conda env create --file seurat.yml

mkdir bin

mv <path to seurat_tutorial.Rmd>/seurat_tutorial.Rmd ./bin/

mkdir data
cd data

#download data
#The wget option -O specifies a file to which the documents is written, and here we use -, meaning it will written to standard output and piped to tar and the tar #flag -x enables extraction of archive files and -z decompresses, compressed archive files created by gzip

#dataset for tutorial
wget -c https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz -O - | tar -xz
##for our new dataset, we used the filtered dataset
#wget -c https://cf.10xgenomics.com/samples/cell exp/4.0.0/Targeted_SC3v3_Human_Glioblastoma_Neuroscience/Targeted_SC3v3_Human_Glioblastoma_Neuroscience_raw_feature_bc_matrix.tar.gz -O - | tar -xz
wget -c https://cf.10xgenomics.com/samples/cell-exp/4.0.0/Targeted_SC3v3_Human_Glioblastoma_Neuroscience/Targeted_SC3v3_Human_Glioblastoma_Neuroscience_filtered_feature_bc_matrix.tar.gz -O - | tar -xz
cd ..

mkdir output
cd output
mkdir images 
mkdir timings
cd ..

conda activate seurat
rstudio 
#which rstudio --- /usr/bin/rstudio 
#rstudio -version  --- 2021.09.1+372

conda env remove -n seurat --all

mkdir bin

mv <path to seurat_tutorial.Rmd>/seurat_tutorial.Rmd ./bin/

mkdir data
cd data

#download data
#The wget option -O specifies a file to which the documents is written, and here we use -, meaning it will written to standard output and piped to tar and the tar #flag -x enables extraction of archive files and -z decompresses, compressed archive files created by gzip

wget -c https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz -O - | tar -xz
cd ..

mkdir output
cd output
mkdir images 
mkdir timings
cd ..

sudo docker run --rm -d -p 8787:8787 -e PASSWORD=password -v $PWD:/home/rstudio/ seurat

sudo docker ps
sudo docker stop <container name>