生信分析代谢通路可视化分析R工具包ggkegg的使用案例
发布时间：2023年12月20日
可视化 DESeq2 中的数值属性
通过提供通常用于转录组分析的 DESeq2 软件包的结果，可以在图形的节点中反映数值结果。该函数可用于此目的。通过将要在图形中反映的数值（例如，）指定为参数，可以将该值分配给节点。如果命中多个基因，则参数指定如何组合多个值（默认值为 ）。assign_deseq2log2FoldChangecolumnnumeric_combinemean
在这里，我们使用RNA-Seq数据集，该数据集分析了感染BK多瘤病毒的人尿路上皮细胞的转录组变化（Baker等人，2022）。从 Sequence Read Archive 获得的原始序列由?nf-core?处理，随后使用 和 进行分析。tximportsalmonDESeq2
library(ggkegg)
library(DESeq2)
library(org.Hs.eg.db)
library(dplyr)## The file stores DESeq() result on transcriptomic dataset deposited by Baker et al. 2022.
load("uro.deseq.res.rda")
res
#> class: DESeqDataSet
#> dim: 29744 26
#> metadata(1): version
#> assays(8): counts avgTxLength ... replaceCounts
#> replaceCooks
#> rownames(29744): A1BG A1BG-AS1 ... ZZEF1 ZZZ3
#> rowData names(27): baseMean baseVar ... maxCooks
#> replace
#> colnames(26): SRR14509882 SRR14509883 ... SRR14509906
#> SRR14509907
#> colData names(27): Assay.Type AvgSpotLen ...
#> viral_infection replaceable
vinf <- results(res, contrast=c("viral_infection","BKPyV (Dunlop) MOI=1","No infection"))
## LFC
g <- pathway("hsa04110") |> mutate(deseq2=assign_deseq2(vinf),
padj=assign_deseq2(vinf, column="padj"),
converted_name=convert_id("hsa"))
ggraph(g, layout="manual", x=x, y=y) +
geom_edge_parallel(width=0.5, arrow = arrow(length = unit(1, 'mm')),
start_cap = square(1, 'cm'),
end_cap = square(1.5, 'cm'), aes(color=subtype_name))+
geom_node_rect(aes(fill=deseq2, filter=type=="gene"), color="black")+
ggfx::with_outer_glow(geom_node_text(aes(label=converted_name, filter=type!="group"), size=2.5), colour="white", expand=1)+
scale_fill_gradient(low="blue",high="red", name="LFC")+
theme_void()
## Adjusted p-values
ggraph(g, layout="manual", x=x, y=y) +
geom_edge_parallel(width=0.5, arrow = arrow(length = unit(1, 'mm')),
start_cap = square(1, 'cm'),
end_cap = square(1.5, 'cm'), aes(color=subtype_name))+
geom_node_rect(aes(fill=padj, filter=type=="gene"), color="black")+
ggfx::with_outer_glow(geom_node_text(aes(label=converted_name, filter=type!="group"), size=2.5), colour="white", expand=1)+
scale_fill_gradient(name="padj")+
theme_void()
用于进一步自定义可视化ggfx
## Highlighting differentially expressed genes at adjusted p-values < 0.05 with coloring of adjusted p-values on raw KEGG map
gg <- ggraph(g, layout="manual", x=x, y=y)+
geom_node_rect(aes(fill=padj, filter=type=="gene"))+
ggfx::with_outer_glow(geom_node_rect(aes(fill=padj, filter=!is.na(padj) & padj<0.05)),
colour="yellow", expand=2)+
overlay_raw_map("hsa04110", transparent_colors = c("#cccccc","#FFFFFF","#BFBFFF","#BFFFBF"))+
scale_fill_gradient(low="pink",high="steelblue") + theme_void()
gg
使用多个几何添加信息
您可以使用自己喜欢的几何图形及其扩展来添加信息。在此示例中，我们使用?geomtextpath?将 log2 折叠更改添加为轮廓，并使用?Monocraft?自定义字体。ggplot2
g <- g |> mutate(lfc=assign_deseq2(vinf, column="log2FoldChange"))
## Make contour data
df <- g |> data.frame()
df <- df[!is.na(df$lfc),]
cont <- akima::interp2xyz(interp::interp(df$x, df$y, df$lfc)) |>
data.frame() |> `colnames<-`(c("x","y","z"))
##
sysfonts::font_add(family="monocraft",regular="Monocraft.ttf")
gg <- ggraph(g, layout="manual", x=x, y=y)+
geom_edge_parallel(arrow=arrow(length=unit(1,"mm")),
aes(color=subtype_name),
end_cap=circle(7.5,"mm"),
alpha=0.5)+
geomtextpath::geom_textcontour(aes(x=x, y=y, z=z,color=after_stat(level)),
size=3, linetype=2,
linewidth=0.1, data=cont)+
geom_node_rect(aes(fill=padj, filter=type=="gene"))+
ggfx::with_outer_glow(geom_node_rect(aes(fill=padj, filter=!is.na(padj) & padj<0.05)),
colour="yellow", expand=2)+
geom_node_text(aes(label=converted_name), family="monocraft")+
scale_color_gradient2(low=scales::muted("blue"),
high=scales::muted("red"),
name="LFC")+
scale_edge_color_manual(values=viridis::viridis(11), name="Edge type")+
scale_fill_gradient(low="pink",high="steelblue") +
theme_void()
gg
将数值积分到tbl_graph
将数值向量积分到tbl_graph
数值可以反映在节点或边表中，利用 或 函数。输入可以是命名向量，也可以是包含 id 和 value 列的 tibble。node_numericedge_numeric
vec <- 1
names(vec) <- c("hsa:51343")
new_g <- g |> mutate(num=node_numeric(vec))
new_g
#> # A tbl_graph: 134 nodes and 157 edges
#> #
#> # A directed acyclic multigraph with 40 components
#> #
#> # A tibble: 134 × 23
#> name type reaction graphics_name x y width
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 hsa:1029 gene <NA> CDKN2A, ARF,… 532 -218 46
#> 2 hsa:51343 gene <NA> FZR1, CDC20C… 981 -630 46
#> 3 hsa:4171 h… gene <NA> MCM2, BM28, … 553 -681 46
#> 4 hsa:23594 … gene <NA> ORC6, ORC6L.… 494 -681 46
#> 5 hsa:10393 … gene <NA> ANAPC10, APC… 981 -392 46
#> 6 hsa:10393 … gene <NA> ANAPC10, APC… 981 -613 46
#> # ? 128 more rows
#> # ? 16 more variables: height <dbl>, fgcolor <chr>,
#> # bgcolor <chr>, graphics_type <chr>, coords <chr>,
#> # xmin <dbl>, xmax <dbl>, ymin <dbl>, ymax <dbl>,
#> # orig.id <chr>, pathway_id <chr>, deseq2 <dbl>,
#> # padj <dbl>, converted_name <chr>, lfc <dbl>, num <dbl>
#> #
#> # A tibble: 157 × 6
#> from to type subtype_name subtype_value pathway_id
#> <int> <int> <chr> <chr> <chr> <chr>
#> 1 118 39 GErel expression --> hsa04110
#> 2 50 61 PPrel inhibition --| hsa04110
#> 3 50 61 PPrel phosphorylation +p hsa04110
#> # ? 154 more rows?将矩阵积分到tbl_graph
如果要在图形中反映表达式矩阵，则 和 函数可能很有用。通过指定基质和基因 ID，您可以将每个样品的数值分配给 . 分配由边连接的两个节点的总和，忽略组节点（Adnan 等人，2020?年）。edge_matrixnode_matrixtbl_graphedge_matrix
mat <- assay(vst(res))
new_g <- g |> edge_matrix(mat) |> node_matrix(mat)
new_g
#> # A tbl_graph: 134 nodes and 157 edges
#> #
#> # A directed acyclic multigraph with 40 components
#> #
#> # A tibble: 134 × 48
#> name type reaction graphics_name x y width
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 hsa:1029 gene <NA> CDKN2A, ARF,… 532 -218 46
#> 2 hsa:51343 gene <NA> FZR1, CDC20C… 981 -630 46
#> 3 hsa:4171 h… gene <NA> MCM2, BM28, … 553 -681 46
#> 4 hsa:23594 … gene <NA> ORC6, ORC6L.… 494 -681 46
#> 5 hsa:10393 … gene <NA> ANAPC10, APC… 981 -392 46
#> 6 hsa:10393 … gene <NA> ANAPC10, APC… 981 -613 46
#> # ? 128 more rows
#> # ? 41 more variables: height <dbl>, fgcolor <chr>,
#> # bgcolor <chr>, graphics_type <chr>, coords <chr>,
#> # xmin <dbl>, xmax <dbl>, ymin <dbl>, ymax <dbl>,
#> # orig.id <chr>, pathway_id <chr>, deseq2 <dbl>,
#> # padj <dbl>, converted_name <chr>, lfc <dbl>,
#> # SRR14509882 <dbl>, SRR14509883 <dbl>, …
#> #
#> # A tibble: 157 × 34
#> from to type subtype_name subtype_value pathway_id
#> <int> <int> <chr> <chr> <chr> <chr>
#> 1 118 39 GErel expression --> hsa04110
#> 2 50 61 PPrel inhibition --| hsa04110
#> 3 50 61 PPrel phosphorylation +p hsa04110
#> # ? 154 more rows
#> # ? 28 more variables: from_nd <chr>, to_nd <chr>,
#> # SRR14509882 <dbl>, SRR14509883 <dbl>,
#> # SRR14509884 <dbl>, SRR14509885 <dbl>,
#> # SRR14509886 <dbl>, SRR14509887 <dbl>,
#> # SRR14509888 <dbl>, SRR14509889 <dbl>,
#> # SRR14509890 <dbl>, SRR14509891 <dbl>, …边值
相同的效果可以通过 获得，使用命名数值向量作为输入。此函数根据节点值添加边值。以下示例显示了将 LFC 组合到边缘。这与 的行为不同。edge_matrixedge_numeric_sumedge_numeric
## Numeric vector (name is SYMBOL)
vinflfc <- vinf$log2FoldChange |> setNames(row.names(vinf))
g |>
## Use graphics_name to merge
mutate(grname=strsplit(graphics_name, ",") |> vapply("[", 1, FUN.VALUE="a")) |>
activate(edges) |>
mutate(summed = edge_numeric_sum(vinflfc, name="grname")) |>
filter(!is.na(summed)) |>
activate(nodes) |>
mutate(x=NULL, y=NULL, deg=centrality_degree(mode="all")) |>
filter(deg>0) |>
ggraph(layout="nicely")+
geom_edge_parallel(aes(color=summed, width=summed,
linetype=subtype_name),
arrow=arrow(length=unit(1,"mm")),
start_cap=circle(2,"mm"),
end_cap=circle(2,"mm"))+
geom_node_point(aes(fill=I(bgcolor)))+
geom_node_text(aes(label=grname,
filter=type=="gene"),
repel=TRUE, bg.colour="white")+
scale_edge_width(range=c(0.1,2))+
scale_edge_color_gradient(low="blue", high="red", name="Edge")+
theme_void()
可视化多重富集结果
您可以可视化多个富集分析的结果。与将函数与类一起使用类似，可以在函数中使用一个函数。通过向此功能提供对象，如果结果中存在可视化的通路，则通路内的基因信息可以反映在图中。在这个例子中，除了上面提到的尿路上皮细胞的变化外，还比较了肾近端肾小管上皮细胞的变化（Assetta等人，2016）。ggkeggenrichResultappend_cpmutateenrichResult
## These are RDAs storing DEGs
load("degListRPTEC.rda")
load("degURO.rda")
library(org.Hs.eg.db);
library(clusterProfiler);
input_uro <- bitr(uroUp, ## DEGs in urothelial cells
fromType = "SYMBOL",
toType = "ENTREZID",
OrgDb = org.Hs.eg.db)$ENTREZID
input_rptec <- bitr(gls$day3_up_rptec, ## DEGs at 3 days post infection in RPTECs
fromType = "SYMBOL",
toType = "ENTREZID",
OrgDb = org.Hs.eg.db)$ENTREZID
ekuro <- enrichKEGG(gene = input_uro)
ekrptec <- enrichKEGG(gene = input_rptec)
g1 <- pathway("hsa04110") |> mutate(uro=append_cp(ekuro, how="all"),
rptec=append_cp(ekrptec, how="all"),
converted_name=convert_id("hsa"))
ggraph(g1, layout="manual", x=x, y=y) +
geom_edge_parallel(width=0.5, arrow = arrow(length = unit(1, 'mm')),
start_cap = square(1, 'cm'),
end_cap = square(1.5, 'cm'), aes(color=subtype_name))+
geom_node_rect(aes(fill=uro, xmax=x, filter=type=="gene"))+
geom_node_rect(aes(fill=rptec, xmin=x, filter=type=="gene"))+
scale_fill_manual(values=c("steelblue","tomato"), name="urothelial|rptec")+
ggfx::with_outer_glow(geom_node_text(aes(label=converted_name, filter=type!="group"), size=2), colour="white", expand=1)+
theme_void()
我们可以按 组合多个图。rawMappatchwork
library(patchwork)
comb <- rawMap(list(ekuro, ekrptec), fill_color=c("tomato","tomato"), pid="hsa04110") +
rawMap(list(ekuro, ekrptec), fill_color=c("tomato","tomato"),
pid="hsa03460")
comb
下面的示例将类似的反射应用于原始 KEGG 图谱，并突出显示在两种条件下都显示出统计学显着变化的基因，使用黄色外光，由 clusterProfiler 生成的组成，富集结果为 。ggfxdotplotpatchwork
right <- (dotplot(ekuro) + ggtitle("Urothelial")) /
(dotplot(ekrptec) + ggtitle("RPTECs"))
g1 <- pathway("hsa03410") |>
mutate(uro=append_cp(ekuro, how="all"),
rptec=append_cp(ekrptec, how="all"),
converted_name=convert_id("hsa"))
gg <- ggraph(g1, layout="manual", x=x, y=y)+
ggfx::with_outer_glow(
geom_node_rect(aes(filter=uro&rptec),
color="gold", fill="transparent"),
colour="gold", expand=5, sigma=10)+
geom_node_rect(aes(fill=uro, filter=type=="gene"))+
geom_node_rect(aes(fill=rptec, xmin=x, filter=type=="gene")) +
overlay_raw_map("hsa03410", transparent_colors = c("#cccccc","#FFFFFF","#BFBFFF","#BFFFBF"))+
scale_fill_manual(values=c("steelblue","tomato"),
name="urothelial|rptec")+
theme_void()
gg2 <- gg + right + plot_layout(design="
AAAA###
AAAABBB
AAAABBB
AAAA###
"
)
gg2
跨多个通路的多重富集分析结果
除了天然布局外，有时还可以在多个通路中显示有趣的基因，例如DEGs。在这里，我们使用散点图库来可视化跨多个途径的多个富集分析结果。
library(scatterpie)
## Obtain enrichment analysis results
entrezid <- uroUp |>
clusterProfiler::bitr("SYMBOL","ENTREZID",org.Hs.eg.db)
cp <- clusterProfiler::enrichKEGG(entrezid$ENTREZID)
entrezid2 <- gls$day3_up_rptec |>
clusterProfiler::bitr("SYMBOL","ENTREZID",org.Hs.eg.db)
cp2 <- clusterProfiler::enrichKEGG(entrezid2$ENTREZID)
## Filter to interesting pathways
include <- (data.frame(cp) |> row.names())[c(1,3,4)]
pathways <- data.frame(cp)[include,"ID"]
pathways
#> [1] "hsa04110" "hsa03460" "hsa03440"我们获得多个通路数据（该函数返回原生坐标，但我们忽略它们）。
g1 <- multi_pathway_native(pathways, row_num=1)
g2 <- g1 |> mutate(new_name=
ifelse(name=="undefined",
paste0(name,"_",pathway_id,"_",orig.id),
name)) |>
convert(to_contracted, new_name, simplify=FALSE) |>
activate(nodes) |>
mutate(purrr::map_vec(.orig_data,function (x) x[1,] )) |>
mutate(pid1 = purrr::map(.orig_data,function (x) unique(x["pathway_id"]) )) |>
mutate(hsa03440 = purrr:::map_lgl(pid1, function(x) "hsa03440" %in% x$pathway_id) ,
hsa04110 = purrr:::map_lgl(pid1, function(x) "hsa04110" %in% x$pathway_id),
hsa03460 = purrr:::map_lgl(pid1, function(x) "hsa03460" %in% x$pathway_id))
nds <- g2 |> activate(nodes) |> data.frame()
eds <- g2 |> activate(edges) |> data.frame()
rmdup_eds <- eds[!duplicated(eds[,c("from","to","subtype_name")]),]
g2_2 <- tbl_graph(nodes=nds, edges=rmdup_eds)
g2_2 <- g2_2 |> activate(nodes) |>
mutate(
in_pathway_uro=append_cp(cp, pid=include,name="new_name"),
x=NULL, y=NULL,
in_pathway_rptec=append_cp(cp2, pid=include,name = "new_name"),
id=convert_id("hsa",name = "new_name")) |>
morph(to_subgraph, type!="group") |>
mutate(deg=centrality_degree(mode="all")) |>
unmorph() |>
filter(deg>0)在这里，我们还将基于图的聚类结果分配给图，并缩放节点的大小，以便节点可以通过散点图可视化。
V(g2_2)$walktrap <- igraph::walktrap.community(g2_2)$membership
## Scale the node size
sizeMin <- 0.1
sizeMax <- 0.3
rawMin <- min(V(g2_2)$deg)
rawMax <- max(V(g2_2)$deg)
scf <- (sizeMax-sizeMin)/(rawMax-rawMin)
V(g2_2)$size <- scf * V(g2_2)$deg + sizeMin - scf * rawMin
## Make base graph
g3 <- ggraph(g2_2, layout="nicely")+
geom_edge_parallel(alpha=0.9,
arrow=arrow(length=unit(1,"mm")),
aes(color=subtype_name),
start_cap=circle(3,"mm"),
end_cap=circle(8,"mm"))+
scale_edge_color_discrete(name="Edge type")
graphdata <- g3$data最后，我们用于可视化。背景散点表示基因是否在通路中，前景表示基因是否在多个数据集中差异表达。我们突出显示了在两个数据集中通过金色差异表达的基因。geom_scatterpie
g4 <- g3+
ggforce::geom_mark_rect(aes(x=x, y=y, group=walktrap),color="grey")+
geom_scatterpie(aes(x=x, y=y, r=size+0.1),
color="transparent",
legend_name="Pathway",
data=graphdata,
cols=c("hsa04110", "hsa03440","hsa03460")) +
geom_scatterpie(aes(x=x, y=y, r=size),
color="transparent",
data=graphdata, legend_name="enrich",
cols=c("in_pathway_rptec","in_pathway_uro"))+
ggfx::with_outer_glow(geom_scatterpie(aes(x=x, y=y, r=size),
color="transparent",
data=graphdata[graphdata$in_pathway_rptec & graphdata$in_pathway_uro,],
cols=c("in_pathway_rptec","in_pathway_uro")), colour="gold", expand=3)+
geom_node_point(shape=19, size=3, aes(filter=!in_pathway_uro & !in_pathway_rptec & type!="map"))+
geom_node_shadowtext(aes(label=id, y=y-0.5), size=3, family="sans", bg.colour="white", colour="black")+
theme_void()+coord_fixed()
g4
5.4?在KEGG图谱上投影基因调控网络
使用此软件包，可以将推断的网络（例如基因调控网络或由其他软件推断的 KO 网络）投射到 KEGG 图谱上。以下是使用 将 CBNplot 推断的通路内的 KO 网络子集投影到相应通路的参考图上的示例。当然，也可以投影使用其他方法创建的网络。MicrobiomeProfiler
library(dplyr)
library(igraph)
library(tidygraph)
library(CBNplot)
library(ggkegg)
library(MicrobiomeProfiler)
data(Rat_data)
ko.res <- enrichKO(Rat_data)
exp.dat <- matrix(abs(rnorm(910)), 91, 10) %>% magrittr::set_rownames(value=Rat_data) %>% magrittr::set_colnames(value=paste0('S', seq_len(ncol(.))))
returnnet <- bngeneplot(ko.res, exp=exp.dat, pathNum=1, orgDb=NULL,returnNet = TRUE)
pg <- pathway("ko00650")
joined <- combine_with_bnlearn(pg, returnnet$str, returnnet$av)绘制生成的地图。在此示例中，估计的强度首先用彩色边缘显示，然后参考图的边缘在其顶部以黑色绘制。此外，两个图形中包含的边缘都以黄色突出显示。CBNplot
## Summarize duplicate edges including `strength` attribute
number <- joined |> activate(edges) |> data.frame() |> group_by(from,to) |>
summarise(n=n(), incstr=sum(!is.na(strength)))
## Annotate them
joined <- joined |> activate(edges) |> full_join(number) |> mutate(both=n>1&incstr>0)
joined |>
activate(nodes) |>
filter(!is.na(type)) |>
mutate(convertKO=convert_id("ko")) |>
activate(edges) |>
ggraph(x=x, y=y) +
geom_edge_link0(width=0.5,aes(filter=!is.na(strength),
color=strength), linetype=1)+
ggfx::with_outer_glow(
geom_edge_link0(width=0.5,aes(filter=!is.na(strength) & both,
color=strength), linetype=1),
colour="yellow", sigma=1, expand=1)+
geom_edge_link0(width=0.1, aes(filter=is.na(strength)))+
scale_edge_color_gradient(low="blue",high="red")+
geom_node_rect(color="black", aes(fill=type))+
geom_node_text(aes(label=convertKO), size=2)+
geom_node_text(aes(label=ifelse(grepl(":", graphics_name), strsplit(graphics_name, ":") |>
sapply("[",2) |> stringr::str_wrap(22), stringr::str_wrap(graphics_name, 22)),
filter=!is.na(type) & type=="map"), family="serif",
size=2, na.rm=TRUE)+
theme_void()
5.4.1?投影到原始 KEGG 地图上
您可以直接将推断网络投影到原始 PATHWAY 地图上，这样可以直接比较您自己的数据集中精选数据库和推断网络的知识。
raws <- joined |>
ggraph(x=x, y=y) +
geom_edge_link(width=0.5,aes(filter=!is.na(strength),
color=strength),
linetype=1,
arrow=arrow(length=unit(1,"mm"),type="closed"),
end_cap=circle(5,"mm"))+
scale_edge_color_gradient2()+
overlay_raw_map(transparent_colors = c("#ffffff"))+
theme_void()
raws
5.5?分析单细胞转录组学中的簇标记基因
该软件包也可应用于单细胞分析。例如，考虑将簇之间的标记基因映射到 KEGG 通路上，并将它们与降维图一起绘制。在这里，我们使用包。我们进行基本面分析。Seurat
library(Seurat)
library(dplyr)
# dir = "../filtered_gene_bc_matrices/hg19"
# pbmc.data <- Read10X(data.dir = dir)
# pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k",
# min.cells=3, min.features=200)
# pbmc <- NormalizeData(pbmc)
# pbmc <- FindVariableFeatures(pbmc, selection.method = "vst")
# pbmc <- ScaleData(pbmc, features = row.names(pbmc))
# pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
# pbmc <- FindNeighbors(pbmc, dims = 1:10, verbose = FALSE)
# pbmc <- FindClusters(pbmc, resolution = 0.5, verbose = FALSE)
# markers <- FindAllMarkers(pbmc)
# save(pbmc, markers, file="../sc_data.rda")
## To reduce file size, pre-calculated RDA will be loaded
load("../sc_data.rda")随后，我们绘制了PCA降维的结果。
其中，在本研究中，我们对簇 1 和 5 的标记基因进行了富集分析。
library(clusterProfiler)
## Directly access slots in Seurat
pcas <- data.frame(
pbmc@reductions$pca@cell.embeddings[,1],
pbmc@reductions$pca@cell.embeddings[,2],
pbmc@active.ident,
pbmc@meta.data$seurat_clusters) |>
`colnames<-`(c("PC_1","PC_2","Cell","group"))
aa <- (pcas %>% group_by(Cell) %>%
mutate(meanX=mean(PC_1), meanY=mean(PC_2))) |>
select(Cell, meanX, meanY)
label <- aa[!duplicated(aa),]
dd <- ggplot(pcas)+
geom_point(aes(x=PC_1, y=PC_2, color=Cell))+
shadowtext::geom_shadowtext(x=label$meanX,y=label$meanY,label=label$Cell, data=label,
bg.colour="white", colour="black")+
theme_minimal()+
theme(legend.position="none")
marker_1 <- clusterProfiler::bitr((markers |> filter(cluster=="1" & p_val_adj < 1e-50) |>
dplyr::select(gene))$gene,fromType="SYMBOL",toType="ENTREZID",OrgDb = org.Hs.eg.db)$ENTREZID
marker_5 <- clusterProfiler::bitr((markers |> filter(cluster=="5" & p_val_adj < 1e-50) |>
dplyr::select(gene))$gene,fromType="SYMBOL",toType="ENTREZID",OrgDb = org.Hs.eg.db)$ENTREZID
mk1_enrich <- enrichKEGG(marker_1)
mk5_enrich <- enrichKEGG(marker_5)从中获取颜色信息，并使用 获取通路。在这里，我们选择了 ，节点根据降维图中的颜色着色，两个聚类中的标记都按指定的颜色 （） 着色。这促进了通路信息（如KEGG）与单细胞分析数据之间的联系，从而能够创建直观且易于理解的视觉表示。ggplot2ggkeggOsteoclast differentiation (hsa04380)ggfxtomato
## Make color map
built <- ggplot_build(dd)$data[[1]]
cols <- built$colour
names(cols) <- as.character(as.numeric(built$group)-1)
gr_cols <- cols[!duplicated(cols)]
g <- pathway("hsa04380") |> mutate(marker_1=append_cp(mk1_enrich),
marker_5=append_cp(mk5_enrich))
gg <- ggraph(g, layout="manual", x=x, y=y)+
geom_node_rect(aes(filter=marker_1&marker_5), fill="tomato")+ ## Marker 1 & 5
geom_node_rect(aes(filter=marker_1&!marker_5), fill=gr_cols["1"])+ ## Marker 1
geom_node_rect(aes(filter=marker_5&!marker_1), fill=gr_cols["5"])+ ## Marker 5
overlay_raw_map("hsa04380", transparent_colors = c("#cccccc","#FFFFFF","#BFBFFF","#BFFFBF"))+
theme_void()
gg+dd+plot_layout(widths=c(0.6,0.4))
5.5.1?组成多个通路的示例
我们可以在多种途径中检查标记基因，以更好地了解标记基因的作用。
library(clusterProfiler)
library(org.Hs.eg.db)
subset_lab <- label[label$Cell %in% c("1","4","5","6"),]
dd <- ggplot(pcas) +
ggfx::with_outer_glow(geom_node_point(size=1,
aes(x=PC_1, y=PC_2, filter=group=="1", color=group)),
colour="tomato", expand=3)+
ggfx::with_outer_glow(geom_node_point(size=1,
aes(x=PC_1, y=PC_2, filter=group=="5", color=group)),
colour="tomato", expand=3)+
ggfx::with_outer_glow(geom_node_point(size=1,
aes(x=PC_1, y=PC_2, filter=group=="4", color=group)),
colour="gold", expand=3)+
ggfx::with_outer_glow(geom_node_point(size=1,
aes(x=PC_1, y=PC_2, filter=group=="6", color=group)),
colour="gold", expand=3)+
shadowtext::geom_shadowtext(x=subset_lab$meanX,
y=subset_lab$meanY, label=subset_lab$Cell,
data=subset_lab,
bg.colour="white", colour="black")+
theme_minimal()
marker_1 <- clusterProfiler::bitr((markers |> filter(cluster=="1" & p_val_adj < 1e-50) |>
dplyr::select(gene))$gene,fromType="SYMBOL",toType="ENTREZID",OrgDb = org.Hs.eg.db)$ENTREZID
marker_5 <- clusterProfiler::bitr((markers |> filter(cluster=="5" & p_val_adj < 1e-50) |>
dplyr::select(gene))$gene,fromType="SYMBOL",toType="ENTREZID",OrgDb = org.Hs.eg.db)$ENTREZID
marker_6 <- clusterProfiler::bitr((markers |> filter(cluster=="6" & p_val_adj < 1e-50) |>
dplyr::select(gene))$gene,fromType="SYMBOL",toType="ENTREZID",OrgDb = org.Hs.eg.db)$ENTREZID
marker_4 <- clusterProfiler::bitr((markers |> filter(cluster=="4" & p_val_adj < 1e-50) |>
dplyr::select(gene))$gene,fromType="SYMBOL",toType="ENTREZID",OrgDb = org.Hs.eg.db)$ENTREZID
mk1_enrich <- enrichKEGG(marker_1)
mk5_enrich <- enrichKEGG(marker_5)
mk6_enrich <- enrichKEGG(marker_6)
mk4_enrich <- enrichKEGG(marker_4)
g1 <- pathway("hsa04612") |> mutate(marker_4=append_cp(mk4_enrich),
marker_6=append_cp(mk6_enrich),
gene_name=convert_id("hsa"))
gg1 <- ggraph(g1, layout="manual", x=x, y=y)+
overlay_raw_map("hsa04612", transparent_colors = c("#FFFFFF", "#BFBFFF", "#BFFFBF"))+
ggfx::with_outer_glow(
geom_node_rect(aes(filter=marker_4&marker_6), fill="white"),
colour="gold")+
ggfx::with_outer_glow(
geom_node_rect(aes(filter=marker_4&!marker_6), fill="white"),
colour=gr_cols["4"])+
ggfx::with_outer_glow(
geom_node_rect(aes(filter=marker_6&!marker_4), fill="white"),
colour=gr_cols["6"], expand=3)+
overlay_raw_map("hsa04612", transparent_colors = c("#B3B3B3", "#FFFFFF", "#BFBFFF", "#BFFFBF"))+
theme_void()
g2 <- pathway("hsa04380") |> mutate(marker_1=append_cp(mk1_enrich),
marker_5=append_cp(mk5_enrich))
gg2 <- ggraph(g2, layout="manual", x=x, y=y)+
ggfx::with_outer_glow(
geom_node_rect(aes(filter=marker_1&marker_5),
fill="white"), ## Marker 1 & 5
colour="tomato")+
ggfx::with_outer_glow(
geom_node_rect(aes(filter=marker_1&!marker_5),
fill="white"), ## Marker 1
colour=gr_cols["1"])+
ggfx::with_outer_glow(
geom_node_rect(aes(filter=marker_5&!marker_1),
fill="white"), ## Marker 5
colour=gr_cols["5"])+
overlay_raw_map("hsa04380",
transparent_colors = c("#cccccc","#FFFFFF","#BFBFFF","#BFFFBF"))+
theme_void()
left <- (gg2 + ggtitle("Marker 1 and 5")) /
(gg1 + ggtitle("Marker 4 and 6"))
final <- left + dd + plot_layout(design="
AAAAA###
AAAAACCC
BBBBBCCC
BBBBB###
")
final
5.5.2?原始地图上数值的条形图
对于它们在多个聚类中丰富的节点，我们可以绘制数值的条形图。引用的代码由 inscaven?提供。
## Assign lfc to graph
mark_4 <- clusterProfiler::bitr((markers |> filter(cluster=="4" & p_val_adj < 1e-50) |>
dplyr::select(gene))$gene,fromType="SYMBOL",toType="ENTREZID",OrgDb = org.Hs.eg.db)
mark_6 <- clusterProfiler::bitr((markers |> filter(cluster=="6" & p_val_adj < 1e-50) |>
dplyr::select(gene))$gene,fromType="SYMBOL",toType="ENTREZID",OrgDb = org.Hs.eg.db)
mark_4$lfc <- markers[markers$cluster=="4" & markers$gene %in% mark_4$SYMBOL,]$avg_log2FC
mark_4$hsa <- paste0("hsa:",mark_4$ENTREZID)
mark_6$lfc <- markers[markers$cluster=="6" & markers$gene %in% mark_4$SYMBOL,]$avg_log2FC
mark_6$hsa <- paste0("hsa:",mark_6$ENTREZID)
mk4lfc <- mark_4$lfc
names(mk4lfc) <- mark_4$hsa
mk6lfc <- mark_6$lfc
names(mk6lfc) <- mark_6$hsa
g1 <- g1 |> mutate(mk4lfc=node_numeric(mk4lfc),
mk6lfc=node_numeric(mk6lfc))
## Make data frame containing necessary data from node
subset_df <- g1 |> activate(nodes) |> data.frame() |>
dplyr::filter(marker_4 & marker_6) |>
dplyr::select(orig.id, mk4lfc, mk6lfc, x, y, xmin, xmax, ymin, ymax) |>
tidyr::pivot_longer(cols=c("mk4lfc","mk6lfc"))
## Actually we dont need position list
pos_list <- list()
annot_list <- list()
for (i in subset_df$orig.id |> unique()) {
tmp <- subset_df[subset_df$orig.id==i,]
ymin <- tmp$ymin |> unique()
ymax <- tmp$ymax |> unique()
xmin <- tmp$xmin |> unique()
xmax <- tmp$xmax |> unique()
pos_list[[as.character(i)]] <- c(xmin, xmax,
ymin, ymax)
barp <- tmp |>
ggplot(aes(x=name, y=value, fill=name))+
geom_col(width=1)+
scale_fill_manual(values=c(gr_cols["4"] |> as.character(),
gr_cols["6"] |> as.character()))+
labs(x = NULL, y = NULL) +
coord_cartesian(expand = FALSE) +
theme(
legend.position = "none",
panel.background = element_rect(fill = "transparent", colour = NA),
line = element_blank(),
text = element_blank()
)
gbar <- ggplotGrob(barp)
panel_coords <- gbar$layout[gbar$layout$name == "panel", ]
gbar_mod <- gbar[panel_coords$t:panel_coords$b, panel_coords$l:panel_coords$r]
annot_list[[as.character(i)]] <- annotation_custom(gbar_mod,
xmin=xmin, xmax=xmax,
ymin=ymin, ymax=ymax)
}
## Make ggraph, annotate barplot, and overlay raw map.
graph_tmp <- ggraph(g1, layout="manual", x=x, y=y)+
geom_node_rect(aes(filter=marker_4&marker_6),
fill="gold")+
geom_node_rect(aes(filter=marker_4&!marker_6),
fill=gr_cols["4"])+
geom_node_rect(aes(filter=marker_6&!marker_4),
fill=gr_cols["6"])+
theme_void()
final_bar <- Reduce("+", annot_list, graph_tmp)+
overlay_raw_map("hsa04612",
transparent_colors = c("#FFFFFF",
"#BFBFFF",
"#BFFFBF"))
final_bar
5.5.3?所有聚类的条形图
通过迭代上述代码，我们可以将所有聚类的定量数据绘制在图上。虽然最好使用 ggplot2 映射来生成图例，但这里我们从降维图中获取图例。
g1 <- pathway("hsa04612")
for (cluster_num in seq_len(9)) {
cluster_num <- as.character(cluster_num - 1)
mark <- clusterProfiler::bitr((markers |> filter(cluster==cluster_num & p_val_adj < 1e-50) |>
dplyr::select(gene))$gene,fromType="SYMBOL",toType="ENTREZID",OrgDb = org.Hs.eg.db)
mark$lfc <- markers[markers$cluster==cluster_num & markers$gene %in% mark$SYMBOL,]$avg_log2FC
mark$hsa <- paste0("hsa:",mark$ENTREZID)
coln <- paste0("marker",cluster_num,"lfc")
g1 <- g1 |> mutate(!!coln := node_numeric(mark$lfc |> setNames(mark$hsa)))
}
subset_df <- g1 |> activate(nodes) |> data.frame() |>
dplyr::select(orig.id, paste0("marker",seq_len(9)-1,"lfc"), x, y, xmin, xmax, ymin, ymax) |>
tidyr::pivot_longer(cols=paste0("marker",seq_len(9)-1,"lfc"))
pos_list <- list()
annot_list <- list()
all_gr_cols <- gr_cols
names(all_gr_cols) <- paste0("marker",names(all_gr_cols),"lfc")
for (i in subset_df$orig.id |> unique()) {
tmp <- subset_df[subset_df$orig.id==i,]
ymin <- tmp$ymin |> unique()
ymax <- tmp$ymax |> unique()
xmin <- tmp$xmin |> unique()
xmax <- tmp$xmax |> unique()
pos_list[[as.character(i)]] <- c(xmin, xmax,
ymin, ymax)
if ((tmp |> filter(!is.na(value)) |> dim())[1]!=0) {
barp <- tmp |> filter(!is.na(value)) |>
ggplot(aes(x=name, y=value, fill=name))+
geom_col(width=1)+
scale_fill_manual(values=all_gr_cols)+
## We add horizontal line to show the direction of bar
geom_hline(yintercept=0, linewidth=1, colour="grey")+
labs(x = NULL, y = NULL) +
coord_cartesian(expand = FALSE) +
theme(
legend.position = "none",
panel.background = element_rect(fill = "transparent", colour = NA),
text = element_blank()
)
gbar <- ggplotGrob(barp)
panel_coords <- gbar$layout[gbar$layout$name == "panel", ]
gbar_mod <- gbar[panel_coords$t:panel_coords$b, panel_coords$l:panel_coords$r]
annot_list[[as.character(i)]] <- annotation_custom(gbar_mod,
xmin=xmin, xmax=xmax,
ymin=ymin, ymax=ymax)
}
}获取图例并进行修改。
## Take scplot legend, make it rectangle
## Make pseudo plot
dd2 <- ggplot(pcas) +
geom_node_point(aes(x=PC_1, y=PC_2, color=group)) +
guides(color = guide_legend(override.aes = list(shape=15, size=5)))+
theme_minimal()
grobs <- ggplot_gtable(ggplot_build(dd2))
num <- which(sapply(grobs$grobs, function(x) x$name) == "guide-box")
legendGrob <- grobs$grobs[[num]]
## Show it
ggplotify::as.ggplot(legendGrob)
## Make dummy legend by `fill`
graph_tmp <- ggraph(g1, layout="manual", x=x, y=y)+
geom_node_rect(aes(fill="transparent"))+
scale_fill_manual(values="transparent" |> setNames("transparent"))+
theme_void()
## Overlaid the raw map
overlaid <- Reduce("+", annot_list, graph_tmp)+
overlay_raw_map("hsa04612",
transparent_colors = c("#FFFFFF",
"#BFBFFF",
"#BFFFBF"))
## Replace the guides
overlaidGtable <- ggplot_gtable(ggplot_build(overlaid))
num2 <- which(sapply(overlaidGtable$grobs, function(x) x$name) == "guide-box")
overlaidGtable$grobs[[num2]] <- legendGrob
ggplotify::as.ggplot(overlaidGtable)
5.6?自定义全局地图可视化
使用的一个优点是利用 和 的强大功能有效地可视化全球地图。在这里，我展示了一个可视化从全球地图中的一些微生物组实验中获得的 log2 倍数变化值的示例。首先，我们加载必要的数据，这些数据可以从调查 KO 的数据集中获得，这些数据是从管道中获得的，例如 .ggkeggggplot2ggraphHUMAnN3
load("../lfcs.rda") ## Storing named vector of KOs storing LFCs and significant KOs
load("../func_cat.rda") ## Functional categories for hex values in ko01100
lfcs |> head()
#> ko:K00013 ko:K00018 ko:K00031 ko:K00042 ko:K00065
#> -0.2955686 -0.4803597 -0.3052872 0.9327130 1.0954976
#> ko:K00087
#> 0.8713860
signame |> head()
#> [1] "ko:K00013" "ko:K00018" "ko:K00031" "ko:K00042"
#> [5] "ko:K00065" "ko:K00087"
func_cat |> head()
#> # A tibble: 6 × 3
#> hex class top
#> <chr> <chr> <chr>
#> 1 #B3B3E6 Metabolism; Carbohydrate metabolism Amin…
#> 2 #F06292 Metabolism; Biosynthesis of other secondary… Bios…
#> 3 #FFB3CC Metabolism; Metabolism of cofactors and vit… Bios…
#> 4 #FF8080 Metabolism; Nucleotide metabolism Puri…
#> 5 #6C63F6 Metabolism; Carbohydrate metabolism Glyc…
#> 6 #FFCC66 Metabolism; Amino acid metabolism Bios…
## Named vector for Assigning functional category
hex <- func_cat$hex |> setNames(func_cat$hex)
class <- func_cat$class |> setNames(func_cat$hex)
hex |> head()
#> #B3B3E6 #F06292 #FFB3CC #FF8080 #6C63F6 #FFCC66
#> "#B3B3E6" "#F06292" "#FFB3CC" "#FF8080" "#6C63F6" "#FFCC66"
class |> head()
#> #B3B3E6
#> "Metabolism; Carbohydrate metabolism"
#> #F06292
#> "Metabolism; Biosynthesis of other secondary metabolites"
#> #FFB3CC
#> "Metabolism; Metabolism of cofactors and vitamins"
#> #FF8080
#> "Metabolism; Nucleotide metabolism"
#> #6C63F6
#> "Metabolism; Carbohydrate metabolism"
#> #FFCC66
#> "Metabolism; Amino acid metabolism"?预处理
我们得到了 ko01100，并处理了图形。首先，我们附加与化合物间关系相对应的边。尽管大多数反应是可逆的，并且默认情况下会在 中添加两条边，但我们在此处指定用于可视化。此外，转换化合物 ID 和 KO ID 并将属性附加到图形中。tbl_graphprocess_reactionsingle_edge=TRUE
g <- ggkegg::pathway("ko01100")
g <- g |> process_reaction(single_edge=TRUE)
g <- g |> mutate(x=NULL, y=NULL)
g <- g |> activate(nodes) |> mutate(compn=convert_id("compound",
first_arg_comma = FALSE))
g <- g |> activate(edges) |> mutate(kon=convert_id("ko",edge=TRUE))接下来，我们将 KO 和度数等值附加到图表中。此外，在这里，我们将其他属性（例如哪些物种具有酶）附加到图表中。此类信息可以从 的分层输出中获得。HUMAnN3
g2 <- g |> activate(edges) |>
mutate(kolfc=edge_numeric(lfcs), ## Pre-computed LFCs
siglgl=.data$name %in% signame) |> ## Whether the KO is significant
activate(nodes) |>
filter(type=="compound") |> ## Subset to compound nodes and
mutate(Degree=centrality_degree(mode="all")) |> ## Calculate degree
activate(nodes) |>
filter(Degree>2) |> ## Filter based on degree
activate(edges) |>
mutate(Species=ifelse(kon=="lyxK", "Escherichia coli", "Others"))接下来，我们根据 ko01100 检查这些 KO 的总体类别，KO 数量最多的类别是碳水化合物代谢。
class_table <- (g |> activate(edges) |>
mutate(siglgl=name %in% signame) |>
filter(siglgl) |>
data.frame())$fgcolor |>
table() |> sort(decreasing=TRUE)
names(class_table) <- class[names(class_table)]
class_table
#> Metabolism; Carbohydrate metabolism
#> 20
#> Metabolism; Glycan biosynthesis and metabolism
#> 16
#> Metabolism; Metabolism of cofactors and vitamins
#> 11
#> Metabolism; Amino acid metabolism
#> 8
#> Metabolism; Nucleotide metabolism
#> 7
#> Metabolism; Metabolism of terpenoids and polyketides
#> 3
#> Metabolism; Energy metabolism
#> 3
#> Metabolism; Xenobiotics biodegradation and metabolism
#> 3
#> Metabolism; Carbohydrate metabolism
#> 2
#> Metabolism; Lipid metabolism
#> 1
#> Metabolism; Biosynthesis of other secondary metabolites
#> 1
#> Metabolism; Metabolism of other amino acids
#> 1绘图
我们首先使用 和 计算度的默认值可视化整个全球地图。ko01100
ggraph(g2, layout="fr")+
geom_edge_link0(aes(color=I(fgcolor)), width=0.1)+
geom_node_point(aes(fill=I(fgcolor), size=Degree), color="black", shape=21)+
theme_graph()
我们可以将各种几何形状应用于KEGG PATHWAY中的组件，以实现有效的可视化。在此示例中，我们突出显示了由其 LFC 着色的有效边 （KO），点大小对应于网络中的度数，并显示了有效 KO 名称的边缘标签。KO名称按属性着色。这一次，我们将其设置为 和 。ggfxSpeciesEscherichia coliOthers
ggraph(g2, layout="fr") +
geom_edge_diagonal(color="grey50", width=0.1)+ ## Base edge
ggfx::with_outer_glow(
geom_edge_diagonal(aes(color=kolfc,filter=siglgl),
angle_calc = "along",
label_size=2.5),
colour="gold", expand=3
)+ ## Highlight significant edges
scale_edge_color_gradient2(midpoint = 0, mid = "white",
low=scales::muted("blue"),
high=scales::muted("red"),
name="LFC")+ ## Set gradient color
geom_node_point(aes(fill=bgcolor,size=Degree),
shape=21,
color="black")+ ## Node size set to degree
scale_size(range=c(1,4))+
geom_edge_label_diagonal(aes(
label=kon,
label_colour=Species,
filter=siglgl
),
angle_calc = "along",
label_size=2.5)+ ## Showing edge label, label color is Species attribute
scale_label_colour_manual(values=c("tomato","black"),
name="Species")+ ## Scale color for edge label
scale_fill_manual(values=hex,labels=class,name="Class")+ ## Show legend based on HEX
theme_graph()+
guides(fill = guide_legend(override.aes = list(size=5))) ## Change legend point size
如果我们想调查特定的类，则按图中的十六进制值进行子集。
## Subset and do the same thing
g2 |>
morph(to_subgraph, siglgl) |>
activate(nodes) |>
mutate(tmp=centrality_degree(mode="all")) |>
filter(tmp>0) |>
mutate(subname=compn) |>
unmorph() |>
activate(nodes) |>
filter(bgcolor=="#B3B3E6") |>
mutate(Degree=centrality_degree(mode="all")) |> ## Calculate degree
filter(Degree>0) |>
ggraph(layout="fr") +
geom_edge_diagonal(color="grey50", width=0.1)+ ## Base edge
ggfx::with_outer_glow(
geom_edge_diagonal(aes(color=kolfc,filter=siglgl),
angle_calc = "along",
label_size=2.5),
colour="gold", expand=3
)+
scale_edge_color_gradient2(midpoint = 0, mid = "white",
low=scales::muted("blue"),
high=scales::muted("red"),
name="LFC")+
geom_node_point(aes(fill=bgcolor,size=Degree),
shape=21,
color="black")+
scale_size(range=c(1,4))+
geom_edge_label_diagonal(aes(
label=kon,
label_colour=Species,
filter=siglgl
),
angle_calc = "along",
label_size=2.5)+ ## Showing edge label
scale_label_colour_manual(values=c("tomato","black"),
name="Species")+ ## Scale color for edge label
geom_node_text(aes(label=stringr::str_wrap(subname,10,whitespace_only = FALSE)),
repel=TRUE, bg.colour="white", size=2)+
scale_fill_manual(values=hex,labels=class,name="Class")+
theme_graph()+
guides(fill = guide_legend(override.aes = list(size=5)))
文章来源:https://blog.csdn.net/zrc_xiaoguo/article/details/135098895
本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。 如若内容造成侵权/违法违规/事实不符，请联系我的编程经验分享网邮箱：chenni525@qq.com进行投诉反馈，一经查实，立即删除！
本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。 如若内容造成侵权/违法违规/事实不符，请联系我的编程经验分享网邮箱：chenni525@qq.com进行投诉反馈，一经查实，立即删除！