Build a bioinformatics r environment from 0 (step on the pit record)

# test Seurat package

rm(list=ls())
suppressMessages(require(Seurat))
suppressMessages(require(ggplot2))
suppressMessages(require(cowplot))
#suppressMessages(require(scater))
#suppressMessages(require(scran))
#suppressMessages(require(BiocParallel))
#suppressMessages(require(BiocNeighbors))
setwd("/data/wangdongxue/yxk/R Learning tests /Seurat3")
pancreas.data <- readRDS(file = "./data/pancreas_v3_file/pancreas_expression_matrix.rds")
metadata <- readRDS(file = "./data/pancreas_v3_file/pancreas_metadata.rds")
pancreas <- CreateSeuratObject(pancreas.data, meta.data = metadata)
# Note that this is different from other situations , There is only metadata Of annotation information 
# The previous data set was just passed in count matrix, But not at the same time meta.data

#  Standardized data (Filter cells omitted , This has little effect )
# Normalize and find variable features
pancreas <- NormalizeData(pancreas, verbose = FALSE)
pancreas <- FindVariableFeatures(pancreas, selection.method = "vst", nfeatures = 2000, verbose = FALSE)

# Run the standard workflow for visualization and clustering
pancreas <- ScaleData(pancreas, verbose = FALSE)
pancreas <- RunPCA(pancreas, npcs = 30, verbose = FALSE)
pancreas <- RunUMAP(pancreas, reduction = "pca", dims = 1:30)

p1 <- DimPlot(pancreas, reduction = "umap", group.by = "tech")# draw batch chart 
p2 <- DimPlot(pancreas, reduction = "umap", group.by = "celltype", label = TRUE, repel = TRUE) + 
  NoLegend() # draw celltype Graph 
print(p1+p2)
ggsave("vis_pancras.png",plot=p1+p2)

give the result as follows

rm(list=ls())
suppressMessages(library(splatter))
suppressMessages(library(Seurat))
suppressMessages(library(ggplot2))


params <- newSplatParams()

params <- setParam(params, "nGenes", 5000)
params <- setParam(params, "batchCells", c(500,500,500))
params <- setParam(params, "batch.facLoc", 0.5)
params <- setParam(params, "batch.facScale", 0.5)
params <- setParam(params, "group.prob", c(1/3,1/3,1/3))

sim <- splatSimulate(params, method="groups", verbose=FALSE)

## create Seurat object
data <- CreateSeuratObject(counts(sim),meta.data = data.frame(colData(sim)))
#

# Note that this is different from other situations , There is only metadata Of annotation information 
# The previous data set was just passed in count matrix, But not at the same time meta.data

data <- NormalizeData(data, verbose = FALSE)
data <- FindVariableFeatures(data, selection.method = "vst", nfeatures = 2000, verbose = FALSE)

# Run the standard workflow for visualization and clustering
data <- ScaleData(data, verbose = FALSE)
data <- RunPCA(data, npcs = 30, verbose = FALSE)
data <- RunUMAP(data, reduction = "pca", dims = 1:30)

p1 <- DimPlot(data, reduction = "umap", group.by = "Batch")# draw batch chart 
p2 <- DimPlot(data, reduction = "umap", group.by = "Group") 
print(p1+p2)

ggsave("vis_splatter.png",plot=p1+p2)

give the result as follows



The end , And the flower

Add questions 1
Find the problem
When using library(reticulate) when , Unable to import scipy package , There are the following problems
terms of settlement ：
https://blog.csdn.net/weixin_44121197/article/details/123018363

# 
sudo apt-get install software-properties-common
sudo apt-get update
# 
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-4.9
sudo apt-get upgrade libstdc++6

Then the problem is solved

Add questions 2

In fact, this is the end of the installation R Not perfect , I hope to install another one jupyter Of R kernel, But what I installed above is the main environment R, There is no way to use jupyter kernel Of , So it can be conda Create your own in the environment R4 Version environment , And then add kernel That's all right. ,
The operation steps are as follows

But I found out in R4 There will be problems in packaging in the environment , I remember there was no such problem in the main environment , But still record it ,
Notice now that I am R4 Of conda Packaging in the environment , Not the main environment R, Dangzhuang devtools Packet time , Found the following error
terms of settlement ： At present R4 Input... In the environment

conda install libgit2

One more question

terms of settlement
https://github.com/r-lib/systemfonts/issues/35
Administrator input

sudo apt -y install libfontconfig1-dev

No problem

Add stringi Installation problems

I'm in today mac Try installing one yourself on conda R4.0.3 Environment , Because my computer has Seurat2, and Seurat4, Only No Seruat3, I thought I'd try , Found or encountered stringi The problem of
I first created a conda Environmental Science

conda create -n Seurat3 -c conda-forge r-base==4.0.3

After successful installation ,
When the input

install.packages("stringi")

There is always a mistake , The following package failed to download
But I found out in conda Environmental R In bag , Not like that stringi package ,conda The environment has its own stringi package
https://github.com/conda-forge/r-stringi-feedstock
Enter... In the current environment

Must be entered in advance conda config That sentence , Otherwise, the installation will still fail , I don't know why , It's a strange feeling , I'll try it on the server later

install Seurat3

If you want to conda Installation in the environment Seurat3, So in R Input in

remotes::install_version("Seurat", version = "3.1.5")

Do not install seurat3.2.3. This one is wrong once it is installed , It's disgusting
And then there's the setup kernel It's something
I was on the server anaconda The environment created is R4, Now in R4 Successfully installed in Seurat3, Now I'm going to be in jupyter Put this in the R4 Environmental kernel Fall out
Steps are as follows

install.packages("BiocManager")
install.packages('IRkernel')
conda install -c conda-forge r-seurat
IRkernel::installspec(name = 'Seurat3', displayname = 'Seurat3')

Pay attention to my kernel Name and conda The names created are different , But this doesn't affect
The final results are as follows