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 rows

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")

Visualization using ggplot

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) = plateNames

So this the list xl:

length(xl)

## [1] 40

names(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 2611

splots::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 in splots::plotScreen(xl, nx = nx, ny = ny, ncol = 4, fill = c("white",
## : 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

Visualization using splots::plotScreen

sessionInfo()

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        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: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggplot2_3.5.1    dplyr_1.1.4      BiocStyle_2.35.0
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6        jsonlite_1.8.9      highr_0.11         
##  [4] compiler_4.4.1      BiocManager_1.30.25 splots_1.73.0      
##  [7] tidyselect_1.2.1    assertthat_0.2.1    jquerylib_0.1.4    
## [10] scales_1.3.0        yaml_2.3.10         fastmap_1.2.0      
## [13] R6_2.5.1            labeling_0.4.3      generics_0.1.3     
## [16] knitr_1.48          tibble_3.2.1        maketools_1.3.1    
## [19] munsell_0.5.1       RColorBrewer_1.1-3  bslib_0.8.0        
## [22] pillar_1.9.0        rlang_1.1.4         utf8_1.2.4         
## [25] cachem_1.1.0        xfun_0.48           sass_0.4.9         
## [28] sys_3.4.3           cli_3.6.3           withr_3.0.2        
## [31] magrittr_2.0.3      digest_0.6.37       grid_4.4.1         
## [34] lifecycle_1.0.4     vctrs_0.6.5         evaluate_1.0.1     
## [37] glue_1.8.0          farver_2.1.2        buildtools_1.0.0   
## [40] colorspace_2.1-1    fansi_1.0.6         rmarkdown_2.28     
## [43] tools_4.4.1         pkgconfig_2.0.3     htmltools_0.5.8.1