AI代码解释 ### Initialize settingslibrary(SCENIC)db='cisTarget_databases/'list.files(db)# 保证cisTarget_databases 文件夹下面有下载好2个1G的文件 scenicOptions<-initializeScenic(org="hgnc",dbDir=db,nCores=4)saveRDS(scenicOpti
mode="r")exprMat<-get_dgem(loom)cellInfo<-get_cellAnnotation(loom)close_loom(loom)### Initialize settings 初始设置,导入评分数据库library(SCENIC)scenicOptions<-initializeScenic(org="mgi",dbDir="cisTarget_databases",nCores=10)# scenicOptions@inputDatasetInfo$cellInfo...
library(SCENIC) db='cisTarget_databases/' list.files(db) # 保证cisTarget_databases 文件夹下面有下载好2个1G的文件 scenicOptions <- initializeScenic(org="hgnc", dbDir=db , nCores=4) saveRDS(scenicOptions, file="int/scenicOptions.Rds") saveRDS(cellInfo, file="int/cellInfo.Rds") 可以看到前面...
//resources.aertslab.org/cistarget/databases/mus_musculus/mm10/refseq_r80/mc_v10_clust/gene_based/mm10_500bp_up_100bp_down_full_tx_clustered.genes_vs_motifs.rankings.feather https://resources.aertslab.org/cistarget/databases/mus_musculus/mm10/refseq_r80/mc_v10_clust/gene_based/mm10_500...
db='cisTarget_databases/'list.files(db)# 保证cisTarget_databases 文件夹下面有下载好2个1G的文件scenicOptions <- initializeScenic(org="hgnc", dbDir=db , nCores=4) saveRDS(scenicOptions, file="int/scenicOptions.Rds") saveRDS(cellInfo, file="int/cellInfo.Rds") ...
保证cisTarget_databases 文件夹下面有下载好2个1G的文件 scenic <- initializeScenic(org="hgnc", db=db , nCores=4) saveRDSscenicOptions, file="int/scenicOptions.Rds") saveRDScellInfo, file="int/cellInfo.Rds") ## Co-expression network genes <- geneFiltering(exprMat, scenicOptions) ...
除了必要的R包之外,需要下载RcisTarget的物种特定数据库(https://resources.aertslab.org/cistarget/;主题排名)。默认情况下,SCENIC使用在基因启动子(TSS上游500 bp)和TSS周围20 kb (+/- 10kb)中对模序进行评分的数据库。 Forhuman: dbFiles <- c("https://resources.aertslab.org/cistarget/databases/homo_...
In fact, I have manually downloaded the two files and put them in the cisTarget_databases. Could you please suggest what could be wrong? Thanks Sign up for freeto join this conversation on GitHub.Already have an account?Sign in to comment ...
For this, a custom score and ranking database was generated using create_cisTarget_databases Python package using the DNA sequences of consensus peaks and all annotated motifs as input. Motif enrichment was performed using both the cisTarget and DEM algorithm on cell-type-based DARs (logFC ...
不同物种不一样, 在https://resources.aertslab.org/cistarget/查看自己的物种,按需下载,因为示例数据是小鼠表达矩阵,所以这里我们下载小鼠的,并且构建了一个 cisTarget_databases 文件夹 来存放下载好的数据库文件。 ### Initialize settingslibrary(SCENIC)# 保证 cisTarget_databases 文件夹下面有下载好2个1G的文...