occdn.limour.top/source/_posts/通过conda安装纯净环境的scenic.md

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title: 通过conda安装纯净环境的SCENIC tags: [] id: '1611' categories:

• - 生信
• • 转录因子 date: 2022-03-12 18:14:37 ---

• https://scenic.aertslab.org/tutorials/

• https://anaconda.org/conda-forge/r-base

• conda create -n scenic -c conda-forge r-base=4.1.2 -y

• conda activate scenic

• conda install -c conda-forge r-seurat=4.1.0 -y

• conda install -c conda-forge r-irkernel=1.3 -y

• conda install -c conda-forge r-biocmanager=1.30.16 -y

• conda install -c conda-forge r-devtools=2.4.3 -y

• conda install -c conda-forge --strict-channel-priority r-arrow=7.0.0 -y

• conda install -c bioconda bioconductor-aucell=1.16.0 -y

• conda install -c bioconda bioconductor-rcistarget -y

• conda install -c bioconda bioconductor-complexheatmap=2.10.0 -y

• conda install -c bioconda bioconductor-genie3=1.16.0 -y

• conda install -c bioconda bioconductor-biocparallel=1.28.3 -y

• conda install -c conda-forge r-doparallel=1.0.17 -y

• conda install -c conda-forge parallel=20220222 -y

• conda install -c conda-forge r-foreach=1.5.2 -y

• conda install -c maximinio r-scopeloomr=0.3.1 -y

• conda install -c conda-forge r-rbokeh=0.5.2 -y

• conda install -c conda-forge r-nmf=0.21.0 -y

• wget https://github.com/aertslab/SCENIC/archive/refs/heads/master.zip -O SCENIC-master.zip

• BiocManager::install(c("NMF", "R2HTML"))

• BiocManager::install(c("doMC", "doRNG"))

• devtools::install_local("SCENIC-master.zip")

• IRkernel::installspec(name='scenic', displayname='r-scenic')

  mkdir RcisTarget_data && cd RcisTarget_data
  mkdir human && cd human
  wget https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/gene_based/hg19-500bp-upstream-7species.mc9nr.feather
  wget https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/gene_based/hg19-tss-centered-10kb-7species.mc9nr.feather
  
  wget https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc9nr/gene_based/hg38__refseq-r80__10kb_up_and_down_tss.mc9nr.feather
  wget https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc9nr/gene_based/hg38__refseq-r80__500bp_up_and_100bp_down_tss.mc9nr.feather
  

总体来说，hg38相对于 hg19是一个巨大进步。by 员炊事

1. 改变了之前的一些测序错误，组装错误。直观的例子有 degenerate bases 少了很多。
2. 补上了hg19中的很多gap。 特别重要的是 centromere 的序列也不再是空白。hg19的时候，centromere 的部分直接就是gap， hg38后，开始有了 a-satellitle sequences。尽管只是“简单”的表现了 a-satellite repeats， 已经是一个巨大的提升。

3. sequence 改了之后，相应的 annotation 也改了很多。比较宏观的就是 hg38 的 exome 比hg19 的大了不少。如果我的记忆没出错的话，大了25% 左右。

   dbs <- list(`500bp`='hg38__refseq-r80__500bp_up_and_100bp_down_tss.mc9nr.feather', `10kb`='hg38__refseq-r80__10kb_up_and_down_tss.mc9nr.feather')
   scenicOptions <- initializeScenic(org="hgnc", dbDir="~/upload/zl_liu/RcisTarget_data/human/", nCores=10, dbs = dbs)
   

   library(foreach)
   library(parallel)
   library(doParallel)
   library(BiocParallel)