In linux:
sudo R
sudo apt-get install python-virtualenv
sudo bash Anaconda3-2019.03-Linux-x86_64.sh
%then reopen the terminal
pip install louvain
pip install umap-learn
sudo apt-get install libudunits2-dev
sudo apt install libgdal-dev
sudo apt-get install mesa-common-dev
sudo apt-get install xorg
sudo apt-get install libcgal-dev libglu1-mesa-dev libglu1-mesa-dev
sudo apt-get install libx11-dev
sudo apt-get install libglu1-mesa-dev
conda install -c r r-rgl
In R:
devtools::dev_mode(path='~/tools/Monocle3/')
source("http://bioconductor.org/biocLite.R")
biocLite()
biocLite("monocle")
install.packages("reticulate")
library(reticulate)
devtools::install_github("cole-trapnell-lab/DDRTree", ref="simple-ppt-like")
devtools::install_github("cole-trapnell-lab/L1-graph")
devtools::install_github("cole-trapnell-lab/monocle-release", ref="monocle3_alpha")
devtools::dev_mode(path='~/tools/Seurat3/')
library(Seurat)
library(dplyr)
library(monocle)
sessionInfo()
R.utils::sourceDirectory('~/tools/Rcodes/Monocle3-alpha-plus/',modifiedOnly=FALSE,verbose=TRUE)
DeepGenes <- read.table("190220DeepTree4000genes.txt",sep="_")
DeepGenes2=as.vector(unlist(DeepGenes[,2]))
load("190627MouseLimb10X_Monocle3.Robj")
MouseFull <- ReadH5AD(file = "../190714MouseLimb10Xraw.h5ad")
pData(updated_MouseLimb10X_Monocle)$bh.pval.more.than.0.1 <- !MouseFull@meta.data$bh.pval.less.than.0.1
head(pData(updated_MouseLimb10X_Monocle))
cell_type_color <- c('0'='#000000',
'1'='#0118FA',
'2'='#C0C0C0',
'3'='#00FFFF',
'4'='#FF8380',
'5'='#8B8A1D',
'6'='#CAC379',
'7'='#00FF00',
'8'='#FFFA2C',
'9'='#C39CFB',
'10'='#7762F0',
'11'='#717E8D',
'12'='#A71206',
'13'='#35B5E2',
'14'='#9834E7',
'15'='#995432',
'16'='#483F84',
'17'='#ff0000',
'18'='#216407',
'19'='#ff42ef',
'20'='#FBC83C',
'21'='#FF9A21',
'22'='#B4246E',
'23'='#09658A',
'24'='#016edb')
DelayedArray:::set_verbose_block_processing(TRUE)
updated_MouseLimb10X_Monocle <- updated_MouseLimb10X_Monocle[,
pData(updated_MouseLimb10X_Monocle)$bh.pval.more.than.0.1]
options(DelayedArray.block.size=1000e6)
updated_MouseLimb10X_Monocle <- estimateSizeFactors(updated_MouseLimb10X_Monocle)
updated_MouseLimb10X_Monocle <- estimateDispersions(updated_MouseLimb10X_Monocle)
options(future.globals.maxSize= 8096 * 1024^2)
library(future)
plan(strategy = "multicore", workers = 30)
plot_pc_variance_explained(updated_MouseLimb10X_Monocle)
updated_MouseLimb10X_Monocle <- preprocessCDS(updated_MouseLimb10X_Monocle, num_dim = 50)
updated_MouseLimb10X_Monocle <- reduceDimension(updated_MouseLimb10X_Monocle, reduction_method = 'UMAP')
updated_MouseLimb10X_Monocle <- partitionCells(updated_MouseLimb10X_Monocle)
updated_MouseLimb10X_Monocle <- learnGraph(updated_MouseLimb10X_Monocle, RGE_method = 'SimplePPT')
plot_cell_trajectory(updated_MouseLimb10X_Monocle,cell_size=0.1,
color_by = "orig.ident") +
scale_color_manual(values = cell_type_color)
updated_MouseLimb10X_Monocle <- preprocessCDS(updated_MouseLimb10X_Monocle, num_dim = 12)
updated_MouseLimb10X_Monocle <- reduceDimension(updated_MouseLimb10X_Monocle, reduction_method = 'UMAP')
updated_MouseLimb10X_Monocle <- partitionCells(updated_MouseLimb10X_Monocle)
updated_MouseLimb10X_Monocle <- learnGraph(updated_MouseLimb10X_Monocle, RGE_method = 'SimplePPT')
plot_cell_trajectory(updated_MouseLimb10X_Monocle,cell_size=0.1,
color_by = "orig.ident") +
scale_color_manual(values = cell_type_color)
pData(updated_MouseLimb10X_Monocle)$bh.pval <- MouseFull@meta.data[row.names(pData(updated_MouseLimb10X_Monocle)),
'bh.pval']
pData(updated_MouseLimb10X_Monocle)$doublet.scores <- MouseFull@meta.data[row.names(pData(updated_MouseLimb10X_Monocle)),
'doublet.scores']
cds = ClusterSubsetPCA_3alpha(updated_MouseLimb10X_Monocle,idents=c("4","7","12","17","22"),genes = DeepGenes2)
plot_pc_variance_explained(cds)
options(DelayedArray.block.size=1000e6)
cds <- preprocessCDS(cds,num_dim = 20)
cds <- UMAPLearnGraphFixPartition_3alpha(cds)
stage = 10.5
cell_ids <- which(pData(cds)[, "stage"] == stage)
root_pr_nodes <- Cells2Nodes_3alpha(cds,cell_ids)
cds = orderCells(cds, root_pr_nodes = root_pr_nodes)
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "orig.ident") +
scale_color_manual(values = cell_type_color)
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "Pseudotime") + viridis::scale_color_viridis(option="plasma")
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "stage") + viridis::scale_color_viridis(option="plasma",discrete=TRUE)
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
markers=c("Osr1","Lum","Pdgfra","Myod1","Myog","Pax3","Pax7","Msc","Col6a3","Col1a1",
"Tnnt1","Tnnc1","Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red",
na.value = "snow2")
cell_ids = 'limb7_10_5AAATGCCAGCGAGAAA_1'
WhichCells(object = MouseFull, expression = Pax3 > 20)
options(DelayedArray.block.size=1000e6)
cds <- preprocessCDS(cds,num_dim = 9)
cds <- UMAPLearnGraphFixPartition_3alpha(cds)
stage = 10.5
cell_ids <- which(pData(cds)[, "stage"] == stage)
root_pr_nodes <- Cells2Nodes_3alpha(cds,cell_ids)
cds = orderCells(cds, root_pr_nodes = root_pr_nodes)
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "orig.ident") +
scale_color_manual(values = cell_type_color)
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "Pseudotime") + viridis::scale_color_viridis(option="plasma")
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "stage") + viridis::scale_color_viridis(option="plasma",discrete=TRUE)
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
markers=c("Osr1","Lum","Pdgfra","Myod1","Myog","Pax3","Pax7","Msc","Col6a3","Col1a1",
"Tnnt1","Tnnc1","Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red",
na.value = "snow2")
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
markers=c("Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red",
na.value = "snow2")
options(DelayedArray.block.size=1000e6)
cds <- preprocessCDS(cds,num_dim = 7)
cds <- UMAPLearnGraphFixPartition_3alpha(cds)
stage = 10.5
cell_ids <- which(pData(cds)[, "stage"] == stage)
root_pr_nodes <- Cells2Nodes_3alpha(cds,cell_ids)
cds = orderCells(cds, root_pr_nodes = root_pr_nodes)
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "orig.ident") +
scale_color_manual(values = cell_type_color)
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "Pseudotime") + viridis::scale_color_viridis(option="plasma")
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "stage") + viridis::scale_color_viridis(option="plasma",discrete=TRUE)
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
markers=c("Osr1","Lum","Pdgfra","Myod1","Myog","Pax3","Pax7","Msc","Col6a3","Col1a1",
"Tnnt1","Tnnc1","Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red",
na.value = "snow2")
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
markers=c("Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red",
na.value = "snow2")
cds = ClusterSubsetPCA_3alpha(updated_MouseLimb10X_Monocle,idents=c("4","7","12","17"),genes = DeepGenes2)
plot_pc_variance_explained(cds)
options(DelayedArray.block.size=1000e6)
cds <- preprocessCDS(cds,num_dim = 8)
cds <- UMAPLearnGraphFixPartition_3alpha(cds)
stage = 10.5
cell_ids <- which(pData(cds)[, "stage"] == stage)
root_pr_nodes <- Cells2Nodes_3alpha(cds,cell_ids)
cds = orderCells(cds, root_pr_nodes = root_pr_nodes)
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "orig.ident") +
scale_color_manual(values = cell_type_color)
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "Pseudotime") + viridis::scale_color_viridis(option="plasma")
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "stage") + viridis::scale_color_viridis(option="plasma",discrete=TRUE)
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
markers=c("Osr1","Lum","Pdgfra","Myod1","Myog","Pax3","Pax7","Msc","Col6a3","Col1a1",
"Tnnt1","Tnnc1","Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red",
na.value = "snow2")
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
markers=c("Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red",
na.value = "snow2")
options(DelayedArray.block.size=1000e6)
cds <- preprocessCDS(cds,num_dim = 7)
cds <- UMAPLearnGraphFixPartition_3alpha(cds)
stage = 10.5
cell_ids <- which(pData(cds)[, "stage"] == stage)
root_pr_nodes <- Cells2Nodes_3alpha(cds,cell_ids)
cds = orderCells(cds, root_pr_nodes = root_pr_nodes)
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "orig.ident") +
scale_color_manual(values = cell_type_color)
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "Pseudotime") + viridis::scale_color_viridis(option="plasma")
plot_cell_trajectory(cds,cell_size=0.1,
color_by = "stage") + viridis::scale_color_viridis(option="plasma",discrete=TRUE)
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
markers=c("Osr1","Lum","Pdgfra","Myod1","Myog","Pax3","Pax7","Msc","Col6a3","Col1a1",
"Tnnt1","Tnnc1","Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red",
na.value = "snow2")
plot_cell_trajectory(cds,use_color_gradient = TRUE,cell_size=0.1,
markers=c("Birc5", "Cenpa", "Top2a", "Mki67")) + scale_color_gradient(low="snow2", high="red",
na.value = "snow2")