小编dan*_*dan的帖子

我想使用ggplot绘制直方图(或使用的步骤图stat_bin)并使用它覆盖几个点geom_point.

这是一个base实现:

library(plotrix)
set.seed(10)
df <- data.frame(id=LETTERS,val=rnorm(length(LETTERS)))
selected.ids <- sample(LETTERS,3,replace=F)
h <- hist(df$val,plot=F,breaks=10)
cols <- sapply(rainbow(length(selected.ids)),function(x) color.id(x)[1])
selected.df <- data.frame(id=selected.ids,col=cols,stringsAsFactors=F)
selected.df$x <- df$val[which(df$id %in% selected.ids)]
selected.df <- selected.df[order(selected.df$x),]
selected.df$y <- h$counts[findInterval(selected.df$x,h$breaks)]
selected.df$col <- factor(selected.df$col,levels=cols)
plot(h)
segments(x0=selected.df$x,x1=selected.df$x,y0=selected.df$y,y1=selected.df$y,cex=18,lwd=8,col=selected.df$col)

这使:

但是,当我尝试ggplot:

ggplot(df,aes(x=val))+geom_histogram(bins=10,colour="black",alpha=0,fill="#FF6666")+geom_point(data=selected.df,aes(x=x,y=y,colour=factor(col)),size=2)+scale_fill_manual(values=levels(selected.df$col),labels=selected.df$id,name="id")+scale_colour_manual(values=levels(selected.df$col),labels=selected.df$id,name="id")

点和直方图未对齐:

理想情况下,我想使用步骤图绘制它:

ggplot(df,aes(x=val))+stat_bin(geom="step",bins=10)+geom_point(data=selected.df,aes(x=x,y=y,colour=factor(col)),size=2)+scale_fill_manual(values=levels(selected.df$col),labels=selected.df$id,name="id")+scale_colour_manual(values=levels(selected.df$col),labels=selected.df$id,name="id")

这看起来非常像 geom_histogram

而且我也想让线的末端触及y = 0线.

所以我使用stat_bin在步骤图中得到正确的结果？

有没有办法在dot布局中绘制DAG (a-la Rgraphviz)ggnet？

我的例子是基因本体图,取自topGO的vignette:

  library(topGO)
  library(ALL)
  data(ALL)
  data(geneList)
  affyLib <- paste(annotation(ALL),"db",sep= ".")
  library(package=affyLib,character.only=TRUE)
  topgo.obj <- new("topGOdata",description="Simple session",ontology="BP",allGenes=geneList,geneSel=topDiffGenes,nodeSize=10,annot=annFUN.db,affyLib=affyLib)
  res.fisher <- runTest(topgo.obj,algorithm="classic",statistic="fisher")
  res.df <- GenTable(topgo.obj,classicFisher=res.fisher,orderBy="classicFisher",topNodes=length(score(res.fisher)))

为方便起见,我设置:

res.df$p.value <- as.numeric(res.df$classicFisher)

我保留了topGO graphNELp值低于0.05 的唯一节点及其祖先:

topgo.graph <- graph(topgo.obj)
sig.cutoff <- 0.05
sig.node.names <- dplyr::filter(res.df,p.value < sig.cutoff)$GO.ID
topgo.graph <- reverseArch(inducedGraph(topgo.graph,sig.node.names))

要使用ggnet我将graphNEL图形转换为igraph:

  library(ggnet)
  library(network)
  library(sna)
  library(scales)
  library(igraph)
  library(intergraph)

  topgo.igraph <- graph_from_graphnel(topgo.graph,name=TRUE,weight=TRUE,unlist.attrs=TRUE)
  topgo.network <- asNetwork(topgo.igraph,amap=attrmap())
  ggnet2(net=topgo.network,size=10,arrow.size=12,arrow.gap=0.025)

这给了我这个:

使用Rgraphviz的dot …

我正在尝试生成一个plotly heatmap，我希望颜色由离散比例指定。

这就是我的意思：

生成具有 2 个集群的数据并按层次对它们进行聚类：

require(permute)
set.seed(1)
mat <- rbind(cbind(matrix(rnorm(2500,2,1),nrow=25,ncol=500),matrix(rnorm(2500,-2,1),nrow=25,ncol=500)),
             cbind(matrix(rnorm(2500,-2,1),nrow=25,ncol=500),matrix(rnorm(2500,2,1),nrow=25,ncol=500)))
rownames(mat) <- paste("g",1:50,sep=".")
colnames(mat) <- paste("s",1:1000,sep=".")
hc.col <- hclust(dist(t(mat)))
dd.col <- as.dendrogram(hc.col)
col.order <- order.dendrogram(dd.col)
hc.row <- hclust(dist(mat))
dd.row <- as.dendrogram(hc.row)
row.order <- order.dendrogram(dd.row)
mat <- mat[row.order,col.order]

将值放入mat间隔并为每个间隔设置颜色：

require(RColorBrewer)
mat.intervals <- cut(mat,breaks=6)
interval.mat <- matrix(mat.intervals,nrow=50,ncol=1000,dimnames=list(rownames(mat),colnames(mat)))
interval.cols <- brewer.pal(6,"Set2")
names(interval.cols) <- levels(mat.intervals)

使用ggplot2我以这种heatmap方式绘制（也legend指定了离散颜色和各自的范围）：

require(reshape2)
interval.df <- reshape2::melt(interval.mat,varnames=c("gene","sample"),value.name="expr")
require(ggplot2)
ggplot(interval.df,aes(x=sample,y=gene,fill=expr))+
  geom_tile(color=NA)+theme_bw()+
  theme(strip.text.x=element_text(angle=90,vjust=1,hjust=0.5,size=6),panel.spacing=unit(0.025,"cm"),legend.key=element_blank(),plot.margin=unit(c(1,1,1,1),"cm"),legend.key.size=unit(0.25,"cm"),panel.border=element_blank(),strip.background=element_blank(),axis.ticks.y=element_line(size=0.25))+
  scale_color_manual(drop=FALSE,values=interval.cols,labels=names(interval.cols),name="expr")+
  scale_fill_manual(drop=FALSE,values=interval.cols,labels=names(interval.cols),name="expr")

这使：

这是我尝试用以下方法生成它plotly：

plot_ly(z=mat,x=colnames(mat),y=rownames(mat),type="heatmap",colors=interval.cols)

这使： …

我希望能够将R plotly heatmap.

我的意思是：我有一个分层聚类的基因表达数据集：

require(permute)
set.seed(1)
mat <- rbind(cbind(matrix(rnorm(2500,2,1),nrow=25,ncol=500),matrix(rnorm(2500,-2,1),nrow=25,ncol=500)),
             cbind(matrix(rnorm(2500,-2,1),nrow=25,ncol=500),matrix(rnorm(2500,2,1),nrow=25,ncol=500)))
rownames(mat) <- paste("g",1:50,sep=".")
colnames(mat) <- paste("s",1:1000,sep=".")
hc.col <- hclust(dist(t(mat)))
dd.col <- as.dendrogram(hc.col)
col.order <- order.dendrogram(dd.col)
hc.row <- hclust(dist(mat))
dd.row <- as.dendrogram(hc.row)
row.order <- order.dendrogram(dd.row)
mat <- mat[row.order,col.order]

然后，我将其离散化为特定的表达范围，因为这恰好有助于解决我的案例的颜色问题。我还创建了其他结构来帮助我按照colorbar我想要的方式绘制：

require(RColorBrewer)
mat.intervals <- cut(mat,breaks=6)
interval.mat <- matrix(mat.intervals,nrow=50,ncol=1000,dimnames=list(rownames(mat),colnames(mat)))
interval.cols <- brewer.pal(6,"Set2")
names(interval.cols) <- levels(mat.intervals)
require(reshape2)
interval.df <- reshape2::melt(interval.mat,varnames=c("gene","sample"),value.name="expr")
interval.cols2 <- rep(interval.cols, each=ncol(mat))
color.df <- data.frame(range=c(0:(2*length(interval.cols)-1)),colors=c(0:(2*length(interval.cols)-1)))
color.df <- setNames(data.frame(color.df$range,color.df$colors),NULL)
for (i in 1:(2*length(interval.cols))) {
  color.df[[2]][[i]] <- interval.cols[[(i + 1) / …

我有一个matrix（来自几个条件的基因表达）：

set.seed(1)
mat <- matrix(rnorm(50*10),nrow=50,ncol=10,dimnames=list(paste("C",1:50,sep="."),paste("G",1:10,sep=".")))

我想将其绘制为heatmap使用plotlyin R。

require(plotly)
heatmap.plotly <- plot_ly(x=colnames(mat),y=rownames(mat),z=mat,type="heatmap",colors=colorRamp(c("darkblue","white","darkred")),colorbar=list(title="Score",len=0.4)) %>%
  layout(yaxis=list(title="Condition"),xaxis=list(title="Gene"))

工作正常。

但是，我想添加仅在悬停时才能看到的文本。

我认为这会奏效：

conditions.text <- paste(paste("C",1:50,sep="."),rep(paste(LETTERS[sample(26,10,replace=T)],collapse=""),50),sep=":")
heatmap.plotly <- plot_ly(x=colnames(mat),y=rownames(mat),z=mat,type="heatmap",colors=colorRamp(c("darkblue","white","darkred")),colorbar=list(title="Score",len=0.4),hoverinfo='text',text=~conditions.text) %>%
  layout(yaxis=list(title="Condition"),xaxis=list(title="Gene"))

但事实并非如此。将鼠标悬停在情节上时，我实际上看不到任何文字。

请注意，我正在使用 amatrix而不是melted data.frame.

我试图得到一个内部节点的高度dendrogram的BFS顺序.

该utils::str函数dendrogram按BFS顺序打印.所以我认为我会使用它(将输出重定向到文件并对其进行一些解析以获取我需要的信息).

我的'dendrogram'2个分支机构和5902个成员总共可下载RDS文件链接:dendro.RDS.

当我尝试:

utils::str(dendro)

我收到此错误:

Error in getOption("OutDec") : node stack overflow
Error during wrapup: node stack overflow

我尝试使用一个简单的递归函数:

nodeHeights <- function(dendro){
  if(is.leaf(dendro))
    0
  else{
    cat(attr(dendro,"height"),"\n")
    max(nodeHeights(dendro[[1]]),nodeHeights(dendro[[2]]))+1
  }
}

但是:nodeHeights(dendro)

抛出此错误:

Error: evaluation nested too deeply: infinite recursion / options(expressions=)?
Error during wrapup: evaluation nested too deeply: infinite recursion / options(expressions=)?

任何的想法？或任何建议如何获得的节点高度dendrogram的BFS订单？

我正在尝试使用这些数据来拟合beta regression模型：betareg functionbetareg package

df <- data.frame(category=c("c1","c1","c1","c1","c1","c1","c2","c2","c2","c2","c2","c2","c3","c3","c3","c3","c3","c3","c4","c4","c4","c4","c4","c4","c5","c5","c5","c5","c5","c5"),
                 value=c(6.6e-18,0.0061,0.015,1.1e-17,4.7e-17,0.0032,0.29,0.77,0.64,0.59,0.39,0.72,0.097,0.074,0.073,0.08,0.06,0.11,0.034,0.01,0.031,0.041,4.7e-17,0.025,0.58,0.14,0.24,0.29,0.55,0.15),stringsAsFactors = F)

df$category <- factor(df$category,levels=c("c1","c2","c3","c4","c5"))

使用此命令：

library(betareg)
fit <- betareg(value ~ category, data = df)

我得到这个error：

Error in chol.default(K) : 
  the leading minor of order 5 is not positive definite
In addition: Warning message:
In sqrt(wpp) : NaNs produced
Error in chol.default(K) : 
  the leading minor of order 5 is not positive definite
In addition: Warning messages:
1: In betareg.fit(X, Y, Z, weights, offset, link, link.phi, …

我正在尝试创建一个森林图，R plotly在其中我要通过相应的p值对效果大小（点）及其误差线进行颜色编码。

以下是玩具数据：

set.seed(1)

factors <- paste0(1:25,":age")
effect.sizes <- rnorm(25,0,1)
effect.errors <- abs(rnorm(25,0,1))
p.values <- runif(25,0,1)

这是我正在尝试的：

library(dplyr)
plotly::plot_ly(type='scatter',mode="markers",y=~factors,x=~effect.sizes,color=~p.values,colors=grDevices::colorRamp(c("darkred","gray"))) %>%
      plotly::add_trace(error_x=list(array=effect.errors),marker=list(color=~p.values,colors=grDevices::colorRamp(c("darkred","gray")))) %>%
      plotly::colorbar(limits=c(0,1),len=0.4,title="P-Value") %>%
      plotly::layout(xaxis=list(title="Effect Size",zeroline=T,showticklabels=T),yaxis=list(title="Factor",zeroline=F,showticklabels=T))

这给了我：

除了以下几点，这与我想要的非常接近：

1. 我希望误差条的颜色类似于效果大小（通过相应的p值）。
2. 删除trace下面的两个图例colorbar
3. y轴上的标签顺序为 factors

任何的想法？

我有我想要的XY数据（tSNE高维数据的 2D嵌入）scatter plot。数据被分配给几个clusters，所以我想对点进行颜色编码cluster，然后为每个点添加一个标签cluster，该标签与clusters具有相同的颜色编码，并且位于（尽可能）从cluster的点。

任何想法如何做到这一点使用R在任ggplot2和ggrepel或plotly？

这是示例数据（XY坐标和cluster分配在 中df，标签在 中label.df）及其ggplot2一部分：

library(dplyr)
library(ggplot2)
set.seed(1)
df <- do.call(rbind,lapply(seq(1,20,4),function(i) data.frame(x=rnorm(50,mean=i,sd=1),y=rnorm(50,mean=i,sd=1),cluster=i)))
df$cluster <- factor(df$cluster)

label.df <- data.frame(cluster=levels(df$cluster),label=paste0("cluster: ",levels(df$cluster)))

ggplot(df,aes(x=x,y=y,color=cluster))+geom_point()+theme_minimal()+theme(legend.position="none")

我有data.frame几列：

set.seed(1)
df <- data.frame(cluster=LETTERS[1:4],group=c(rep("m",2),rep("f",2)),point=rnorm(4),err=runif(4,0.1,0.3))

并且我将添加另一列"\n" concatenates，其中列名在值之前，其各自行的所有列。

我知道这个：

library(tidyr)
library(dplyr)
tidyr::unite(df,text,sep="\n")

给我这个tibble：

                                         text
1  A\nm\n0.487429052428485\n0.286941046221182
2  B\nm\n0.738324705129217\n0.142428504256532
3  C\nf\n0.575781351653492\n0.230334753217176
4 D\nf\n-0.305388387156356\n0.125111019192263

但我想要的是tibble：

                                         text
1  cluster: A\ngroup: m\npoint: 0.487429052428485\nerr: 0.286941046221182
2  cluster: B\ngroup: m\npoint: 0.738324705129217\nerr: 0.142428504256532
3  cluster: C\ngroup: f\npoint: 0.575781351653492\nerr: 0.230334753217176
4 cluster: D\ngroup: f\npoint: -0.305388387156356\nerr: 0.125111019192263

任何的想法？