library(tidytof)
library(dplyr)
library(ggplot2)
count <- dplyr::count
Often, high-dimensional cytometry experiments collect tens or hundreds or millions of cells in total, and it can be useful to downsample to a smaller, more computationally tractable number of cells - either for a final analysis or while developing code.
To do this, {tidytof} implements the tof_downsample() verb, which allows downsampling using 3 methods: downsampling to an integer number of cells, downsampling to a fixed proportion of the total number of input cells, or downsampling to a fixed cellular density in phenotypic space.
Downsampling with tof_downsample()
Using {tidytof}’s built-in dataset phenograph_data, we can see that the original size of the dataset is 1000 cells per cluster, or 3000 cells in total:
data(phenograph_data)
phenograph_data |>
dplyr::count(phenograph_cluster)
#> # A tibble: 3 × 2
#> phenograph_cluster n
#> <chr> <int>
#> 1 cluster1 1000
#> 2 cluster2 1000
#> 3 cluster3 1000
To randomly sample 200 cells per cluster, we can use tof_downsample() using the “constant” method:
phenograph_data |>
# downsample
tof_downsample(
group_cols = phenograph_cluster,
method = "constant",
num_cells = 200
) |>
# count the number of downsampled cells in each cluster
count(phenograph_cluster)
#> # A tibble: 3 × 2
#> phenograph_cluster n
#> <chr> <int>
#> 1 cluster1 200
#> 2 cluster2 200
#> 3 cluster3 200
Alternatively, if we wanted to sample 50% of the cells in each cluster, we could use the “prop” method:
phenograph_data |>
# downsample
tof_downsample(
group_cols = phenograph_cluster,
method = "prop",
prop_cells = 0.5
) |>
# count the number of downsampled cells in each cluster
count(phenograph_cluster)
#> # A tibble: 3 × 2
#> phenograph_cluster n
#> <chr> <int>
#> 1 cluster1 500
#> 2 cluster2 500
#> 3 cluster3 500
And finally, we might also be interested in taking a slightly different approach to downsampling that reduces the number of cells not to a fixed constant or proportion, but to a fixed density in phenotypic space. For example, the following scatterplot demonstrates that there are certain areas of phenotypic density in phenograph_data that contain more cells than others along the cd34/cd38 axes:
rescale_max <-
function(x, to = c(0, 1), from = range(x, na.rm = TRUE)) {
x / from[2] * to[2]
}
phenograph_data |>
# preprocess all numeric columns in the dataset
tof_preprocess(undo_noise = FALSE) |>
# plot
ggplot(aes(x = cd34, y = cd38)) +
geom_hex() +
coord_fixed(ratio = 0.4) +
scale_x_continuous(limits = c(NA, 1.5)) +
scale_y_continuous(limits = c(NA, 4)) +
scale_fill_viridis_c(
labels = function(x) round(rescale_max(x), 2)
) +
labs(
fill = "relative density"
)
To reduce the number of cells in our dataset until the local density around each cell in our dataset is relatively constant, we can use the “density” method of tof_downsample:
phenograph_data |>
tof_preprocess(undo_noise = FALSE) |>
tof_downsample(method = "density", density_cols = c(cd34, cd38)) |>
# plot
ggplot(aes(x = cd34, y = cd38)) +
geom_hex() +
coord_fixed(ratio = 0.4) +
scale_x_continuous(limits = c(NA, 1.5)) +
scale_y_continuous(limits = c(NA, 4)) +
scale_fill_viridis_c(
labels = function(x) round(rescale_max(x), 2)
) +
labs(
fill = "relative density"
)
Thus, we can see that the density after downsampling is more uniform (though not exactly uniform) across the range of cd34/cd38 values in phenograph_data.
Additional documentation
For more details, check out the documentation for the 3 underlying members of the tof_downsample_* function family (which are wrapped by tof_downsample):
tof_downsample_constanttof_downsample_proptof_downsample_density
Session info
sessionInfo()
#> R version 4.5.0 RC (2025-04-04 r88126)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
#>
#> 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] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] tidyr_1.3.1 stringr_1.5.1
#> [3] HDCytoData_1.27.0 flowCore_2.20.0
#> [5] SummarizedExperiment_1.38.0 Biobase_2.68.0
#> [7] GenomicRanges_1.60.0 GenomeInfoDb_1.44.0
#> [9] IRanges_2.42.0 S4Vectors_0.46.0
#> [11] MatrixGenerics_1.20.0 matrixStats_1.5.0
#> [13] ExperimentHub_2.16.0 AnnotationHub_3.16.0
#> [15] BiocFileCache_2.16.0 dbplyr_2.5.0
#> [17] BiocGenerics_0.54.0 generics_0.1.3
#> [19] forcats_1.0.0 ggplot2_3.5.2
#> [21] dplyr_1.1.4 tidytof_1.2.0
#>
#> loaded via a namespace (and not attached):
#> [1] jsonlite_2.0.0 shape_1.4.6.1 magrittr_2.0.3
#> [4] farver_2.1.2 rmarkdown_2.29 vctrs_0.6.5
#> [7] memoise_2.0.1 sparsevctrs_0.3.3 htmltools_0.5.8.1
#> [10] S4Arrays_1.8.0 curl_6.2.2 SparseArray_1.8.0
#> [13] sass_0.4.10 parallelly_1.43.0 bslib_0.9.0
#> [16] lubridate_1.9.4 cachem_1.1.0 commonmark_1.9.5
#> [19] igraph_2.1.4 mime_0.13 lifecycle_1.0.4
#> [22] iterators_1.0.14 pkgconfig_2.0.3 Matrix_1.7-3
#> [25] R6_2.6.1 fastmap_1.2.0 GenomeInfoDbData_1.2.14
#> [28] future_1.40.0 digest_0.6.37 colorspace_2.1-1
#> [31] AnnotationDbi_1.70.0 irlba_2.3.5.1 RSQLite_2.3.9
#> [34] labeling_0.4.3 filelock_1.0.3 cytolib_2.20.0
#> [37] yardstick_1.3.2 timechange_0.3.0 httr_1.4.7
#> [40] polyclip_1.10-7 abind_1.4-8 compiler_4.5.0
#> [43] bit64_4.6.0-1 withr_3.0.2 doParallel_1.0.17
#> [46] viridis_0.6.5 DBI_1.2.3 hexbin_1.28.5
#> [49] ggforce_0.4.2 MASS_7.3-65 lava_1.8.1
#> [52] embed_1.1.5 rappdirs_0.3.3 DelayedArray_0.34.0
#> [55] tools_4.5.0 future.apply_1.11.3 nnet_7.3-20
#> [58] glue_1.8.0 grid_4.5.0 Rtsne_0.17
#> [61] recipes_1.2.1 gtable_0.3.6 tzdb_0.5.0
#> [64] class_7.3-23 data.table_1.17.0 hms_1.1.3
#> [67] utf8_1.2.4 tidygraph_1.3.1 XVector_0.48.0
#> [70] RcppAnnoy_0.0.22 markdown_2.0 ggrepel_0.9.6
#> [73] BiocVersion_3.21.1 foreach_1.5.2 pillar_1.10.2
#> [76] RcppHNSW_0.6.0 splines_4.5.0 tweenr_2.0.3
#> [79] lattice_0.22-7 survival_3.8-3 bit_4.6.0
#> [82] RProtoBufLib_2.20.0 tidyselect_1.2.1 Biostrings_2.76.0
#> [85] knitr_1.50 gridExtra_2.3 litedown_0.7
#> [88] xfun_0.52 graphlayouts_1.2.2 hardhat_1.4.1
#> [91] timeDate_4041.110 stringi_1.8.7 UCSC.utils_1.4.0
#> [94] yaml_2.3.10 evaluate_1.0.3 codetools_0.2-20
#> [97] ggraph_2.2.1 tibble_3.2.1 BiocManager_1.30.25
#> [100] cli_3.6.4 uwot_0.2.3 rpart_4.1.24
#> [103] munsell_0.5.1 jquerylib_0.1.4 Rcpp_1.0.14
#> [106] globals_0.16.3 png_0.1-8 parallel_4.5.0
#> [109] gower_1.0.2 readr_2.1.5 blob_1.2.4
#> [112] listenv_0.9.1 glmnet_4.1-8 viridisLite_0.4.2
#> [115] ipred_0.9-15 ggridges_0.5.6 scales_1.3.0
#> [118] prodlim_2024.06.25 purrr_1.0.4 crayon_1.5.3
#> [121] rlang_1.1.6 KEGGREST_1.48.0