R/networkplot.R
In taowenmicro/ggClusterNet:
Defines functions
network
Documented in
network
#' Microbial related network
#'
#' @param ps phyloseq Object, contains OTU tables, tax table and map table, represented sequences,phylogenetic tree.
#' @param N filter OTU tables by abundance.The defult, N=0, extract the top N number relative abundance of OTU.
#' @param r.threshold The defult, r.threshold=0.6, it represents the correlation that the absolute value
#' of the correlation threshold is greater than 0.6. the value range of correlation threshold from 0 to 1.
#' @param p.threshold The defult, p.threshold=0.05, it represents significance threshold below 0.05.
#' @param big culculate big network TURE or FALSE
#' @param select_layout TURE or FALSE
#' @param layout_net defult "model_maptree"
#' @param method method for Correlation calculation,method="pearson" is the default value. The alternatives to be passed to cor are "spearman" and "kendall".
#' @param label Whether to add node label.
#' @param group Separate Group.
#' @param lay layout which network show
#' @param path save path of all of network analyse.
#' @param fill fill coulor of node
#' @param size node size
#' @param zipi zipi Calculation
#' @param step Random network sampling times
#' @param R repeat number of p value calculate
#' @param ncpus number of cpus used for sparcc
#' @param layout_net select layout from ggClusterNet
#' @param big TRUE or FALSE the number of micro data was so many (> 300),you can chose TREU
#' @examples
#' data(ps)
#' result = network (ps = ps,N = 100,r.threshold=0.6,p.threshold=0.05,label = FALSE,path = path ,zipi = TRUE)
#' result[[1]]
#' result[[2]]
#' @return list which contains OTU correlation matrix
#' @author Contact: Tao Wen \email{taowen@@njau.edu.cn} Penghao Xie \email{2019103106@@njau.edu.cn} yongxin liu \email{yxliu@@genetics.ac.cn} Jun Yuan \email{junyuan@@njau.edu.cn}
#' @references
#'
#' Tao Wen#, Penghao Xie#, Shengdie Yang, Guoqing Niu, Xiaoyu Liu, Zhexu Ding, Chao Xue, Yong-Xin Liu *, Qirong Shen, Jun Yuan*
#' ggClusterNet: an R package for microbiome network analysis and modularity-based multiple network layouts
#' iMeta 2022,DOI: \url{doi: 10.1002/imt2.32}
#' @export
network = function(otu = NULL,
tax = NULL,
map = NULL,
ps = NULL,
N = 0,
big = FALSE,
select_layout = FALSE,
layout_net = "model_maptree",
r.threshold = 0.6,
p.threshold = 0.05,
method = "spearman",
label = FALSE,
lab = "elements",
group = "Group",
path = "./",
fill = "Phylum",
size = "igraph.degree",
scale = TRUE,
zipi = FALSE,
clu_method = "cluster_fast_greedy",
step = 100,
yourmem = theme_void(),
ncol = 3,
nrow = 1,
R = 10,
ncpus = 1,
a = 1.5
){
#--imput data ---------
ps = inputMicro(otu,tax,map,tree,ps,group = group)
#---------------------------data washing-------------------------------------------------
#transform to relative abundance
if (scale == TRUE) {
ps_rela = scale_micro(ps = ps,method = "rela")
} else {
ps_rela <- ps
}
mapping = as.data.frame(sample_data(ps))
ps_sub = ps
y = matrix(1,nrow = 14,ncol = length(unique(mapping$Group)))
#--transmit N
# d = N
layouts = as.character(unique(mapping$Group))
mapping$ID = row.names(mapping)
##################---------------------------------------calculate network---------------------------------------------------
plots = list()
plots1 = list()
# layout = layouts[2]
aa = 1
for (layout in layouts) {
mapi <- mapping[mapping$Group == layout,]
psi = phyloseq(otu_table(ps),
tax_table(ps),
sample_data(mapi)
) %>%
filter_OTU_ps(Top = N) %>%
filter_taxa( function(x) sum(x ) > 0 , TRUE)
print(layout)
if (big == TRUE) {
result = cor_Big_micro(ps = psi,N = 0,r.threshold= r.threshold,p.threshold=p.threshold,method = method,scale = FALSE)
a = 2
} else if(big == FALSE){
result = corMicro (ps = psi,N = 0,r.threshold= r.threshold,p.threshold=p.threshold,method = method,R = R,ncpus = ncpus)
a = 1
}
print("cor matrix culculating over")
cor = result[[1]] #Extract correlation matrix
if (cor %>% as.vector() %>% max() == 0) {
stop("The connect value in cor matrix all was zone")
}
print("1")
if (select_layout == TRUE) {
node = culculate_node_axis(
cor.matrix = cor,
layout = layout_net,
seed = 1,
group = NULL,
model = FALSE,
method = clu_method)
}else if(select_layout == FALSE) {
result2 <- model_Gephi.2(cor = cor,
method = clu_method,
seed = 12
)
node = result2[[1]]
}
ps_net = psi
otu_table = as.data.frame(t(vegan_otu(ps_net)))
tax_table = as.data.frame(vegan_tax(ps_net))
#---node ann # -----------
nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table)
#-----culculate edge #--------
# edge = edgeBuild(cor = cor,plotcord = node)
tem1 = cor %>%
tidyfst::mat_df() %>%
dplyr::filter(row != col) %>%
dplyr::rename(OTU_1 = row,OTU_2 = col,weight = value ) %>%
dplyr::filter(weight != 0)
head(tem1)
tem2 = tem1 %>% dplyr::left_join(node,by = c("OTU_1" = "elements")) %>%
dplyr::rename(Y1 = X2)
head(tem2)
tem3 = node %>%
dplyr::rename(Y2 = X2,X2 = X1) %>%
dplyr::right_join(tem2,by = c("elements" = "OTU_2")) %>%
dplyr::rename(OTU_2 = elements)
edge = tem3 %>%
dplyr::mutate(
cor = ifelse(weight > 0,"+","-")
)
colnames(edge)[8] = "cor"
#-------output---edges and nodes--to Gephi --imput--
edge_Gephi = data.frame(source = edge$OTU_1,target = edge$OTU_2,correlation = edge$weight,direct= "undirected",cor = edge$cor)
# building node table
node_Gephi = data.frame(ID= nodes$elements,nodes[4:dim(nodes)[2]],Label = nodes$elements)
idedge <- c(as.character(edge_Gephi$source),as.character(edge_Gephi$target))
idedge <- unique(idedge)
row.names(node_Gephi) <- as.character(node_Gephi$ID)
node_Gephi1 <- node_Gephi[idedge, ]
write.csv(edge_Gephi ,paste(path,"/",layout,"_Gephi_edge.csv",sep = ""),row.names = FALSE)
write.csv(node_Gephi,paste(path,"/",layout,"_Gephi_allnode.csv",sep = ""),row.names = FALSE)
write.csv(node_Gephi1,paste(path,"/",layout,"_Gephi_edgenode.csv",sep = ""),row.names = FALSE)
# a = nodeEdge(cor = cor)[[1]]
# dim(a)
# head(a)
# a %>% filter(weight != 0)
# as.vector(lower.tri(cor))
igraph = igraph::graph_from_data_frame(nodeEdge(cor = cor)[[1]], directed = FALSE, vertices = nodeEdge(cor = cor)[[2]])
nodepro = node_properties(igraph)
write.csv(nodepro,paste(path,"/",layout,"_node_properties.csv",sep = ""),row.names = TRUE)
nodeG = merge(nodes,nodepro,by = "row.names",all.x = TRUE)
row.names(nodeG) = nodeG$Row.names
nodeG$Row.names = NULL
numna = (dim(nodeG)[2] - 3) : dim(nodeG)[2]
nodeG[,numna][is.na(nodeG[,numna])] = 0
head(nodeG)
pnet <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor),
data = edge, size = 0.5,alpha = 0.3) +
geom_point(aes(X1, X2,fill = !!sym(fill),size = !!sym(size)),pch = 21, data = nodeG) +
labs( title = paste(layout,"network",sep = "_")) +
# geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodeG) +
# geom_text(aes(X1, X2,label = elements),pch = 21, data = nodeG) +
scale_colour_manual(values = c("#377EB8","#E41A1C")) +
scale_size(range = c(4, 14)) +
scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
theme(panel.background = element_blank()) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
theme(legend.background = element_rect(colour = NA)) +
theme(panel.background = element_rect(fill = "white", colour = NA)) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
pnet
pnet1 <- ggplot() + geom_curve(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor),
data = edge, size = 0.5,alpha = 0.3,curvature = -0.2) +
geom_point(aes(X1, X2,fill = !!sym(fill),size = !!sym(size)),pch = 21, data = nodeG) +
labs( title = paste(layout,"network",sep = "_")) +
# geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodeG) +
# geom_text(aes(X1, X2,label = elements),pch = 21, data = nodeG) +
scale_colour_manual(values = c("#377EB8","#E41A1C")) +
scale_size(range = c(4, 14)) +
scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
theme(panel.background = element_blank()) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
theme(legend.background = element_rect(colour = NA)) +
theme(panel.background = element_rect(fill = "white", colour = NA)) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
pnet1
if (label == TRUE ) {
pnet <- pnet + geom_text_repel(aes(X1, X2,label=!!sym(lab)),size=4, data = nodeG)
pnet1 <- pnet1 + geom_text_repel(aes(X1, X2,label=!!sym(lab)),size=4, data = nodeG)
}
plotname = paste(path,"/network",layout,".pdf",sep = "")
# ggsave(plotname, pnet, width = 16 * dim(nodeG)[1]/200, height = 14*dim(nodeG)[1]/200)
ggsave(plotname, pnet, width = 16*a , height = 14*a)
plotname = paste(path,"/network",layout,"_cover.pdf",sep = "")
# ggsave(plotname, pnet, width = 16 * dim(nodeG)[1]/200, height = 14*dim(nodeG)[1]/200)
ggsave(plotname, pnet1, width = 16*a , height = 14*a)
plots[[aa]] = pnet
plots1[[aa]] = pnet1
# nodepro = node_properties(igraph)
if (zipi ) {
#----culculate zi pi
res = ZiPiPlot(igraph = igraph,method = clu_method)
p <- res[[1]]
ggsave(paste(path,"/",layout,"_ZiPi.pdf",sep = ""),p,width = 12, height = 10)
ZiPi <- res[[2]]
write.csv(ZiPi ,paste(path,"/",layout,"ZiPi.csv",sep = ""),row.names = FALSE)
}
netpro_result<- net_properties(igraph)
colnames(netpro_result)<-layout
result = random_Net_compate(igraph = igraph, type = "gnm", step = 100, netName = layout)
p1 = result[[1]]
sum_net = result[[4]]
plotname = paste(path,"/Power_law_distribution_",layout,".pdf",sep = "")
ggsave(plotname, p1, width = 8, height =6)
write.csv(sum_net,paste(path,"/",layout,"_net_VS_erdos_properties.csv",sep = ""),row.names = TRUE)
y = as.data.frame(y)
colnames(y) = layouts
# head(y)
y[layout] = netpro_result[,1]
row.names(y) = row.names(netpro_result)
aa = aa+1
}
plotname = paste(path,"/network_all.pdf",sep = "")
p = ggpubr::ggarrange(plotlist = plots,
common.legend = TRUE, legend="right",ncol = ncol,nrow = nrow)
p1 = ggpubr::ggarrange(plotlist = plots1,
common.legend = TRUE, legend="right",ncol = ncol,nrow = nrow)
if (length(layouts) == 1) {
p = pnet
p1 = pnet1
}
return(list(p,y,p1))
}
taowenmicro/ggClusterNet documentation built on March 29, 2024, 1:32 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions network
Documented in network
#' Microbial related network
#'
#' @param ps phyloseq Object, contains OTU tables, tax table and map table, represented sequences,phylogenetic tree.
#' @param N filter OTU tables by abundance.The defult, N=0, extract the top N number relative abundance of OTU.
#' @param r.threshold The defult, r.threshold=0.6, it represents the correlation that the absolute value
#' of the correlation threshold is greater than 0.6. the value range of correlation threshold from 0 to 1.
#' @param p.threshold The defult, p.threshold=0.05, it represents significance threshold below 0.05.
#' @param big culculate big network TURE or FALSE
#' @param select_layout TURE or FALSE
#' @param layout_net defult "model_maptree"
#' @param method method for Correlation calculation,method="pearson" is the default value. The alternatives to be passed to cor are "spearman" and "kendall".
#' @param label Whether to add node label.
#' @param group Separate Group.
#' @param lay layout which network show
#' @param path save path of all of network analyse.
#' @param fill fill coulor of node
#' @param size node size
#' @param zipi zipi Calculation
#' @param step Random network sampling times
#' @param R repeat number of p value calculate
#' @param ncpus number of cpus used for sparcc
#' @param layout_net select layout from ggClusterNet
#' @param big TRUE or FALSE the number of micro data was so many (> 300),you can chose TREU
#' @examples
#' data(ps)
#' result = network (ps = ps,N = 100,r.threshold=0.6,p.threshold=0.05,label = FALSE,path = path ,zipi = TRUE)
#' result[[1]]
#' result[[2]]
#' @return list which contains OTU correlation matrix
#' @author Contact: Tao Wen \email{taowen@@njau.edu.cn} Penghao Xie \email{2019103106@@njau.edu.cn} yongxin liu \email{yxliu@@genetics.ac.cn} Jun Yuan \email{junyuan@@njau.edu.cn}
#' @references
#'
#' Tao Wen#, Penghao Xie#, Shengdie Yang, Guoqing Niu, Xiaoyu Liu, Zhexu Ding, Chao Xue, Yong-Xin Liu *, Qirong Shen, Jun Yuan*
#' ggClusterNet: an R package for microbiome network analysis and modularity-based multiple network layouts
#' iMeta 2022,DOI: \url{doi: 10.1002/imt2.32}
#' @export
network = function(otu = NULL,
tax = NULL,
map = NULL,
ps = NULL,
N = 0,
big = FALSE,
select_layout = FALSE,
layout_net = "model_maptree",
r.threshold = 0.6,
p.threshold = 0.05,
method = "spearman",
label = FALSE,
lab = "elements",
group = "Group",
path = "./",
fill = "Phylum",
size = "igraph.degree",
scale = TRUE,
zipi = FALSE,
clu_method = "cluster_fast_greedy",
step = 100,
yourmem = theme_void(),
ncol = 3,
nrow = 1,
R = 10,
ncpus = 1,
a = 1.5
){
#--imput data ---------
ps = inputMicro(otu,tax,map,tree,ps,group = group)
#---------------------------data washing-------------------------------------------------
#transform to relative abundance
if (scale == TRUE) {
ps_rela = scale_micro(ps = ps,method = "rela")
} else {
ps_rela <- ps
}
mapping = as.data.frame(sample_data(ps))
ps_sub = ps
y = matrix(1,nrow = 14,ncol = length(unique(mapping$Group)))
#--transmit N
# d = N
layouts = as.character(unique(mapping$Group))
mapping$ID = row.names(mapping)
##################---------------------------------------calculate network---------------------------------------------------
plots = list()
plots1 = list()
# layout = layouts[2]
aa = 1
for (layout in layouts) {
mapi <- mapping[mapping$Group == layout,]
psi = phyloseq(otu_table(ps),
tax_table(ps),
sample_data(mapi)
) %>%
filter_OTU_ps(Top = N) %>%
filter_taxa( function(x) sum(x ) > 0 , TRUE)
print(layout)
if (big == TRUE) {
result = cor_Big_micro(ps = psi,N = 0,r.threshold= r.threshold,p.threshold=p.threshold,method = method,scale = FALSE)
a = 2
} else if(big == FALSE){
result = corMicro (ps = psi,N = 0,r.threshold= r.threshold,p.threshold=p.threshold,method = method,R = R,ncpus = ncpus)
a = 1
}
print("cor matrix culculating over")
cor = result[[1]] #Extract correlation matrix
if (cor %>% as.vector() %>% max() == 0) {
stop("The connect value in cor matrix all was zone")
}
print("1")
if (select_layout == TRUE) {
node = culculate_node_axis(
cor.matrix = cor,
layout = layout_net,
seed = 1,
group = NULL,
model = FALSE,
method = clu_method)
}else if(select_layout == FALSE) {
result2 <- model_Gephi.2(cor = cor,
method = clu_method,
seed = 12
)
node = result2[[1]]
}
ps_net = psi
otu_table = as.data.frame(t(vegan_otu(ps_net)))
tax_table = as.data.frame(vegan_tax(ps_net))
#---node ann # -----------
nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table)
#-----culculate edge #--------
# edge = edgeBuild(cor = cor,plotcord = node)
tem1 = cor %>%
tidyfst::mat_df() %>%
dplyr::filter(row != col) %>%
dplyr::rename(OTU_1 = row,OTU_2 = col,weight = value ) %>%
dplyr::filter(weight != 0)
head(tem1)
tem2 = tem1 %>% dplyr::left_join(node,by = c("OTU_1" = "elements")) %>%
dplyr::rename(Y1 = X2)
head(tem2)
tem3 = node %>%
dplyr::rename(Y2 = X2,X2 = X1) %>%
dplyr::right_join(tem2,by = c("elements" = "OTU_2")) %>%
dplyr::rename(OTU_2 = elements)
edge = tem3 %>%
dplyr::mutate(
cor = ifelse(weight > 0,"+","-")
)
colnames(edge)[8] = "cor"
#-------output---edges and nodes--to Gephi --imput--
edge_Gephi = data.frame(source = edge$OTU_1,target = edge$OTU_2,correlation = edge$weight,direct= "undirected",cor = edge$cor)
# building node table
node_Gephi = data.frame(ID= nodes$elements,nodes[4:dim(nodes)[2]],Label = nodes$elements)
idedge <- c(as.character(edge_Gephi$source),as.character(edge_Gephi$target))
idedge <- unique(idedge)
row.names(node_Gephi) <- as.character(node_Gephi$ID)
node_Gephi1 <- node_Gephi[idedge, ]
write.csv(edge_Gephi ,paste(path,"/",layout,"_Gephi_edge.csv",sep = ""),row.names = FALSE)
write.csv(node_Gephi,paste(path,"/",layout,"_Gephi_allnode.csv",sep = ""),row.names = FALSE)
write.csv(node_Gephi1,paste(path,"/",layout,"_Gephi_edgenode.csv",sep = ""),row.names = FALSE)
# a = nodeEdge(cor = cor)[[1]]
# dim(a)
# head(a)
# a %>% filter(weight != 0)
# as.vector(lower.tri(cor))
igraph = igraph::graph_from_data_frame(nodeEdge(cor = cor)[[1]], directed = FALSE, vertices = nodeEdge(cor = cor)[[2]])
nodepro = node_properties(igraph)
write.csv(nodepro,paste(path,"/",layout,"_node_properties.csv",sep = ""),row.names = TRUE)
nodeG = merge(nodes,nodepro,by = "row.names",all.x = TRUE)
row.names(nodeG) = nodeG$Row.names
nodeG$Row.names = NULL
numna = (dim(nodeG)[2] - 3) : dim(nodeG)[2]
nodeG[,numna][is.na(nodeG[,numna])] = 0
head(nodeG)
pnet <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor),
data = edge, size = 0.5,alpha = 0.3) +
geom_point(aes(X1, X2,fill = !!sym(fill),size = !!sym(size)),pch = 21, data = nodeG) +
labs( title = paste(layout,"network",sep = "_")) +
# geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodeG) +
# geom_text(aes(X1, X2,label = elements),pch = 21, data = nodeG) +
scale_colour_manual(values = c("#377EB8","#E41A1C")) +
scale_size(range = c(4, 14)) +
scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
theme(panel.background = element_blank()) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
theme(legend.background = element_rect(colour = NA)) +
theme(panel.background = element_rect(fill = "white", colour = NA)) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
pnet
pnet1 <- ggplot() + geom_curve(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor),
data = edge, size = 0.5,alpha = 0.3,curvature = -0.2) +
geom_point(aes(X1, X2,fill = !!sym(fill),size = !!sym(size)),pch = 21, data = nodeG) +
labs( title = paste(layout,"network",sep = "_")) +
# geom_text_repel(aes(X1, X2,label = elements),pch = 21, data = nodeG) +
# geom_text(aes(X1, X2,label = elements),pch = 21, data = nodeG) +
scale_colour_manual(values = c("#377EB8","#E41A1C")) +
scale_size(range = c(4, 14)) +
scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
theme(panel.background = element_blank()) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
theme(legend.background = element_rect(colour = NA)) +
theme(panel.background = element_rect(fill = "white", colour = NA)) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
pnet1
if (label == TRUE ) {
pnet <- pnet + geom_text_repel(aes(X1, X2,label=!!sym(lab)),size=4, data = nodeG)
pnet1 <- pnet1 + geom_text_repel(aes(X1, X2,label=!!sym(lab)),size=4, data = nodeG)
}
plotname = paste(path,"/network",layout,".pdf",sep = "")
# ggsave(plotname, pnet, width = 16 * dim(nodeG)[1]/200, height = 14*dim(nodeG)[1]/200)
ggsave(plotname, pnet, width = 16*a , height = 14*a)
plotname = paste(path,"/network",layout,"_cover.pdf",sep = "")
# ggsave(plotname, pnet, width = 16 * dim(nodeG)[1]/200, height = 14*dim(nodeG)[1]/200)
ggsave(plotname, pnet1, width = 16*a , height = 14*a)
plots[[aa]] = pnet
plots1[[aa]] = pnet1
# nodepro = node_properties(igraph)
if (zipi ) {
#----culculate zi pi
res = ZiPiPlot(igraph = igraph,method = clu_method)
p <- res[[1]]
ggsave(paste(path,"/",layout,"_ZiPi.pdf",sep = ""),p,width = 12, height = 10)
ZiPi <- res[[2]]
write.csv(ZiPi ,paste(path,"/",layout,"ZiPi.csv",sep = ""),row.names = FALSE)
}
netpro_result<- net_properties(igraph)
colnames(netpro_result)<-layout
result = random_Net_compate(igraph = igraph, type = "gnm", step = 100, netName = layout)
p1 = result[[1]]
sum_net = result[[4]]
plotname = paste(path,"/Power_law_distribution_",layout,".pdf",sep = "")
ggsave(plotname, p1, width = 8, height =6)
write.csv(sum_net,paste(path,"/",layout,"_net_VS_erdos_properties.csv",sep = ""),row.names = TRUE)
y = as.data.frame(y)
colnames(y) = layouts
# head(y)
y[layout] = netpro_result[,1]
row.names(y) = row.names(netpro_result)
aa = aa+1
}
plotname = paste(path,"/network_all.pdf",sep = "")
p = ggpubr::ggarrange(plotlist = plots,
common.legend = TRUE, legend="right",ncol = ncol,nrow = nrow)
p1 = ggpubr::ggarrange(plotlist = plots1,
common.legend = TRUE, legend="right",ncol = ncol,nrow = nrow)
if (length(layouts) == 1) {
p = pnet
p1 = pnet1
}
return(list(p,y,p1))
}
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