单细胞转录组数据分析||Seurat并行策略

你吃饭吃到一半，电话来了，你一直到吃完了以后才去接，这就说明你不支持并发也不支持并行。
你吃饭吃到一半，电话来了，你停了下来接了电话，接完后继续吃饭，这说明你支持并发。
你吃饭吃到一半，电话来了，你一边打电话一边吃饭，这说明你支持并行。

并发的关键是你有处理多个任务的能力，不一定要同时。
并行的关键是你有同时处理多个任务的能力。
所以我认为它们最关键的点就是：是否是『同时』。

作者：知乎用户
链接：https://www.zhihu.com/question/33515481/answer/58849148
来源：知乎
著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。

library(future)
# check the current active plan
plan()

sequential:
- args: function (expr, envir = parent.frame(), substitute = TRUE, lazy = FALSE, seed = NULL, globals = TRUE, local = TRUE, earlySignal = FALSE, label = NULL, ...)
- tweaked: FALSE
- call: NULL

# change the current plan to access parallelization
plan("multiprocess", workers = 4)
plan()

## multiprocess:
## - args: function (expr, envir = parent.frame(), substitute = TRUE, lazy = FALSE, seed = NULL, globals = TRUE, workers = 4, gc = FALSE, earlySignal = FALSE, label = NULL, ...)
## - tweaked: TRUE
## - call: plan("multiprocess", workers = 4)

NormalizeData
ScaleData
JackStraw
FindMarkers
FindIntegrationAnchors
FindClusters - if clustering over multiple resolutions

library(Seurat)
pbmc <- readRDS("../data/pbmc3k_final.rds")

# Enable parallelization
plan("multiprocess", workers = 4)
markers <- FindMarkers(pbmc, ident.1 = "NK", verbose = FALSE)

timing.comparisons <- data.frame(fxn = character(), time = numeric(), strategy = character())
plan("sequential")
start <- Sys.time()
pbmc <- ScaleData(pbmc, vars.to.regress = "percent.mt", verbose = FALSE)
end <- Sys.time()
timing.comparisons <- rbind(timing.comparisons, data.frame(fxn = "ScaleData", time = as.numeric(end - 
    start, units = "secs"), strategy = "sequential"))

start <- Sys.time()
markers <- FindMarkers(pbmc, ident.1 = "NK", verbose = FALSE)
end <- Sys.time()
timing.comparisons <- rbind(timing.comparisons, data.frame(fxn = "FindMarkers", time = as.numeric(end - 
    start, units = "secs"), strategy = "sequential"))

plan("multiprocess", workers = 4)
start <- Sys.time()
pbmc <- ScaleData(pbmc, vars.to.regress = "percent.mt", verbose = FALSE)
end <- Sys.time()
timing.comparisons <- rbind(timing.comparisons, data.frame(fxn = "ScaleData", time = as.numeric(end - 
    start, units = "secs"), strategy = "multiprocess"))

start <- Sys.time()
markers <- FindMarkers(pbmc, ident.1 = "NK", verbose = FALSE)
end <- Sys.time()
timing.comparisons <- rbind(timing.comparisons, data.frame(fxn = "FindMarkers", time = as.numeric(end - 
    start, units = "secs"), strategy = "multiprocess"))

library(ggplot2)
library(cowplot)
ggplot(timing.comparisons, aes(fxn, time)) + geom_bar(aes(fill = strategy), stat = "identity", position = "dodge") + 
    ylab("Time(s)") + xlab("Function") + theme_cowplot()

Error in getGlobalsAndPackages(expr, envir = envir, globals = TRUE) : 
  The total size of the X globals that need to be exported for the future expression ('FUN()') is X GiB. This exceeds the maximum allowed size of 500.00 MiB (option 'future.globals.maxSize'). The X largest globals are ... 

使用更少的线程进行并行；
如果你的电脑内存非常小，有一个简单的方法确定你的最大使用线程：max cores = memory.limit() / memory.size()；
将大量的并行分小部分进行；
在代码中多使用rm()删除没用的变量，使用gc()回收内存空间；