge_numeric(c(1.1) |> setNames("degradation"), name="subtype_name"))
add numeric attribute to edge of tbl_graph based on node values The implementation is based on the paper by Adnan et al. 2020 (https://doi.org/10.1186/s12859-020-03692-2).
edge_numeric_sum( num, num_combine = mean, how = "any", name = "name", sep = " ", remove_dot = TRUE )edge_numeric_sum( num, num_combine = mean, how = "any", name = "name", sep = " ", remove_dot = TRUE )
num |
named vector or tibble with id and value column |
num_combine |
how to combine number when multiple hit in the same node |
how |
'any' or 'all' |
name |
name of column to match for |
sep |
separater for name, default to " " |
remove_dot |
remove "..." in the name |
numeric vector
graph <- create_test_pathway() graph <- graph |> activate("edges") |> mutate(num=edge_numeric_sum(c(1.2,-1.2) |> setNames(c("TRIM21","DDX41")), name="graphics_name"))graph <- create_test_pathway() graph <- graph |> activate("edges") |> mutate(num=edge_numeric_sum(c(1.2,-1.2) |> setNames(c("TRIM21","DDX41")), name="graphics_name"))
Wrapper function for plotting KEGG pathway graph add geom_node_rect, geom_node_text and geom_edge_link simultaneously
geom_kegg( edge_color = NULL, node_label = .data$name, group_color = "red", parallel = FALSE )geom_kegg( edge_color = NULL, node_label = .data$name, group_color = "red", parallel = FALSE )
edge_color |
color attribute to edge |
node_label |
column name for node label |
group_color |
border color for group node rectangles |
parallel |
use geom_edge_parallel() instead of geom_edge_link() |
ggplot2 object
test_pathway <- create_test_pathway() p <- ggraph(test_pathway, layout="manual", x=x, y=y)+ geom_kegg()test_pathway <- create_test_pathway() p <- ggraph(test_pathway, layout="manual", x=x, y=y)+ geom_kegg()
Plot rectangular shapes to ggplot2 using GeomRect, using StatFilter in ggraph
geom_node_rect( mapping = NULL, data = NULL, position = "identity", show.legend = NA, ... )geom_node_rect( mapping = NULL, data = NULL, position = "identity", show.legend = NA, ... )
mapping |
aes mapping |
data |
data to plot |
position |
positional argument |
show.legend |
whether to show legend |
... |
passed to 'params' in 'layer()' function |
geom
test_pathway <- create_test_pathway() plt <- ggraph(test_pathway, layout="manual", x=x, y=y) + geom_node_rect()test_pathway <- create_test_pathway() plt <- ggraph(test_pathway, layout="manual", x=x, y=y) + geom_node_rect()
Wrapper function for plotting a certain type of nodes with background color with geom_node_rect()
geom_node_rect_kegg(type = NULL, rect_fill = "grey")geom_node_rect_kegg(type = NULL, rect_fill = "grey")
type |
type to be plotted (gene, map, compound ...) |
rect_fill |
rectangular fill |
ggplot2 object
test_pathway <- create_test_pathway() plt <- ggraph(test_pathway, layout="manual", x=x, y=y) + geom_node_rect_kegg(type="gene")test_pathway <- create_test_pathway() plt <- ggraph(test_pathway, layout="manual", x=x, y=y) + geom_node_rect_kegg(type="gene")
Wrapper function for plotting multiple rects with background color with geom_node_rect(). All columns should belong to the same scale when using 'asIs=FALSE'. If you need multiple scales for each element, please use 'ggh4x::scale_fill_multi' for each.
geom_node_rect_multi(..., asIs = TRUE)geom_node_rect_multi(..., asIs = TRUE)
... |
color columns |
asIs |
treat the color as is or not |
ggplot2 object
plt <- create_test_pathway() %>% ggraph() + geom_node_rect_multi(bgcolor)plt <- create_test_pathway() %>% ggraph() + geom_node_rect_multi(bgcolor)
Plot shadowtext at node position, use StatFilter in ggraph
geom_node_shadowtext( mapping = NULL, data = NULL, position = "identity", show.legend = NA, ... )geom_node_shadowtext( mapping = NULL, data = NULL, position = "identity", show.legend = NA, ... )
mapping |
aes mapping |
data |
data to plot |
position |
positional argument |
show.legend |
whether to show legend |
... |
passed to 'params' in 'layer()' function |
geom
test_pathway <- create_test_pathway() plt <- ggraph(test_pathway, layout="manual", x=x, y=y) + geom_node_shadowtext(aes(label=name))test_pathway <- create_test_pathway() plt <- ggraph(test_pathway, layout="manual", x=x, y=y) + geom_node_shadowtext(aes(label=name))
Get slot from 'kegg_module' class object.
get_module_attribute(x, attribute)get_module_attribute(x, attribute)
x |
kegg_module class object |
attribute |
pass to get_module_attribute |
Get slot from 'kegg_module' class object.
attribute of kegg_module
get the kegg_module class attribute
## S4 method for signature 'kegg_module' get_module_attribute(x, attribute)## S4 method for signature 'kegg_module' get_module_attribute(x, attribute)
x |
kegg_module class object |
attribute |
slot name |
attribute of kegg_module
get slot from 'kegg_network' class
get_network_attribute(x, attribute)get_network_attribute(x, attribute)
x |
kegg_network class object |
attribute |
pass to get_network_attribute |
attribute of kegg_network
get the kegg_network class attribute
## S4 method for signature 'kegg_network' get_network_attribute(x, attribute)## S4 method for signature 'kegg_network' get_network_attribute(x, attribute)
x |
kegg_network class object |
attribute |
slot name |
attribute of kegg_module
main function parsing KEGG pathway data, making igraph object and passing it to ggraph.
ggkegg( pid, layout = "native", return_igraph = FALSE, return_tbl_graph = FALSE, pathway_number = 1, convert_org = NULL, convert_first = TRUE, convert_collapse = NULL, convert_reaction = FALSE, delete_undefined = FALSE, delete_zero_degree = FALSE, numeric_attribute = NULL, node_rect_nudge = 0, group_rect_nudge = 2, module_type = "definition", module_definition_type = "text" )ggkegg( pid, layout = "native", return_igraph = FALSE, return_tbl_graph = FALSE, pathway_number = 1, convert_org = NULL, convert_first = TRUE, convert_collapse = NULL, convert_reaction = FALSE, delete_undefined = FALSE, delete_zero_degree = FALSE, numeric_attribute = NULL, node_rect_nudge = 0, group_rect_nudge = 2, module_type = "definition", module_definition_type = "text" )
pid |
KEGG Pathway id e.g. hsa04110 |
layout |
default to "native", using KGML positions |
return_igraph |
return the resulting igraph object |
return_tbl_graph |
return the resulting tbl_graph object (override 'return_igraph' argument) |
pathway_number |
pathway number if passing enrichResult |
convert_org |
these organism names are fetched from REST API and cached, and used to convert the KEGG identifiers. e.g. c("hsa", "compound") |
convert_first |
after converting, take the first element as node name when multiple genes are listed in the node |
convert_collapse |
if not NULL, collapse the gene names by this character when multiple genes are listed in the node. |
convert_reaction |
reaction name (graph attribute 'reaction') will be converted to reaction formula |
delete_undefined |
delete 'undefined' node specifying group, should be set to 'TRUE' when the layout is not from native KGML. |
delete_zero_degree |
delete nodes with zero degree, default to FALSE |
numeric_attribute |
named vector for appending numeric attribute |
node_rect_nudge |
parameter for nudging the node rect |
group_rect_nudge |
parameter for nudging the group node rect |
module_type |
specify which module attributes to obtain (definition or reaction) |
module_definition_type |
'text' or 'network' when parsing module definition. If 'text', return ggplot object. If 'network', return 'tbl_graph'. |
ggplot2 object
## Use pathway ID to obtain `ggraph` object directly. g <- ggkegg("hsa04110") g + geom_node_rect()## Use pathway ID to obtain `ggraph` object directly. g <- ggkegg("hsa04110") g + geom_node_rect()
save the image respecting the original width and height of the image. Only applicable for the ggplot object including 'overlay_raw_map' layers.
ggkeggsave(filename, plot, dpi = 300, wscale = 90, hscale = 90)ggkeggsave(filename, plot, dpi = 300, wscale = 90, hscale = 90)
filename |
file name of the image |
plot |
plot to be saved |
dpi |
dpi, passed to ggsave |
wscale |
width scaling factor for pixel to inches |
hscale |
height scaling factor fo pixel to inches |
save the image
ggplot_add.geom_kegg
## S3 method for class 'geom_kegg' ggplot_add(object, plot, object_name)## S3 method for class 'geom_kegg' ggplot_add(object, plot, object_name)
object |
An object to add to the plot |
plot |
The ggplot object to add object to |
object_name |
The name of the object to add |
ggplot2 object
test_pathway <- create_test_pathway() p <- ggraph(test_pathway, layout="manual", x=x, y=y)+ geom_kegg()test_pathway <- create_test_pathway() p <- ggraph(test_pathway, layout="manual", x=x, y=y)+ geom_kegg()
ggplot_add.geom_node_rect_kegg
## S3 method for class 'geom_node_rect_kegg' ggplot_add(object, plot, object_name)## S3 method for class 'geom_node_rect_kegg' ggplot_add(object, plot, object_name)
object |
An object to add to the plot |
plot |
The ggplot object to add object to |
object_name |
The name of the object to add |
ggplot2 object
test_pathway <- create_test_pathway() plt <- ggraph(test_pathway, layout="manual", x=x, y=y) + geom_node_rect_kegg(type="gene")test_pathway <- create_test_pathway() plt <- ggraph(test_pathway, layout="manual", x=x, y=y) + geom_node_rect_kegg(type="gene")
ggplot_add.geom_node_rect_multi
## S3 method for class 'geom_node_rect_multi' ggplot_add(object, plot, object_name)## S3 method for class 'geom_node_rect_multi' ggplot_add(object, plot, object_name)
object |
An object to add to the plot |
plot |
The ggplot object to add object to |
object_name |
The name of the object to add |
ggplot2 object
plt <- create_test_pathway() %>% ggraph() + geom_node_rect_multi(bgcolor)plt <- create_test_pathway() %>% ggraph() + geom_node_rect_multi(bgcolor)
ggplot_add.overlay_raw_map
## S3 method for class 'overlay_raw_map' ggplot_add(object, plot, object_name)## S3 method for class 'overlay_raw_map' ggplot_add(object, plot, object_name)
object |
An object to add to the plot |
plot |
The ggplot object to add object to |
object_name |
The name of the object to add |
ggplot2 object
## Need `pathway_id` column in graph ## if the function is to automatically infer graph <- create_test_pathway() |> mutate(pathway_id="hsa04110") ggraph(graph) + overlay_raw_map()## Need `pathway_id` column in graph ## if the function is to automatically infer graph <- create_test_pathway() |> mutate(pathway_id="hsa04110") ggraph(graph) + overlay_raw_map()
ggplot_add.stamp
## S3 method for class 'stamp' ggplot_add(object, plot, object_name)## S3 method for class 'stamp' ggplot_add(object, plot, object_name)
object |
An object to add to the plot |
plot |
The ggplot object to add object to |
object_name |
The name of the object to add |
ggplot2 object
test_pathway <- create_test_pathway() plt <- ggraph(test_pathway, layout="manual", x=x, y=y) + stamp("hsa:6737")test_pathway <- create_test_pathway() plt <- ggraph(test_pathway, layout="manual", x=x, y=y) + stamp("hsa:6737")
highlight the entities in the pathway, overlay raw map and return the results. Note that highlighted nodes are considered to be rectangular, so it is not compatible with the type like 'compound'.
highlight_entities( pathway, set, how = "any", num_combine = mean, name = "graphics_name", sep = ", ", no_sep = FALSE, show_type = "gene", fill_color = "tomato", remove_dot = TRUE, legend_name = NULL, use_cache = FALSE, return_graph = FALSE, directory = NULL )highlight_entities( pathway, set, how = "any", num_combine = mean, name = "graphics_name", sep = ", ", no_sep = FALSE, show_type = "gene", fill_color = "tomato", remove_dot = TRUE, legend_name = NULL, use_cache = FALSE, return_graph = FALSE, directory = NULL )
pathway |
pathway ID to be passed to 'pathway()' |
set |
vector of identifiers, or named vector of numeric values |
how |
if 'all', if node contains multiple IDs separated by 'sep', highlight if all the IDs are in query. if 'any', highlight if one of the IDs is in query. |
num_combine |
combining function if multiple hits are obtained per node |
name |
which column to search for |
sep |
separater for node names |
no_sep |
not separate node name |
show_type |
entitie type, default to 'gene' |
fill_color |
highlight color, default to 'tomato' |
remove_dot |
remove the "..." in the graphics name column |
legend_name |
legend name, NULL to suppress |
use_cache |
use cache or not |
return_graph |
return tbl_graph instead of plot |
directory |
directroy with XML files. ignore caching when specified. |
overlaid map
highlight_entities("hsa04110", c("CDKN2A"), legend_name="interesting")highlight_entities("hsa04110", c("CDKN2A"), legend_name="interesting")
identify if edges are involved in module reaction, and whether linked compounds are involved in the reaction. It would not be exactly the same as KEGG mapper. For instance, 'R04293' involved in 'M00912' is not included in KGML of 'ko01100'.
highlight_module(graph, kmo, name = "name", sep = " ", verbose = FALSE)highlight_module(graph, kmo, name = "name", sep = " ", verbose = FALSE)
graph |
tbl_graph |
kmo |
kegg_module class object which stores reaction |
name |
which column to search for |
sep |
separator for node names |
verbose |
show messages or not |
boolean vector
## Highlight module within the pathway graph <- create_test_pathway() mo <- create_test_module() graph <- graph |> highlight_module(mo)## Highlight module within the pathway graph <- create_test_pathway() mo <- create_test_module() graph <- graph |> highlight_module(mo)
identify if edges are involved in specific query. if multiple IDs are listed after separation by 'sep', only return TRUE if all the IDs are in the query.
highlight_set_edges(set, how = "all", name = "name", sep = " ", no_sep = FALSE)highlight_set_edges(set, how = "all", name = "name", sep = " ", no_sep = FALSE)
set |
set of identifiers |
how |
if 'all', if node contains multiple IDs separated by 'sep', highlight if all the IDs are in query. if 'any', highlight if one of the IDs is in query. |
name |
which column to search for |
sep |
separater for node names |
no_sep |
not separate node name |
boolean vector
graph <- create_test_pathway() ## Specify edge column by `name` ## In this example, edges having `degradation` value in ## `subtype_name` column will be highlighted graph <- graph |> activate("edges") |> mutate(hl=highlight_set_edges(c("degradation"), name="subtype_name"))graph <- create_test_pathway() ## Specify edge column by `name` ## In this example, edges having `degradation` value in ## `subtype_name` column will be highlighted graph <- graph |> activate("edges") |> mutate(hl=highlight_set_edges(c("degradation"), name="subtype_name"))
identify if nodes are involved in specific queriy. if multiple IDs are listed after separation by 'sep', only return TRUE if all the IDs are in the query.
highlight_set_nodes( set, how = "all", name = "name", sep = " ", no_sep = FALSE, remove_dot = TRUE )highlight_set_nodes( set, how = "all", name = "name", sep = " ", no_sep = FALSE, remove_dot = TRUE )
set |
set of identifiers |
how |
if 'all', if node contains multiple IDs separated by 'sep', highlight if all the IDs are in query. if 'any', highlight if one of the IDs is in query. |
name |
which column to search for |
sep |
separater for node names |
no_sep |
not separate node name |
remove_dot |
remove "..." after graphics name column |
boolean vector
graph <- create_test_pathway() ## Highlight set of nodes by specifying ID graph <- graph |> mutate(hl=highlight_set_nodes(c("hsa:51428"))) ## node column can be specified by `name` argument graph <- graph |> mutate(hl=highlight_set_nodes(c("DDX41"), name="graphics_name"))graph <- create_test_pathway() ## Highlight set of nodes by specifying ID graph <- graph |> mutate(hl=highlight_set_nodes(c("hsa:51428"))) ## node column can be specified by `name` argument graph <- graph |> mutate(hl=highlight_set_nodes(c("DDX41"), name="graphics_name"))
module KEGG module parsing function
module(mid, use_cache = FALSE, directory = NULL)module(mid, use_cache = FALSE, directory = NULL)
mid |
KEGG module ID |
use_cache |
use cache |
directory |
directory to save raw files |
list of module definition and reaction
module("M00003")module("M00003")
module_abundance weighted mean abundance of fraction of present KO in the block
module_abundance(mod_id, vec, num = 1, calc = "weighted_mean")module_abundance(mod_id, vec, num = 1, calc = "weighted_mean")
mod_id |
module ID |
vec |
KO-named vector of abundance without prefix 'ko:' |
num |
definition number when multiple definitions are present |
calc |
calculation of final results, mean or weighted_mean |
numeric value
module_abundance("M00003",c(1.2) |> setNames("K00927"))module_abundance("M00003",c(1.2) |> setNames("K00927"))
This converts module definitions consisting of KO identifiers to the expression by converting '+' and ' ' to 'AND', and ',' to 'OR'. After that, KO IDs specified by 'query' is inserted to expression by 'TRUE' or 'FALSE', and is evaluated. Please feel free to contact the bug, or modules that cannot be calculated. (Module definitions consisting of module IDs [M*] cannot be calculated)
module_completeness(kmo, query, name = "1")module_completeness(kmo, query, name = "1")
kmo |
module object |
query |
vector of KO |
name |
name of definitions when multiple definitions are present |
Below is quoted from https://www.genome.jp/kegg/module.html
'A space or a plus sign, representing a connection in the pathway or the molecular complex, is treated as an AND operator and a comma, used for alternatives, is treated as an OR operator. A minus sign designates an optional item in the complex.'
tibble
## Assess completeness based on one KO input test_complete <- module_completeness(create_test_module(), c("K00112"))## Assess completeness based on one KO input test_complete <- module_completeness(create_test_module(), c("K00112"))
module_text Obtain textual representation of module definition for all the blocks
module_text( kmo, name = "1", candidate_ko = NULL, paint_colour = "tomato", convert = NULL )module_text( kmo, name = "1", candidate_ko = NULL, paint_colour = "tomato", convert = NULL )
kmo |
module object |
name |
name of definition |
candidate_ko |
KO to highlight |
paint_colour |
color to highlight |
convert |
named vector converting the KO to gene name |
textual description of module definitions
mo <- create_test_module() tex <- module_text(mo)mo <- create_test_module() tex <- module_text(mo)
If you want to combine multiple KEGG pathways with their native coordinates, supply this function a vector of pathway IDs and row number. This returns the joined graph or list of graphs in which the coordinates are altered to panel the pathways.
multi_pathway_native(pathways, row_num = 2, return_list = FALSE)multi_pathway_native(pathways, row_num = 2, return_list = FALSE)
pathways |
pathway vector |
row_num |
row number |
return_list |
return list of graphs instead of joined graph |
graph adjusted for the position
## Pass multiple pathway IDs multi_pathway_native(list("hsa04110","hsa03460"))## Pass multiple pathway IDs multi_pathway_native(list("hsa04110","hsa03460"))
parsing the network elements starting with N
network(nid, use_cache = FALSE, directory = NULL)network(nid, use_cache = FALSE, directory = NULL)
nid |
KEGG NETWORK ID |
use_cache |
use cache |
directory |
directory to save raw files |
list of network definition
network("N00002")network("N00002")
obtain tbl_graph of KEGG network
network_graph(kne, type = "definition")network_graph(kne, type = "definition")
kne |
network object |
type |
definition or expanded |
tbl_graph
ne <- create_test_network() neg <- network_graph(ne)ne <- create_test_network() neg <- network_graph(ne)
given the matrix representing gene as row and sample as column, append the node value to node matrix and return tbl_graph object
node_matrix( graph, mat, gene_type = "SYMBOL", org = "hsa", org_db = NULL, num_combine = mean, name = "name", sep = " ", remove_dot = TRUE )node_matrix( graph, mat, gene_type = "SYMBOL", org = "hsa", org_db = NULL, num_combine = mean, name = "name", sep = " ", remove_dot = TRUE )
graph |
tbl_graph to append values to |
mat |
matrix representing gene as row and sample as column |
gene_type |
gene ID of matrix row |
org |
organism ID to convert ID |
org_db |
organism database to convert ID |
num_combine |
function to combine multiple numeric values |
name |
name column in node data, default to node |
sep |
separater of name, default to " " |
remove_dot |
remove "..." in the name |
tbl_graph
## Append data.frame to tbl_graph graph <- create_test_pathway() num_df <- data.frame(row.names=c("6737","51428"), "sample1"=c(1.1,1.2), "sample2"=c(1.5,2.2), check.names=FALSE) graph <- graph |> node_matrix(num_df, gene_type="ENTREZID")## Append data.frame to tbl_graph graph <- create_test_pathway() num_df <- data.frame(row.names=c("6737","51428"), "sample1"=c(1.1,1.2), "sample2"=c(1.5,2.2), check.names=FALSE) graph <- graph |> node_matrix(num_df, gene_type="ENTREZID")
simply add numeric attribute to node of tbl_graph
node_numeric( num, num_combine = mean, name = "name", how = "any", sep = " ", remove_dot = TRUE )node_numeric( num, num_combine = mean, name = "name", how = "any", sep = " ", remove_dot = TRUE )
num |
named vector or tibble with id and value column |
num_combine |
how to combine number when multiple hit in the same node |
name |
name of column to match for |
how |
how to match the node IDs with the queries 'any' or 'all' |
sep |
separater for name, default to " " |
remove_dot |
remove "..." in the name |
numeric vector
graph <- create_test_pathway() graph <- graph |> mutate(num=node_numeric(c(1.1) |> setNames("hsa:6737")))graph <- create_test_pathway() graph <- graph |> mutate(num=node_numeric(c(1.1) |> setNames("hsa:6737")))
Given module definition and block number, Recursively obtain graphical represencation of block and connect them by pseudo-nodes representing blocks.
obtain_sequential_module_definition(kmo, name = "1", block = NULL)obtain_sequential_module_definition(kmo, name = "1", block = NULL)
kmo |
module object |
name |
name of definition when multiple definitions are present |
block |
specify if need to parse specific block |
list of module definitions
mo <- create_test_module() sequential_mod <- obtain_sequential_module_definition(mo)mo <- create_test_module() sequential_mod <- obtain_sequential_module_definition(mo)
The function first exports the image, combine it with the original image. Note that if the legend is outside the pathway image, the result will not show it correctly. Place the legend inside the panel by adding the theme such as theme(legend.position=c(0.5, 0.5)).
output_overlay_image( gg, with_legend = TRUE, use_cache = TRUE, high_res = FALSE, res = 72, out = NULL, directory = NULL, transparent_colors = c("#FFFFFF", "#BFBFFF", "#BFFFBF", "#7F7F7F", "#808080"), unlink = TRUE, with_legend_image = FALSE, legend_horiz = FALSE, legend_space = 100 )output_overlay_image( gg, with_legend = TRUE, use_cache = TRUE, high_res = FALSE, res = 72, out = NULL, directory = NULL, transparent_colors = c("#FFFFFF", "#BFBFFF", "#BFFFBF", "#7F7F7F", "#808080"), unlink = TRUE, with_legend_image = FALSE, legend_horiz = FALSE, legend_space = 100 )
gg |
ggraph object |
with_legend |
if legend (group-box) is in gtable, output them |
use_cache |
use BiocFileCache for caching the image |
high_res |
use 2x resolution image |
res |
resolution parameter passed to saving the ggplot2 image |
out |
output file name |
directory |
specify if you have already downloaded the image |
transparent_colors |
transparent colors |
unlink |
unlink the intermediate image |
with_legend_image |
append legend image instead of using gtable |
legend_horiz |
append legend to the bottom of the image |
legend_space |
legend spacing specification (in pixel) |
If the legend must be placed outside the image, the users can set with_legend_image to TRUE. This will create another legend only image and concatenate it with the pathway image. legend_space option can be specified to control the spacing for the legend. If need to append horizontal legend, enable legend_horiz option.
By default, unlink option is enabled which means the function will delete the intermediate files.
output the image and return the path
## Not run: ouput_overlay_image(ggraph(pathway("hsa04110"))) ## End(Not run)## Not run: ouput_overlay_image(ggraph(pathway("hsa04110"))) ## End(Not run)
Overlay the raw KEGG pathway image on ggraph
overlay_raw_map( pid = NULL, directory = NULL, transparent_colors = c("#FFFFFF", "#BFBFFF", "#BFFFBF"), adjust = FALSE, adjust_manual_x = NULL, adjust_manual_y = NULL, clip = FALSE, use_cache = TRUE, interpolate = TRUE, high_res = FALSE, fix_coordinates = TRUE )overlay_raw_map( pid = NULL, directory = NULL, transparent_colors = c("#FFFFFF", "#BFBFFF", "#BFFFBF"), adjust = FALSE, adjust_manual_x = NULL, adjust_manual_y = NULL, clip = FALSE, use_cache = TRUE, interpolate = TRUE, high_res = FALSE, fix_coordinates = TRUE )
pid |
pathway ID |
directory |
directory to store images if not use cache |
transparent_colors |
make these colors transparent to overlay Typical choice of colors would be: "#CCCCCC", "#FFFFFF","#BFBFFF","#BFFFBF", "#7F7F7F", "#808080", "#ADADAD","#838383","#B3B3B3" |
adjust |
adjust the x- and y-axis location by 0.5 in data coordinates |
adjust_manual_x |
adjust the position manually for x-axis Override 'adjust' |
adjust_manual_y |
adjust the position manually for y-axis Override 'adjust' |
clip |
clip the both end of x- and y-axis by one dot |
use_cache |
whether to use BiocFileCache() |
interpolate |
parameter in annotation_raster() |
high_res |
Use high resolution (2x) image for the overlay |
fix_coordinates |
fix the coordinate (coord_fixed) |
ggplot2 object
## Need `pathway_id` column in graph ## if the function is to automatically infer graph <- create_test_pathway() |> mutate(pathway_id="hsa04110") ggraph(graph) + overlay_raw_map()## Need `pathway_id` column in graph ## if the function is to automatically infer graph <- create_test_pathway() |> mutate(pathway_id="hsa04110") ggraph(graph) + overlay_raw_map()
KEGG pathway parsing function
pathway( pid, directory = NULL, use_cache = FALSE, group_rect_nudge = 2, node_rect_nudge = 0, invert_y = TRUE, add_pathway_id = TRUE, return_tbl_graph = TRUE, return_image = FALSE )pathway( pid, directory = NULL, use_cache = FALSE, group_rect_nudge = 2, node_rect_nudge = 0, invert_y = TRUE, add_pathway_id = TRUE, return_tbl_graph = TRUE, return_image = FALSE )
pid |
pathway id |
directory |
directory to download KGML |
use_cache |
whether to use BiocFileCache |
group_rect_nudge |
nudge the position of group node default to add slight increase to show the group node |
node_rect_nudge |
nudge the position of all node |
invert_y |
invert the y position to match with R graphics |
add_pathway_id |
add pathway id to graph, default to TRUE needed for the downstream analysis |
return_tbl_graph |
return tbl_graph object, if FALSE, return igraph |
return_image |
return the image URL |
tbl_graph by default
pathway("hsa04110")pathway("hsa04110")
pathway_abundance
pathway_abundance(id, vec, num = 1)pathway_abundance(id, vec, num = 1)
id |
pathway id |
vec |
named vector of abundance |
num |
number of module definition |
numeric value
pathway_abundance("ko00270", c(1.2) |> `setNames`("K00927"))pathway_abundance("ko00270", c(1.2) |> `setNames`("K00927"))
obtain the list of pathway information
pathway_info(pid, use_cache = FALSE, directory = NULL)pathway_info(pid, use_cache = FALSE, directory = NULL)
pid |
KEGG Pathway id |
use_cache |
whether to use cache |
directory |
directory of file |
list of orthology and module contained in the pathway
pathway_info("hsa04110")pathway_info("hsa04110")
plot the output of network_graph
plot_kegg_network(g, layout = "nicely")plot_kegg_network(g, layout = "nicely")
g |
graph object returned by 'network()' |
layout |
layout to be used, default to nicely |
ggplot2 object
ne <- create_test_network() ## Output of `network_graph` must be used with plot_kegg_network neg <- network_graph(ne) plt <- plot_kegg_network(neg)ne <- create_test_network() ## Output of `network_graph` must be used with plot_kegg_network neg <- network_graph(ne) plt <- plot_kegg_network(neg)
wrapper function for plotting network representation of module definition blocks
plot_module_blocks(all_steps, layout = "kk")plot_module_blocks(all_steps, layout = "kk")
all_steps |
the result of 'obtain_sequential_module_definition()' |
layout |
ggraph layout parameter |
ggplot2 object
mo <- create_test_module() ## The output of `obtain_sequential_module_definition` ## is used for `plot_module_blocks()` sequential_mod <- obtain_sequential_module_definition(mo) plt <- plot_module_blocks(sequential_mod)mo <- create_test_module() ## The output of `obtain_sequential_module_definition` ## is used for `plot_module_blocks()` sequential_mod <- obtain_sequential_module_definition(mo) plt <- plot_module_blocks(sequential_mod)
plot the text representation of KEGG modules
plot_module_text(plot_list, show_name = "name")plot_module_text(plot_list, show_name = "name")
plot_list |
the result of 'module_text()' |
show_name |
name column to be plotted |
ggplot2 object
mo <- create_test_module() ## The output of `module_text` is used for `plot_module_text()` tex <- module_text(mo) plt <- plot_module_text(tex)mo <- create_test_module() ## The output of `module_text` is used for `plot_module_text()` tex <- module_text(mo) plt <- plot_module_text(tex)
process the KGML containing graphics type of 'line', like global maps e.g. ko01100. Recursively add nodes and edges connecting them based on 'coords' properties in KGML.
process_line(g, invert_y = TRUE, verbose = FALSE)process_line(g, invert_y = TRUE, verbose = FALSE)
g |
graph |
invert_y |
whether to invert the position, default to TRUE should match with 'pathway' function |
verbose |
show progress |
We cannot show directed arrows, as coords are not ordered to show direction.
tbl_graph
## For those containing nodes with the graphic type of `line`, ## parse the coords attributes to edges. gm_test <- create_test_pathway(line=TRUE) test <- process_line(gm_test)## For those containing nodes with the graphic type of `line`, ## parse the coords attributes to edges. gm_test <- create_test_pathway(line=TRUE) test <- process_line(gm_test)
process the kgml of global maps e.g. in ko01100
process_reaction(g, single_edge = FALSE, keep_no_reaction = TRUE)process_reaction(g, single_edge = FALSE, keep_no_reaction = TRUE)
g |
graph |
single_edge |
discard one edge when edge type is 'reversible' |
keep_no_reaction |
keep edges not related to reaction |
Typically, 'process_line' function is used to draw relationships as in the original KGML positions, however, the 'coords' properties is not considering the direction of reactions (substrate -> product), thus if it is preferred, 'process_reaction' is used to populate new edges corresponding to 'substrate -> product' and 'product -> substrate' if the reaction is reversible.
tbl_graph
gm_test <- create_test_pathway(line=TRUE) test <- process_reaction(gm_test)gm_test <- create_test_pathway(line=TRUE) test <- process_reaction(gm_test)
given enrichResult class object, return the ggplot object with raw KEGG map overlaid on enriched pathway. Can be used with the function such as 'clusterProfiler::enrichKEGG' and 'MicrobiomeProfiler::enrichKO()'
rawMap( enrich, pathway_number = 1, pid = NULL, fill_color = "red", how = "any", white_background = TRUE, infer = FALSE, name = "name", sep = " ", remove_dot = TRUE )rawMap( enrich, pathway_number = 1, pid = NULL, fill_color = "red", how = "any", white_background = TRUE, infer = FALSE, name = "name", sep = " ", remove_dot = TRUE )
enrich |
enrichResult or gseaResult class object, or list of them |
pathway_number |
pathway number sorted by p-values |
pid |
pathway id, override pathway_number if specified |
fill_color |
color for genes |
how |
how to match the node IDs with the queries 'any' or 'all' |
white_background |
fill background color white |
infer |
if TRUE, append the prefix to queried IDs based on pathway ID |
name |
name of column to match for |
sep |
separater for name, default to " " |
remove_dot |
remove "..." in the name |
ggraph with overlaid KEGG map
if (require("clusterProfiler")) { cp <- enrichKEGG(c("1029","4171")) ## Multiple class object can be passed by list rawMap(list(cp,cp), pid="hsa04110") }if (require("clusterProfiler")) { cp <- enrichKEGG(c("1029","4171")) ## Multiple class object can be passed by list rawMap(list(cp,cp), pid="hsa04110") }
given named vector of quantitative values, return the ggplot object with raw KEGG map overlaid. Colors can be changed afterwards.
rawValue( values, pid = NULL, column = "name", show_type = "gene", how = "any", white_background = TRUE, auto_add = FALSE, man_graph = NULL, sep = " ", remove_dot = TRUE )rawValue( values, pid = NULL, column = "name", show_type = "gene", how = "any", white_background = TRUE, auto_add = FALSE, man_graph = NULL, sep = " ", remove_dot = TRUE )
values |
named vector, or list of them |
pid |
pathway id |
column |
name of column to match for |
show_type |
type to be shown |
how |
how to match the node IDs with the queries 'any' or 'all' |
white_background |
fill background color white |
auto_add |
automatically add prefix based on pathway prefix |
man_graph |
provide manual tbl_graph |
sep |
separater for name, default to " " |
remove_dot |
remove "..." in the name typically, "gene", "ortholog", or "compound" |
ggraph with overlaid KEGG map
## Colorize by passing the named vector of numeric values rv <- rawValue(c(1.1) |> setNames("hsa:6737"), man_graph=create_test_pathway())## Colorize by passing the named vector of numeric values rv <- rawValue(c(1.1) |> setNames("hsa:6737"), man_graph=create_test_pathway())
In the map, where lines are converted to edges, identify compounds that are linked by the reaction. Give the original edge ID of KGML (orig.id in edge table), and return the original compound node ID
return_line_compounds(g, orig)return_line_compounds(g, orig)
g |
tbl_graph object |
orig |
original edge ID |
vector of original compound node IDs
## For those containing nodes with the graphic type of `line` ## This returns no IDs as no edges are present gm_test <- create_test_pathway(line=TRUE) test <- process_line(gm_test) |> return_line_compounds(1)## For those containing nodes with the graphic type of `line` ## This returns no IDs as no edges are present gm_test <- create_test_pathway(line=TRUE) test <- process_line(gm_test) |> return_line_compounds(1)
place stamp on the specified node
stamp(name, color = "red", which_column = "name", xval = 2, yval = 2)stamp(name, color = "red", which_column = "name", xval = 2, yval = 2)
name |
name of the nodes |
color |
color of the stamp |
which_column |
which node column to search |
xval |
adjustment value for x-axis |
yval |
adjustment value for y-axis |
ggplot2 object
test_pathway <- create_test_pathway() plt <- ggraph(test_pathway, layout="manual", x=x, y=y) + stamp("hsa:6737")test_pathway <- create_test_pathway() plt <- ggraph(test_pathway, layout="manual", x=x, y=y) + stamp("hsa:6737")