之前在自己的小机器上分析，现在需要在学校集群进行分析，因此需要在两个没有公网ip且不互联的服务器之间转移大量数据。因此计划使用Rclone，通过OneDrive进行中转。
打包需要转移的数据
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| data <- list()
ref_sce <- readRDS('~/upload/zl_liu/data/pca.rds')
data$zyy_umi <- ref_sce@assays$RNA@counts
data$zyy_meta <- ref_sce@meta.data
ref_sce <- readRDS('~/work_st/Prognosis/idea_2/fig3.2/fig6/sce.rds')
data$ch_umi <- ref_sce@assays$originalexp@counts
data$ch_meta <- ref_sce@meta.data
tp_dir <- list(
P1 = '~/work/GSE137829/GSM4089151_P1_gene_cell_exprs_table.txt.gz',
P2 = '~/work/GSE137829/GSM4089152_P2_gene_cell_exprs_table.txt.gz',
P3 = '~/work/GSE137829/GSM4089153_P3_gene_cell_exprs_table.txt.gz',
P4 = '~/work/GSE137829/GSM4089154_P4_gene_cell_exprs_table.txt.gz',
P5 = '~/work/GSE137829/GSM4711414_P5_gene_cell_exprs_table.txt.gz',
P6 = '~/work/GSE137829/GSM4711415_P6_gene_cell_exprs_table.txt.gz'
)
sce <- list()
for (i in names(tp_dir)){
tmp <- read.table(gzfile(tp_dir[[i]]), header = T)
umi <- Matrix::as.matrix(x = tmp[-c(1,2)])
umi <- Matrix::Matrix(data = umi, sparse = T)
rownames(umi) <- tmp$Symbol
sce[[i]] <- Seurat::CreateSeuratObject(umi, project = i,
min.cells = 3, min.features = 200)
}
sce <- Reduce(merge, sce)
data$geo_umi <- sce@assays$RNA@counts
data$geo_meta <- sce@meta.data
saveRDS(data, '22.10.04.rds')
|
Rclone挂载OneDrive
- conda activate jupyter
- conda install -c conda-forge rclone -y
在两台服务器上挂载同一个OneDrive，第二台可以直接使用第一台的配置
Rclone上传下载数据
- rclone copy –ignore-existing –progress –ignore-errors –transfers=1 ./22.10.04.rds onedrive:tmp
- rclone ls onedrive:tmp
- rclone copy –ignore-existing –progress –ignore-errors –transfers=1 onedrive:tmp/22.10.04.rds .