I am working on a Drake workflow that is defined as such:
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)))
)
The target output looks like this:
> 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"
This very close to what I want, but what I'm stuck on is the projectName: I want one of three project names to be used for the final target depending on whether the input, produced in earlier steps, were normalized and/or log transformed.
Currently, I produce 18 targets, so I want each project name to be mapped to 6 of the targets.
Is there some way I can accomplish this?
Seems like you could write a function to accept normalization and log transform settings and output the name of the project. Sketch below.
Static branching in drake is hard. In drake's successor, targets, I try to make both kinds of branching easier. (Might not be feasible to make the switch mid-project though.)
targets: https://github.com/ropensci/targetstarchetypes, the package with static branching for targets: https://github.com/ropensci/tarchetypeslibrary(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, subsampleReal = NULL, resampleReal = NULL,
#> subsamplePerm, filterGenesMinCells = filterGenesMinCells_percent_cells_datExprLog_TRUE_datExprNorm_TRUE_datExpr,
#> filterCellsMinGenes = NULL, fdrSubsample, futureThreads = NULL,
#> openBlasThreads = 10, outDir)
#>
#> [[2]]
#> realVsPermCor(datExpr = datExprLog_FALSE_datExprNorm_TRUE_datExpr_2,
#> projectName = "lake_2018_CER_lib_norm", featureType = "feature_type",
#> nPerms = 100, subsampleReal = NULL, resampleReal = NULL,
#> subsamplePerm, filterGenesMinCells = filterGenesMinCells_percent_cells_datExprLog_FALSE_datExprNorm_TRUE_datExpr_2,
#> filterCellsMinGenes = NULL, fdrSubsample, futureThreads = NULL,
#> openBlasThreads = 10, outDir)
#>
#> [[3]]
#> realVsPermCor(datExpr = datExprLog_FALSE_datExprNorm_FALSE_datExpr,
#> projectName = "lake_2018_CER_raw_counts", featureType = "feature_type",
#> nPerms = 100, subsampleReal = NULL, resampleReal = NULL,
#> subsamplePerm, filterGenesMinCells = filterGenesMinCells_percent_cells_datExprLog_FALSE_datExprNorm_FALSE_datExpr,
#> filterCellsMinGenes = NULL, fdrSubsample, futureThreads = NULL,
#> openBlasThreads = 10, outDir)
Created on 2021-01-12 by the reprex package (v0.3.0)