数据集选择

使用更新到 1.4.0 的 UCSCXenaTools https://github.com/ropensci/UCSCXenaTools。

library(UCSCXenaTools)
#> =========================================================================================
#> UCSCXenaTools version 1.4.0
#> Project URL: https://github.com/ropensci/UCSCXenaTools
#> Usages: https://cran.r-project.org/web/packages/UCSCXenaTools/vignettes/USCSXenaTools.html
#> 
#> If you use it in published research, please cite:
#> Wang et al., (2019). The UCSCXenaTools R package: a toolkit for accessing genomics data
#>   from UCSC Xena platform, from cancer multi-omics to single-cell RNA-seq.
#>   Journal of Open Source Software, 4(40), 1627, https://doi.org/10.21105/joss.01627
#> =========================================================================================
#>                               --Enjoy it--

看下数据类型：

table(XenaData$Type)
#> 
#> clinicalMatrix  genomicMatrix genomicSegment mutationVector 
#>            349            975            152            203

这里随机每种数据类型选 1 个数据集。

set.seed(1234)
set1 <- sample(which(XenaData$Type == "clinicalMatrix"), 1)
set.seed(1234)
set2 <- sample(which(XenaData$Type == "genomicMatrix"), 1)
set.seed(1234)
set3 <- sample(which(XenaData$Type == "genomicSegment"), 1)
set.seed(1234)
set4 <- sample(which(XenaData$Type == "mutationVector"), 1)

set1 <- XenaData[set1, ]
set2 <- XenaData[set2, ]
set3 <- XenaData[set3, ]
set4 <- XenaData[set4, ]

我们看看随机挑出来了哪些数据集，如果太大了就不下载了。。。

set1
#> # A tibble: 1 x 17
#>   XenaHosts XenaHostNames XenaCohorts XenaDatasets SampleCount DataSubtype Label
#>   <chr>     <chr>         <chr>       <chr>              <int> <chr>       <chr>
#> 1 https://… pcawgHub      PCAWG (spe… pcawg_speci…        3205 phenotype   tumo…
#> # … with 10 more variables: Type <chr>, AnatomicalOrigin <chr>,
#> #   SampleType <chr>, Tags <chr>, ProbeMap <chr>, LongTitle <chr>,
#> #   Citation <chr>, Version <chr>, Unit <chr>, Platform <chr>
set2
#> # A tibble: 1 x 17
#>   XenaHosts XenaHostNames XenaCohorts XenaDatasets SampleCount DataSubtype Label
#>   <chr>     <chr>         <chr>       <chr>              <int> <chr>       <chr>
#> 1 https://… tcgaHub       TCGA Pheoc… TCGA.PCPG.s…         187 DNA methyl… Meth…
#> # … with 10 more variables: Type <chr>, AnatomicalOrigin <chr>,
#> #   SampleType <chr>, Tags <chr>, ProbeMap <chr>, LongTitle <chr>,
#> #   Citation <chr>, Version <chr>, Unit <chr>, Platform <chr>
set3
#> # A tibble: 1 x 17
#>   XenaHosts XenaHostNames XenaCohorts XenaDatasets SampleCount DataSubtype Label
#>   <chr>     <chr>         <chr>       <chr>              <int> <chr>       <chr>
#> 1 https://… tcgaHub       TCGA Prost… TCGA.PRAD.s…         495 copy numbe… Befo…
#> # … with 10 more variables: Type <chr>, AnatomicalOrigin <chr>,
#> #   SampleType <chr>, Tags <chr>, ProbeMap <chr>, LongTitle <chr>,
#> #   Citation <chr>, Version <chr>, Unit <chr>, Platform <chr>
set4
#> # A tibble: 1 x 17
#>   XenaHosts XenaHostNames XenaCohorts XenaDatasets SampleCount DataSubtype Label
#>   <chr>     <chr>         <chr>       <chr>              <int> <chr>       <chr>
#> 1 https://… tcgaHub       TCGA Lung … mc3/LUSC_mc…         480 somatic mu… MC3 …
#> # … with 10 more variables: Type <chr>, AnatomicalOrigin <chr>,
#> #   SampleType <chr>, Tags <chr>, ProbeMap <chr>, LongTitle <chr>,
#> #   Citation <chr>, Version <chr>, Unit <chr>, Platform <chr>

set2 的数据集比较大，我们去掉它。有兴趣的可以单独测试。

下载测试

library(microbenchmark)

先编写一个下载函数：

download_test <- function(x, use_hiplot = FALSE) {
  if (use_hiplot) {
    options(use_hiplot = TRUE)
    on.exit(options(use_hiplot = FALSE))
  } else {
    options(use_hiplot = FALSE)
  }
  x %>%
    XenaGenerate() %>%
    XenaQuery() %>%
    XenaDownload(force = TRUE)
}

我们下载看一看，下载重复 5 次，看下平均水平：

options(use_hiplot = TRUE)
download_stats <- microbenchmark(
  set1_no_hiplot = download_test(set1),
  set1_by_hiplot = download_test(set1, use_hiplot = TRUE),
  set3_no_hiplot = download_test(set3),
  set3_by_hiplot = download_test(set3, use_hiplot = TRUE),
  set4_no_hiplot = download_test(set4),
  set4_by_hiplot = download_test(set4, use_hiplot = TRUE),
  times = 5
)

结果可视化

print(ggplot2::autoplot(download_stats))
#> Coordinate system already present. Adding new coordinate system, which will replace the existing one.

数据值：

print(download_stats)
#> Unit: milliseconds
#>            expr  min    lq  mean median    uq   max neval cld
#>  set1_no_hiplot 3537  4523  4780   4708  5406  5729     5  a 
#>  set1_by_hiplot  472   495   584    506   604   842     5  a 
#>  set3_no_hiplot 9478 10504 12471  11354 11789 19229     5   b
#>  set3_by_hiplot 2703  2744  2967   2857  3053  3478     5  a 
#>  set4_no_hiplot 9998 10469 13398  12136 15663 18725     5   b
#>  set4_by_hiplot 3453  3479  3526   3490  3491  3716     5  a

本次测试通过位于上海的学校网络完成。由于网络、地理坐标的原因，读者使用的实际下载速度可能有所不同。