Hello, with the SCE implementation, it no longer works and you get this error:
Warning: 7 parallel jobs did not deliver resultsWarning: 1 parallel job did not deliver a resultError in reducer$value.cache[[as.character(idx)]] <- values :
wrong args for environment subassignment
url.base <- "https://github.com/goncalves-lab/waddR-data/blob/master/data/"
sce.blood.url <- paste0(url.base, "data_blood.rda?raw=true")
sce.decidua.url <- paste0(url.base, "data_decidua.rda?raw=true")
getCachedPath <- function(url, rname){
bfc <- BiocFileCache() # fire up cache
res <- bfcquery(bfc, url, field="fpath", exact=TRUE)
if (bfccount(res) == 0L)
cachedFilePath <- bfcadd(bfc, rname=rname, fpath=url)
else
cachedFilePath <- bfcpath(bfc, res[["rid"]])
cachedFilePath
}
load(getCachedPath(sce.blood.url, "data_blood"))
load(getCachedPath(sce.decidua.url, "data_decidua"))
set.seed(28)
randgenes <- sample(rownames(data_blood), 1000, replace=FALSE)
sce.blood <- data_blood[randgenes, ]
sce.decidua <- data_decidua[randgenes, ]
res <- wasserstein.sc(sce.blood, sce.decidua, method="TS",permnum=1000,seed=24)
head(res, n=10L)
Hello, with the SCE implementation, it no longer works and you get this error:
Warning: 7 parallel jobs did not deliver resultsWarning: 1 parallel job did not deliver a resultError in reducer$value.cache[[as.character(idx)]] <- values :
wrong args for environment subassignment
url.base <- "https://github.com/goncalves-lab/waddR-data/blob/master/data/"
sce.blood.url <- paste0(url.base, "data_blood.rda?raw=true")
sce.decidua.url <- paste0(url.base, "data_decidua.rda?raw=true")
getCachedPath <- function(url, rname){
bfc <- BiocFileCache() # fire up cache
res <- bfcquery(bfc, url, field="fpath", exact=TRUE)
if (bfccount(res) == 0L)
cachedFilePath <- bfcadd(bfc, rname=rname, fpath=url)
else
cachedFilePath <- bfcpath(bfc, res[["rid"]])
cachedFilePath
}
load(getCachedPath(sce.blood.url, "data_blood"))
load(getCachedPath(sce.decidua.url, "data_decidua"))
set.seed(28)
randgenes <- sample(rownames(data_blood), 1000, replace=FALSE)
sce.blood <- data_blood[randgenes, ]
sce.decidua <- data_decidua[randgenes, ]
res <- wasserstein.sc(sce.blood, sce.decidua, method="TS",permnum=1000,seed=24)
head(res, n=10L)