monocle2轨迹分析（monocle2轨迹分析包安装）

安装monocle2

• 下载源代码

wget https://www.bioconductor.org/packages/release/bioc/src/contrib/monocle_2.26.0.tar.gz
tar -xf monocle*gz

• 修改源代码bug

monocle/R/BEAM.R

#203行的
progenitor_method == 'duplicate'
#改为
'duplicate' %in% progenitor_method

#251行的
progenitor_method == 'sequential_split'
#改为
'sequential_split' %in% progenitor_method

monocle/R/order_cells.R

#删除1620行
if(class(projection) != 'matrix')

• 安装

install.packages(remotes)
library(remotes)
remotes::install_local("monocle")

从anndata导出monocle所需的输入文件

bdata是注释好细胞类型的anndata对象

adata是原始的count数据，没经过任何处理

所以要从bdata获得pd，adata.var作为fd，adata.X作为count信息

import pandas as pd
import scanpy as sc
from scipy.io import mmwrite

adata=sc.datasets.pbmc3k()
bdata=sc.datasets.pbmc3k_processed()
adata
# AnnData object with n_obs × n_vars = 2700 × 32738
#     var: 'gene_ids'
bdata
# AnnData object with n_obs × n_vars = 2638 × 1838
#     obs: 'n_genes', 'percent_mito', 'n_counts', 'louvain'
#     var: 'n_cells'
#     uns: 'draw_graph', 'louvain', 'louvain_colors', 'neighbors', 'pca', 'rank_genes_groups'
#     obsm: 'X_pca', 'X_tsne', 'X_umap', 'X_draw_graph_fr'
#     varm: 'PCs'
#     obsp: 'distances', 'connectivities'

• pd

adata = adata[adata.obs_names.isin(bdata.obs_names)]
m=pd.merge(adata.obs,bdata.obs,left_index=True,right_index=True,how="left")
adata.obs = m.loc[:,["louvain"]]

• fd 删除不表达的基因

lg=adata.X.sum(axis=0)>
0.5
lg=lg.tolist()[0]
adata=adata[:,lg]

• 修改列名

adata.obs.rename(columns={
"louvain":"cell_type"}
,inplace=True)
adata.obs.insert(loc=0,column="Obs",value=adata.obs_names)
adata.var.insert(loc=0,column="gene_short_name",value=adata.var_names)

• 保存数据

mmwrite("PBMC.mtx",adata.X.transpose())
adata.obs.to_csv("PBMC_pd.csv",index=False)
adata.var.loc[:,["gene_short_name"]].to_csv("PBMC_fd.csv",index=False)

• 高变基因

sc.pp.normalize_total(adata, target_sum=10000, inplace=True)
sc.pp.log1p(adata,base=None)
sc.pp.highly_variable_genes(adata,n_top_genes=2000,flavor="seurat", subset=True,inplace=True)
pd.DataFrame({
"Var":adata.var_names}
    ).to_csv("PBMC_hvg.csv",index=False)

• 得到如下4个文件

PBMC.mtx
PBMC_fd.csv  
PBMC_hvg.csv 
PBMC_pd.csv

• 压缩mtxa文件

gzip PBMC.mtx

• 文件内容

# PBMC_fd.csv
gene_short_name
AL627309.1
AP006222.2
#PBMC_hvg.csv 
Var
TNFRSF4
CPSF3L
#PBMC_pd.csv
Obs,cell_type
AAACATACAACCAC-1,CD4 T cells
AAACATTGAGCTAC-1,B cells

构造monocle对象

suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(tibble))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(tidyr))
suppressPackageStartupMessages(library(magrittr))
suppressPackageStartupMessages(library(patchwork))
suppressPackageStartupMessages(library(Matrix))
suppressPackageStartupMessages(library(monocle))

  data - Matrix::readMM("PBMC.mtx.gz")
  pd - data.table::fread("PBMC_pd.csv", data.table = FALSE, header = TRUE) %>
    %
    tibble::column_to_rownames("Obs")
  fd - data.table::fread("PBMC_fd.csv", data.table = FALSE, header = TRUE)
  rownames(fd) - fd$gene_short_name
  
  cds - newCellDataSet(data, 
    phenoData = new("AnnotatedDataFrame", data = pd), 
    featureData = new("AnnotatedDataFrame", data = fd), 
    expressionFamily = negbinomial.size()
    )
  rm(data,pd,fd)　# 删除不需要的变量

估计size factor和离散度

cds - estimateSizeFactors(cds)
cds - estimateDispersions(cds)

轨迹定义基因选择及可视化和构建轨迹

ordering_genes - data.table::fread("PBMC_hvg.csv", header = TRUE) %>
    %
  dplyr::pull(1)
ordering_genes - intersect(rownames(cds), ordering_genes)
cds - setOrderingFilter(cds, ordering_genes)
plot_ordering_genes(cds)

降维

cds - reduceDimension(cds, max_components = 2, method = "DDRTree")
cds - orderCells(cds)

轨迹图

#以pseudotime值上色 
plot_cell_trajectory(cds,color_by="Pseudotime", size=1,show_backbone=TRUE)
#以细胞类型上色
plot_cell_trajectory(cds,color_by="cell_type", size=1,show_backbone=TRUE)
#以细胞状态上色
plot_cell_trajectory(cds, color_by = "State",size=1,show_backbone=TRUE)
#轨迹图分面显示
plot_cell_trajectory(cds, color_by = "State") + facet_wrap("~State", nrow = 1)

树状图

p1 - plot_cell_trajectory(mycds, x = 1, y = 2, color_by = "cell_type") + 
  theme(legend.position='none',panel.border = element_blank())  #去掉第一个的legend

p2 - plot_complex_cell_trajectory(mycds, x = 1, y = 2,
                                   color_by = "cell_type")+
  theme(legend.title = element_blank()) 
p1|p2

指定基因的可视化

s.genes - c("需要可视化的基因向量")
p1 - plot_genes_jitter(cds[s.genes,], grouping = "cell_type", color_by = "cell_type")
p2 - plot_genes_violin(cds[s.genes,], grouping = "cell_type", color_by = "cell_type")
p3 - plot_genes_in_pseudotime(cds[s.genes,], color_by = "cell_type")
plotc - p1|p2|p3

寻找拟时相关的基因（拟时差异基因）

Time_diff - differentialGeneTest(cds[ordergene,], cores = 8, 
                                  fullModelFormulaStr = "~sm.ns(Pseudotime)")

Time_genes - Time_diff %>
    % pull(gene_short_name) %>
    % as.character()
plot_pseudotime_heatmap(cds[Time_genes,], num_clusters=8, show_rownames=T, return_heatmap=T)

Reference

https://www.jianshu.com/p/5d6fd4561bc0
http://cole-trapnell-lab.github.io/monocle-release/
http://pklab.med.harvard.edu/scw2014/monocle_tutorial_oedsymbol.html
http://cole-trapnell-lab.github.io/monocle-release/tutorials/

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