问题描述
我正在研究一个定义如下的 Drake 工作流程：
projectName <- c("lake_2018_CER_lib_norm_log2","lake_2018_CER_lib_norm","lake_2018_CER_raw_counts")
normalize <- c(TRUE,TRUE,FALSE)
logTransform <- c(TRUE,FALSE,FALSE)
normalize_fxn <- function(datExpr) {
datExpr <- sweep(datExpr,2,colSums(datExpr),FUN = "/")
return(datExpr)
}
plan <- drake_plan(
datExpr = target(fread(file_in(filePath),sep = "\t") %>% select(-1),transform = map(filePath = !!filePath,.id = FALSE)),datExprnorm = target(if(normalize == TRUE) {normalize_fxn(datExpr)*1e6 + 1} else {datExpr},transform = map(datExpr,normalize = !!normalize)),datExprLog = target(if(logTransform == TRUE) {log2(datExprnorm*1e6 + 1)} else {datExprnorm},transform = map(datExprnorm,logTransform = !!logTransform)),filterGenesMinCells = target(if(is.numeric(percentCells)) {round(ncol(datExprLog)*percentCells)} else {NULL},transform = cross(datExprLog,percentCells = !!percentCells)),makePlots = target(realVsPermCor(datExpr = datExprLog,projectName = projectName,featureType = featureType,nPerms = 100,subsampleReal = NULL,resampleReal = NULL,subsamplePerm,filterGenesMinCells = filterGenesMinCells,filterCellsMinGenes = NULL,fdrSubsample,futureThreads = NULL,openBlasThreads = 10,outDir),transform = cross(filterGenesMinCells,featureType = !!featureType,.id = c(featureType,percentCells)))
)
目标输出如下所示：
> plan$target
[1] "datExpr" "datExprLog_TRUE_datExprnorm_TRUE_datExpr"
[3] "datExprLog_FALSE_datExprnorm_TRUE_datExpr_2" "datExprLog_FALSE_datExprnorm_FALSE_datExpr"
[5] "datExprnorm_TRUE_datExpr" "datExprnorm_TRUE_datExpr_2"
[7] "datExprnorm_FALSE_datExpr" "filterGenesMinCells_NULL_datExprLog_TRUE_datExprnorm_TRUE_datExpr"
[9] "filterGenesMinCells_0.01_datExprLog_TRUE_datExprnorm_TRUE_datExpr" "filterGenesMinCells_0.02_datExprLog_TRUE_datExprnorm_TRUE_datExpr"
[11] "filterGenesMinCells_NULL_datExprLog_FALSE_datExprnorm_TRUE_datExpr_2" "filterGenesMinCells_0.01_datExprLog_FALSE_datExprnorm_TRUE_datExpr_2"
[13] "filterGenesMinCells_0.02_datExprLog_FALSE_datExprnorm_TRUE_datExpr_2" "filterGenesMinCells_NULL_datExprLog_FALSE_datExprnorm_FALSE_datExpr"
[15] "filterGenesMinCells_0.01_datExprLog_FALSE_datExprnorm_FALSE_datExpr" "filterGenesMinCells_0.02_datExprLog_FALSE_datExprnorm_FALSE_datExpr"
[17] "makePlots_gene_NULL" "makePlots_cell_NULL"
[19] "makePlots_gene_0.01" "makePlots_cell_0.01"
[21] "makePlots_gene_0.02" "makePlots_cell_0.02"
[23] "makePlots_gene_NULL_2" "makePlots_cell_NULL_2"
[25] "makePlots_gene_0.01_2" "makePlots_cell_0.01_2"
[27] "makePlots_gene_0.02_2" "makePlots_cell_0.02_2"
[29] "makePlots_gene_NULL_3" "makePlots_cell_NULL_3"
[31] "makePlots_gene_0.01_3" "makePlots_cell_0.01_3"
[33] "makePlots_gene_0.02_3" "makePlots_cell_0.02_3"
这非常接近我想要的，但我坚持的是 projectName：我想要三个项目名称之一用于最终目标，具体取决于输入是否产生在前面的步骤中，进行了标准化和/或对数转换。
目前，我生成了 18 个目标，因此我希望每个项目名称都映射到 6 个目标。
有什么办法可以做到这一点吗？
解决方法
似乎您可以编写一个函数来接受规范化和日志转换设置并输出项目名称。草图如下。
drake 中的静态分支很难。在 drake 的继任者 targets 中，我尝试使两种分支更容易。 （不过，在项目中期进行切换可能不可行。）
-
targets：https://github.com/ropensci/targets -
tarchetypes，带有targets静态分支的包：https://github.com/ropensci/tarchetypes - 用户手册：https://wlandau.github.io/targets-manual（即将转到 https://books.ropensci.org/targets）。
library(drake)
filePath <- "file_path.txt"
normalize <- c(TRUE,TRUE,FALSE)
logTransform <- c(TRUE,FALSE,FALSE)
percentCells <- "percent_cells"
featureType <- "feature_type"
normalize_fxn <- function(datExpr) {
datExpr <- sweep(datExpr,2,colSums(datExpr),FUN = "/")
return(datExpr)
}
name_project <- function(normalize,log_transform) {
switch(
paste0(normalize,"_",log_transform),TRUE_TRUE = "lake_2018_CER_lib_norm_log2",TRUE_FALSE = "lake_2018_CER_lib_norm",FALSE_FALSE = "lake_2018_CER_raw_counts"
)
}
plan <- drake_plan(
datExpr = target(fread(file_in(filePath),sep = "\t") %>% select(-1),transform = map(filePath = !!filePath,.id = FALSE)),datExprNorm = target(if(normalize == TRUE) {normalize_fxn(datExpr)*1e6 + 1} else {datExpr},transform = map(datExpr,normalize = !!normalize)),datExprLog = target(if(logTransform == TRUE) {log2(datExprNorm*1e6 + 1)} else {datExprNorm},transform = map(datExprNorm,logTransform = !!logTransform)),filterGenesMinCells = target(if(is.numeric(percentCells)) {round(ncol(datExprLog)*percentCells)} else {NULL},transform = cross(datExprLog,percentCells = !!percentCells)),makePlots = target(
realVsPermCor(
datExpr = datExprLog,# The project name is a function of normalization and log transform.
projectName = !!name_project(deparse(substitute(normalize)),deparse(substitute(logTransform))),featureType = featureType,nPerms = 100,subsampleReal = NULL,resampleReal = NULL,subsamplePerm,filterGenesMinCells = filterGenesMinCells,filterCellsMinGenes = NULL,fdrSubsample,futureThreads = NULL,openBlasThreads = 10,outDir
),transform = cross(filterGenesMinCells,featureType = !!featureType,.id = c(featureType,percentCells))
)
)
dplyr::filter(plan,grepl("makePlots",target))$command
#> [[1]]
#> realVsPermCor(datExpr = datExprLog_TRUE_datExprNorm_TRUE_datExpr,#> projectName = "lake_2018_CER_lib_norm_log2",featureType = "feature_type",#> nPerms = 100,#> subsamplePerm,filterGenesMinCells = filterGenesMinCells_percent_cells_datExprLog_TRUE_datExprNorm_TRUE_datExpr,#> filterCellsMinGenes = NULL,#> openBlasThreads = 10,outDir)
#>
#> [[2]]
#> realVsPermCor(datExpr = datExprLog_FALSE_datExprNorm_TRUE_datExpr_2,#> projectName = "lake_2018_CER_lib_norm",filterGenesMinCells = filterGenesMinCells_percent_cells_datExprLog_FALSE_datExprNorm_TRUE_datExpr_2,outDir)
#>
#> [[3]]
#> realVsPermCor(datExpr = datExprLog_FALSE_datExprNorm_FALSE_datExpr,#> projectName = "lake_2018_CER_raw_counts",filterGenesMinCells = filterGenesMinCells_percent_cells_datExprLog_FALSE_datExprNorm_FALSE_datExpr,outDir)
由 reprex package (v0.3.0) 于 2021 年 1 月 12 日创建