# #——风格,回声= FALSE,结果=“隐藏”,消息= FALSE ------------------------- 图书馆(BiocStyle)图书馆(knitr)美元opts_chunk组(错误= FALSE,消息= FALSE,警告= FALSE) opts_chunk设置(fig.asp = 1) # #美元——安装、呼应= TRUE, eval = FALSE -------------------------------------- # ## 试试http://如果不支持https:// url # (!requireNamespace(“BiocManager”,悄悄地= TRUE)) # install.packages (BiocManager) # BiocManager::安装(“梅丽莎 ") # # ## 或下载从Github库# # install.packages (devtools) # devtools:: install_github(“andreaskapou /梅丽莎”build_vignettes = TRUE) # #——俾斯麦,eval = FALSE ------------------------------------------------------ # # 需要俾斯麦# bismark_methylation_extractor——全面——merge_non_CpG \ #——no_header——gzip bedGraph input_file。bam # #——binarise eval = FALSE ----------------------------------------------------- # 库(Melissa) # # Binarise scBS-seq数据# binarise_files (indir = " path_to_met_files_dir ") ## ---- compress_files eval = FALSE ----------------------------------------------- # gzip文件名# #——melissa_data_obj呼应= TRUE,消息= FALSE, eval = FALSE ------------------- # melissa_data < - create_melissa_data_obj (met_dir =“path_to_bin_met_dir”,# anno_file =“anno_file”,x = 3) # #——save_obj,eval = FALSE ----------------------------------------------------- # saveRDS(文件= " melissa_data_obj。rds”,melissa_data) # #——filter_regions_by_coverage eval = FALSE ----------------------------------- # melissa_data < - filter_by_cpg_coverage (melissa_data min_cpgcov = 10) # #——filter_regions_by_variability eval = FALSE -------------------------------- # melissa_data < - filter_by_variability (melissa_data min_var = 0.2) # #——filter_by_coverage_across_cells eval = FALSE ------------------------------ # melissa_data < - filter_by_coverage_across_cells (melissa_data# min_cell_cov_prcg = 0.5) # #——save_obj_filtered eval = FALSE -------------------------------------------- # saveRDS(文件= " melissa_data_obj_filtered。rds”,melissa_data ) ## ---- eval = FALSE -------------------------------------------------------------- # #================= # # 1。下载BAM数据# DATA_DIR="../编码/ wgbs /“# #下载GM12878细胞系# wget - p $ {DATA_DIR} GM12878 / https://www.encodeproject.org/files/ENCFF681ASN/@@download ENCFF681ASN.bam # # #下载H1-hESC细胞系wget - p $ {DATA_DIR} H1hESC / https://www.encodeproject.org/files/ENCFF546TLK/@@download ENCFF546TLK.bam # # - eval = FALSE -------------------------------------------------------------- # data_dir = "编码/ wgbs / GM12878 / SRR4235788。bam" # out_dir="encode/wgb /GM12878/subsampled/GM12878" # for ((i=1;I <= 40;++i)) # do # my_command="samtools view -s ${i}. "005 -b $data_dir > ${out_dir}_${i}.使用实例bam" # eval $my_command # done ## ---- eval=FALSE-------------------------------------------------------------- # data_dir="encode/wgbs/GM12878/subsampled/" # proc_dir="encode/wgbs/GM12878/processed/" # for (( i=1; i <= 40; ++i )) # do # my_command="bismark_methylation_extractor --ignore 2 --comprehensive --merge_non_CpG --no_header --multicore 4 -o $proc_dir --gzip --bedGraph ${data_dir}GM12878_${i}.bam" # eval $my_command # done ## ---- eval=FALSE-------------------------------------------------------------- # http://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeHaibMethylRrbs ## ---- eval=FALSE-------------------------------------------------------------- # bismark_genome_preparation hg19/ ## ---- eval=FALSE-------------------------------------------------------------- # #================= # # 3. Run bismark # bismark --genome hg19/ encode/wgEncodeHaibMethylRrbsGm12878HaibRawDataRep2.fastq.gz # bismark --genome hg19/ encode/wgEncodeHaibMethylRrbsH1hescHaibRawDataRep2.fastq.gz ## ----session_info, echo=TRUE, message=FALSE----------------------------------- sessionInfo()