splots: visualization of data from assays in microtitre plate or slide format
2021-01-04
Package
splots 1.68.0
Contents
The splots package was written in 2006, and it is still here to support legacy code and to not disrupt other packages that depend on it. It should not be used for new code development. The package provides a single function, plotScreen, for visualising data in microtitre plate or slide format. Please consider using the platetools package (see also https://github.com/Swarchal/platetools), or simply ggplot2, as exemplified below.
1 Example data
data("featuresPerWell", package = "HD2013SGI")This dataset contains measurements of a combinatorial RNAi screen on a human cell line (HCT116) with fluorescent microscopy as a phenotypic readout, as described in the documentation of the HD2013SGI package.
There are 168 plates, each with 384 wells. Within each well, the microscope took images at 4 locations, called field. Plate, row, column and field ID are given in the following dataframe.
str(featuresPerWell[[1]])## 'data.frame': 231840 obs. of 4 variables:
## $ plate: chr "001CIQ01IRI" "001CIQ01IRI" "001CIQ01IRI" "001CIQ01IRI" ...
## $ row : chr "B" "B" "B" "B" ...
## $ col : chr "1" "1" "1" "1" ...
## $ field: chr "1" "2" "3" "4" ...The actual measurements are in the following matrix, whose rows are aligned with the above dataframe. Its 353 columns are different morphometric measurements, averaged across the cells in that image.
str(featuresPerWell[[2]])## num [1:231840, 1:353] 2780 3120 2242 2603 2170 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:353] "count" "nuc.0.m.cx" "nuc.0.m.cy" "nuc.0.m.majoraxis" ...For the purpose of this demo, we only use the first 40 plates and the count measurement in field 1, i.e., the number of cells in that field—a proxy for the cells’ viability. We also convert the row and col variables into integers.
library("dplyr")
sgi = tibble(featuresPerWell[[1]], count = featuresPerWell[[2]][, "count"]) |>
filter(plate %in% unique(plate)[1:40],
field == "1") |>
mutate (col = as.integer(col),
row = match(row, LETTERS))
sgi## # A tibble: 13,800 × 5
## plate row col field count
## <chr> <int> <int> <chr> <dbl>
## 1 001CIQ01IRI 2 1 1 2780
## 2 001CIQ01IRI 2 2 1 2170
## 3 001CIQ01IRI 2 3 1 2548
## 4 001CIQ01IRI 2 4 1 2673
## 5 001CIQ01IRI 2 5 1 2486
## 6 001CIQ01IRI 2 6 1 2611
## 7 001CIQ01IRI 2 7 1 2305
## 8 001CIQ01IRI 2 8 1 2261
## 9 001CIQ01IRI 2 9 1 2724
## 10 001CIQ01IRI 2 10 1 4026
## # ℹ 13,790 more rows2 Using ggplot2
library("ggplot2")
ggplot(sgi, aes(x = col, y = row, fill = count)) + geom_raster() +
facet_wrap(vars(plate), ncol = 4) +
scale_fill_gradient(low = "white", high = "#00008B")
Figure 1: Visualization using ggplot
3 Using splots::plotScreen
The plotScreen takes as input the list xl, constructed below. As you can see, this is a lot more clumsy.
np = 40
nx = 24
ny = 16
plateNames = unique(featuresPerWell[[1]]$plate)
assertthat::assert_that(length(plateNames) >= np)
plateNames = plateNames[seq_len(np)]
xl = lapply(plateNames, function(pl) {
sel = with(featuresPerWell[[1]], plate == pl & field == "1")
rv = rep(NA_real_, nx * ny)
r = match(featuresPerWell[[1]]$row[sel], LETTERS)
c = match(featuresPerWell[[1]]$col[sel], paste(seq_len(nx)))
i = (r-1) * nx + c
assertthat::assert_that(!any(is.na(r)), !any(is.na(c)), !any(duplicated(i)),
all(r>=1), all(r<=ny), all(c>=1), all(c<=nx))
rv[i] = featuresPerWell[[2]][sel, "count"]
rv
})
names(xl) = plateNamesSo this the list xl:
length(xl)## [1] 40names(xl)[1:4]## [1] "001CIQ01IRI" "002CIQ01IIRI" "003CIIQ01IRI" "004CIIQ01IIRI"unique(vapply(xl, function(x) paste(class(x), length(x)), character(1)))## [1] "numeric 384"xl[[1]][1:30]## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [16] NA NA NA NA NA NA NA NA NA 2780 2170 2548 2673 2486 2611splots::plotScreen(xl, nx = nx, ny = ny, ncol = 4,
fill = c("white", "#00008B"),
main = "HD2013SGI", legend.label = "count",
zrange = c(0, max(unlist(xl), na.rm = TRUE)))## Warning: The function splots::plotScreen is obsolete, please use ggplot with
## geom_raster and facet_wrap instead, as described in the vignette of the splots
## package
Figure 2: Visualization using splots::plotScreen
sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.4.4 dplyr_1.1.3 BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.4 jsonlite_1.8.7 compiler_4.3.1
## [4] BiocManager_1.30.22 splots_1.68.0 Rcpp_1.0.11
## [7] tidyselect_1.2.0 magick_2.8.1 assertthat_0.2.1
## [10] jquerylib_0.1.4 scales_1.2.1 yaml_2.3.7
## [13] fastmap_1.1.1 R6_2.5.1 labeling_0.4.3
## [16] generics_0.1.3 knitr_1.44 tibble_3.2.1
## [19] bookdown_0.36 munsell_0.5.0 RColorBrewer_1.1-3
## [22] bslib_0.5.1 pillar_1.9.0 rlang_1.1.1
## [25] utf8_1.2.4 cachem_1.0.8 xfun_0.40
## [28] sass_0.4.7 cli_3.6.1 withr_2.5.1
## [31] magrittr_2.0.3 digest_0.6.33 grid_4.3.1
## [34] lifecycle_1.0.3 vctrs_0.6.4 evaluate_0.22
## [37] glue_1.6.2 farver_2.1.1 fansi_1.0.5
## [40] colorspace_2.1-0 rmarkdown_2.25 tools_4.3.1
## [43] pkgconfig_2.0.3 htmltools_0.5.6.1