circos.heatmap():rowMeans(m) 中的错误:'x' 必须是至少有两个维度的数组

问题描述

我正在按照教程制作 circular plots in R。假设我有这个数据集:

set.seed(123)
    mat1 = rbind(cbind(matrix(rnorm(50*5,mean = 1),nr = 50),matrix(rnorm(50*5,mean = -1),nr = 50)),cbind(matrix(rnorm(50*5,nr = 50))
                )
    rownames(mat1) = paste0("R",1:100)
    colnames(mat1) = paste0("C",1:10)
    mat1 = mat1[sample(100,100),] # randomly permute rows
    split = sample(letters[1:5],100,replace = TRUE)
    split = factor(split,levels = letters[1:5])

我尝试使用 Heatmap 并正确返回。

library(ComplexHeatmap)
Heatmap(mat1,row_split = split)

enter image description here

但是,当我尝试使用 circos.heatmap() 时,它返回错误

library(circlize)
col_fun1 = colorRamp2(c(-2,2),c("blue","white","red"))
circos.heatmap(mat = mat1,split = split,col = col_fun1)
circos.heatmap(mat = mat1,col = col_fun1)
> Error in rowMeans(m) : 'x' must be an array of at least two dimensions

矩阵本身的维度为 100 x 10。有什么我遗漏的吗?

解决方法

CRAN 版本也有同样的问题,但安装开发版后修复了

devtools::install_github("jokergoo/circlize")

-测试

> library(circlize)
========================================
circlize version 0.4.13
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research,please cite:
Gu,Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================

> col_fun1 = colorRamp2(c(-2,2),c("blue","white","red"))
> ?circos.heatmap
starting httpd help server ... done
> 
> set.seed(123)
> mat1 = rbind(cbind(matrix(rnorm(50*5,mean = 1),nr = 50),+                    matrix(rnorm(50*5,mean = -1),nr = 50)),+              cbind(matrix(rnorm(50*5,nr = 50))
+             )
> rownames(mat1) = paste0("R",1:100)
> colnames(mat1) = paste0("C",1:10)
> mat1 = mat1[sample(100,100),] # randomly permute rows
> split = sample(letters[1:5],100,replace = TRUE)
> spilt = factor(split,levels = letters[1:5])
> col_fun1 = colorRamp2(c(-2,"red"))
> circos.heatmap(mat1,split = split,col = col_fun1)
> circos.clear()

-输出

enter image description here