b.limour.top/source/_posts/qsub使用方法.md

---

title: 复旦高性能计算 (二) qsub使用方法 tags:

• Linux
• qsub
• 高性能计算节点 id: '637' categories:
• - 生物信息学
• - 超算基础 date: 2021-09-19 19:43:00 ---

管理使用

• 查看所有任务：qstat
• 删除某个任务：qdel <Job ID>
• 交互使用：qsub -I [-q <batch>]
• 登入指定节点：qsub -l nodes=cu05 -I

提交任务

示例一 jupyter.pbs

• 创建 /home/rqzhang/jupyter.pbs 写入以下内容：

  #!/bin/bash
  #PBS -q batch
  #PBS -V
  #PBS -o /home/rqzhang/jupyter.out
  #PBS -e /home/rqzhang/jupyter.err
  #PBS -l nodes=1:ppn=8
  #PBS -r y
  cd /home/rqzhang
  /home/rqzhang/zlliu/bin/frpc -c /home/rqzhang/zlliu/frp/frpc.ini &
  jupyter notebook --ip='0.0.0.0' --port=18888 --no-browser --NotebookApp.token="<token>" --NotebookNotary.db_file=':memory:'
  echo $HOME
  

• 提交任务： qsub ./jupyter.pbs

示例二 SingleR.pbs

• 创建 /home/rqzhang/`SingleR.pbs` 写入以下内容：

  #!/bin/bash
  #PBS -q fat
  #PBS -V
  #PBS -o /home/rqzhang/SingleR.out
  #PBS -e /home/rqzhang/SingleR.err
  #PBS -l nodes=1:ppn=32
  #PBS -r y
  cd /home/rqzhang
  Rscript /home/rqzhang/r.pbs/21.07.15.10x.hM1.se.R
  echo $HOME
  

• 创建 /home/rqzhang/r.pbs/21.07.15.10x.hM1.se.R 写入以下内容：

  # 0、加载程辑包
  library(Seurat)
  library(dplyr)
  library(patchwork)
  
  # 配置数据和mark基因表的路径
  root_path = "~/zlliu/R_data/hBLA"
  # 配置结果保存路径
  output_path = "~/zlliu/R_output/21.07.15.10x.hM1.se/"
  
  # 设置工作目录，输出文件将保存在此目录下
  setwd(output_path)
  print(getwd())
  
  # 5、读取全部samples
  tp_samples <- list.files(file.path(root_path, "10x"))
  tp_dir <- file.path(root_path, "10x", tp_samples)
  names(tp_dir) <- tp_samples
  counts <- Read10X(data.dir = tp_dir)
  scRNA <- CreateSeuratObject(counts, project = 'hBLA',
                          min.cells = 3, min.features = 200)
  rm(counts)
  rm(tp_dir)
  gc()
  
  # 6、QC
  scRNA[["percent.mt"]] <- PercentageFeatureSet(scRNA, pattern = "^MT-")
  
  # 6.2、分割数据以便QC
  x10s <- SplitObject(scRNA, split.by = 'ident')
  rm(scRNA)
  gc()
  
  # 6.3、分别对各样本进行QC
  x10s$BA213 <- subset(x10s$BA213,
                subset = nFeature_RNA > 200 & nFeature_RNA < 4000 & percent.mt < 10)
  x10s$BA04 <- subset(x10s$BA04,
                subset = nFeature_RNA > 200 & nFeature_RNA < 3000 & percent.mt < 10)
  x10s$BA09 <- subset(x10s$BA09,
                subset = nFeature_RNA > 200 & nFeature_RNA < 3500 & percent.mt < 10)
  x10s$BA21 <- subset(x10s$BA21,
                subset = nFeature_RNA > 200 & nFeature_RNA < 4500 & percent.mt < 10)
  x10s$BA22 <- subset(x10s$BA22,
                subset = nFeature_RNA > 200 & nFeature_RNA < 3000 & percent.mt < 10)
  x10s$BA39 <- subset(x10s$BA39,
                subset = nFeature_RNA > 200 & nFeature_RNA < 4000 & percent.mt < 10)
  x10s$BA40 <- subset(x10s$BA40,
                subset = nFeature_RNA > 200 & nFeature_RNA < 4000 & percent.mt < 10)
  x10s$BA44 <- subset(x10s$BA44,
                subset = nFeature_RNA > 200 & nFeature_RNA < 4000 & percent.mt < 10)
  x10s$BA45 <- subset(x10s$BA45,
                subset = nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 10)
  
  # 加载 SingleR
  library(SingleR)
  library(SummarizedExperiment)
  library(scater)
  library(BiocParallel) # 并行计算加速
  
  # 加载参考数据集 大概3分钟
  if (!("hM1.se" %in% ls())){
  hM1.se <- readRDS("/home/rqzhang/zlliu/R_data/human_M1_10x/hM1.se.rds")
  }
  
  f_get_pred <- function(scRNA, lc_i){
      
  # 如果已经计算过了，就不再重复计算，直接返回上一次的结果
  if(file.exists(sprintf( "%02d_hM1_subclass.txt", lc_i))){
      result_main_hpca <- read.table(sprintf( "%02d_hM1_subclass.txt", lc_i), stringsAsFactors = F)
      return (result_main_hpca)
  }
      
  # 导出原始counts矩阵
  test.count=as.data.frame(scRNA[["RNA"]]@counts)
      
  # 保留共同基因
  common_hpca <- intersect(rownames(test.count), rownames(hM1.se))
  lc_hM1.se <- hM1.se[common_hpca,]
  test.count_forhpca <- test.count[common_hpca,]
  gc()
      
  # 生成test数据集
  test.count_forhpca.se <- SummarizedExperiment(assays=list(counts=test.count_forhpca))
  test.count_forhpca.se <- logNormCounts(test.count_forhpca.se)
      
  # 进行分类预测 大概一小时
  pred.main.hpca <- SingleR(test = test.count_forhpca.se, ref = lc_hM1.se, labels = hM1.se$meta$subclass_label, BPPARAM=MulticoreParam(32))
  # 32 CPUs
  gc()
      
  #构造返回结果
  result_main_hpca <- as.data.frame(pred.main.hpca$labels)
  result_main_hpca$CB <- rownames(pred.main.hpca)
  colnames(result_main_hpca) <- c('hM1_subclass', 'CB')
  write.table(result_main_hpca, sprintf( "%02d_hM1_subclass.txt", lc_i)) #保存下来，方便以后调用
  result_main_hpca
  }
  
  tp_sc <- x10s
  # rm(x10s)
  gc()
  tp_sc_st <- 1
  tp_sc_len <- length(tp_sc)
  for (lc_i in tp_sc_st:tp_sc_len) {
  print(lc_i)
  f_get_pred(tp_sc[[lc_i]], lc_i)
  }
  

• 提交任务： qsub ./`` `SingleR.pbs` ``

示例三 cellphonedb.pbs

• 创建 /home/rqzhang/`cellphonedb.pbs` 写入以下内容：

  #!/bin/bash
  #PBS -q fat
  #PBS -V
  #PBS -o /home/rqzhang/cellphonedb.out
  #PBS -e /home/rqzhang/cellphonedb.err
  #PBS -l nodes=1:ppn=32
  #PBS -r y
  
  cd /home/rqzhang/zlliu/R_output/21.07.15.10x.hM1.se/SingleR_01
  cellphonedb method statistical_analysis  cellphonedb_meta.txt  cellphonedb_count.txt --counts-data=gene_name  --threads=32
  cellphonedb plot dot_plot
  cellphonedb plot heatmap_plot cellphonedb_meta.txt
  
  cd /home/rqzhang/zlliu/R_output/21.07.15.10x.hM1.se/SingleR_02
  cellphonedb method statistical_analysis  cellphonedb_meta.txt  cellphonedb_count.txt --counts-data=gene_name  --threads=32
  cellphonedb plot dot_plot
  cellphonedb plot heatmap_plot cellphonedb_meta.txt
  
  cd /home/rqzhang/zlliu/R_output/21.07.15.10x.hM1.se/SingleR_03
  cellphonedb method statistical_analysis  cellphonedb_meta.txt  cellphonedb_count.txt --counts-data=gene_name  --threads=32
  cellphonedb plot dot_plot
  cellphonedb plot heatmap_plot cellphonedb_meta.txt
  
  cd /home/rqzhang/zlliu/R_output/21.07.15.10x.hM1.se/SingleR_04
  cellphonedb method statistical_analysis  cellphonedb_meta.txt  cellphonedb_count.txt --counts-data=gene_name  --threads=32
  cellphonedb plot dot_plot
  cellphonedb plot heatmap_plot cellphonedb_meta.txt
  
  cd /home/rqzhang/zlliu/R_output/21.07.15.10x.hM1.se/SingleR_05
  cellphonedb method statistical_analysis  cellphonedb_meta.txt  cellphonedb_count.txt --counts-data=gene_name  --threads=32
  cellphonedb plot dot_plot
  cellphonedb plot heatmap_plot cellphonedb_meta.txt
  
  cd /home/rqzhang/zlliu/R_output/21.07.15.10x.hM1.se/SingleR_06
  cellphonedb method statistical_analysis  cellphonedb_meta.txt  cellphonedb_count.txt --counts-data=gene_name  --threads=32
  cellphonedb plot dot_plot
  cellphonedb plot heatmap_plot cellphonedb_meta.txt
  
  cd /home/rqzhang/zlliu/R_output/21.07.15.10x.hM1.se/SingleR_07
  cellphonedb method statistical_analysis  cellphonedb_meta.txt  cellphonedb_count.txt --counts-data=gene_name  --threads=32
  cellphonedb plot dot_plot
  cellphonedb plot heatmap_plot cellphonedb_meta.txt
  
  cd /home/rqzhang/zlliu/R_output/21.07.15.10x.hM1.se/SingleR_08
  cellphonedb method statistical_analysis  cellphonedb_meta.txt  cellphonedb_count.txt --counts-data=gene_name  --threads=32
  cellphonedb plot dot_plot
  cellphonedb plot heatmap_plot cellphonedb_meta.txt
  
  cd /home/rqzhang/zlliu/R_output/21.07.15.10x.hM1.se/SingleR_09
  cellphonedb method statistical_analysis  cellphonedb_meta.txt  cellphonedb_count.txt --counts-data=gene_name  --threads=32
  cellphonedb plot dot_plot
  cellphonedb plot heatmap_plot cellphonedb_meta.txt
  
  echo $HOME
  

• 提交任务： qsub ./```` ``` `` `cellphonedb.pbs` `` ``` ````