周末了各位,昨天去看了奥本海默
,不得不说,大神就是大神。😘
比起我们的电影,似乎诺兰
更好地还原了奥本海默
的真实。🧐
言归正传,今天分享的是CCPlotR
包,用于基于scRNAseq
数据推断细胞间相互作用的可视化。😋
rm(list = ls())
#devtools::install_github("Sarah145/CCPlotR")
library(tidyverse)
library(CCPlotR)
我们先把示例数据导进来吧,一共有2
个文件哦。🫠
data(toy_data, toy_exp, package = 'CCPlotR')
先查看一下数据的结构呢。🥳
DT::datatable(toy_data)
接着是另外一个data
。🥳
DT::datatable(toy_exp)
cc_heatmap(toy_data)
展示top10
。🥰
cc_heatmap(toy_data, option = 'B', n_top_ints = 10)
cc_heatmap(toy_data, option = 'CellPhoneDB')
cc_heatmap(toy_data, option = 'Liana')
cc_dotplot(toy_data)
展示top15
。😚
cc_dotplot(toy_data, option = 'B', n_top_ints = 15)
cc_dotplot(toy_data, option = 'CellPhoneDB', n_top_ints = 15)
cc_dotplot(toy_data, option = 'Liana', n_top_ints = 15)
cc_network(toy_data)
cc_network(toy_data, colours = c('orange', 'cornflowerblue', 'hotpink'), option = 'B')
cc_network(toy_data, colours = c('orange', 'cornflowerblue', 'hotpink'),
option = 'B',
layout = "nicely"
)
cc_circos(toy_data)
cc_circos(toy_data, option = 'B', n_top_ints = 10)
cc_circos(toy_data, option = 'C', n_top_ints = 15,
exp_df = toy_exp,
cell_cols = c(`B` = 'hotpink', `NK` = 'orange', `CD8 T` = 'cornflowerblue'),
palette = 'PuRd',
cex = 0.5)
cc_arrow(toy_data, cell_types = c('B', 'CD8 T'), colours = c(`B` = 'hotpink', `CD8 T` = 'orange'))
cc_arrow(toy_data, cell_types = c('NK', 'CD8 T'), option = 'B', exp_df = toy_exp, n_top_ints = 10, palette = 'OrRd')
cc_sigmoid(toy_data)
最后祝大家早日不卷!~