GatingSets cannot have non-character columns in phenoData #375
Description
Describe the bug
When creating a GatingSet from a flowSet, or when attempting to modify an existing GatingSet, logical and numeric columns in phenoData are converted to character.
To Reproduce
## Make example data
library(cytoverse)
exprs <- matrix(seq(1, 6), ncol=2)
colnames(exprs) <- c("ch1", "ch2")
fs <- flowSet(flowFrame(exprs), flowFrame(exprs))
pData(fs)$is_test <- c(TRUE, FALSE)
pData(fs)$nums <- c(1, 2)
## Columns are logical/numeric when accessing via the flowSet
is(pData(fs)$is_test)
#> [1] "logical" "vector" "atomic" "index" "replValue"
#> [6] "numLike" "atomicVector" "vector_OR_Vector" "vector_OR_factor" "Uvector"
#> [11] "replValueSp"
is(pData(fs)$nums)
#> [1] "numeric" "vector" "atomic" "characterOrNumeric" "Cnumeric"
#> [6] "Unumeric" "index" "replValue" "numLike" "number"
#> [11] "atomicVector" "EnumerationValue" "vector_OR_Vector" "vector_OR_factor" "Uvector"
#> [16] "replValueSp"
## But not after creating GatingSet
gs <- GatingSet(fs)
is(pData(gs)$is_test)
#> [1] "character" "vector" "data.frameRowLabels"
#> [4] "SuperClassMethod" "character_OR_connection" "characterORMIAME"
#> [7] "character_OR_NULL" "atomic" "characterOrTransformation"
#> [10] "characterOrParameters" "characterOrNumeric" "Cnumeric"
#> [13] "Ufunction" "index" "atomicVector"
#> [16] "EnumerationValue" "vector_OR_Vector" "vector_OR_factor"
#> [19] "Uvector"
is(pData(gs)$nums)
#> [1] "character" "vector" "data.frameRowLabels"
#> [4] "SuperClassMethod" "character_OR_connection" "characterORMIAME"
#> [7] "character_OR_NULL" "atomic" "characterOrTransformation"
#> [10] "characterOrParameters" "characterOrNumeric" "Cnumeric"
#> [13] "Ufunction" "index" "atomicVector"
#> [16] "EnumerationValue" "vector_OR_Vector" "vector_OR_factor"
#> [19] "Uvector"
## Explicitly casting the column to the expected type results in the column being
## immediately converted back to character
pData(gs)$is_test <- as.logical(pData(gs)$is_test)
is(pData(gs)$is_test)
#> [1] "character" "vector" "data.frameRowLabels"
#> [4] "SuperClassMethod" "character_OR_connection" "characterORMIAME"
#> [7] "character_OR_NULL" "atomic" "characterOrTransformation"
#> [10] "characterOrParameters" "characterOrNumeric" "Cnumeric"
#> [13] "Ufunction" "index" "atomicVector"
#> [16] "EnumerationValue" "vector_OR_Vector" "vector_OR_factor"
#> [19] "Uvector" Expected behavior
I expected columns to maintain their original type when creating a GatingSet, and to be able to create non-character columns in an existing GatingSet.
SessionInfo:
R version 4.1.2 (2021-11-01)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.3
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] reprex_2.0.1 CytoML_2.6.0 openCyto_2.6.0 ggcyto_1.22.0
[5] ncdfFlow_2.40.0 BH_1.78.0-0 RcppArmadillo_0.10.8.1.0 ggplot2_3.3.5
[9] flowWorkspace_4.6.0 flowCore_2.6.0 cytoverse_0.0.0.9000
loaded via a namespace (and not attached):
[1] fs_1.5.2 bitops_1.0-7 matrixStats_0.61.0 RColorBrewer_1.1-2 httr_1.4.2
[6] R.cache_0.15.0 Rgraphviz_2.38.0 tools_4.1.2 utf8_1.2.2 R6_2.5.1
[11] KernSmooth_2.23-20 DBI_1.1.2 BiocGenerics_0.40.0 colorspace_2.0-2 withr_2.4.3
[16] tidyselect_1.1.1 gridExtra_2.3 mnormt_2.0.2 curl_4.3.2 compiler_4.1.2
[21] graph_1.72.0 cli_3.1.1 Biobase_2.54.0 flowClust_3.32.0 xml2_1.3.3
[26] flowStats_4.6.0 scales_1.1.1 DEoptimR_1.0-10 hexbin_1.28.2 mvtnorm_1.1-3
[31] robustbase_0.93-9 RBGL_1.70.0 digest_0.6.29 rainbow_3.6 R.utils_2.11.0
[36] rrcov_1.6-2 base64enc_0.1-3 jpeg_0.1-9 pkgconfig_2.0.3 styler_1.7.0
[41] rlang_1.0.1 rstudioapi_0.13 generics_0.1.2 jsonlite_1.7.3 gtools_3.9.2
[46] mclust_5.4.9 dplyr_1.0.7 R.oo_1.24.0 RCurl_1.98-1.5 magrittr_2.0.2
[51] RProtoBufLib_2.6.0 Matrix_1.3-4 Rcpp_1.0.8 munsell_0.5.0 S4Vectors_0.32.3
[56] fansi_1.0.2 clipr_0.7.1 lifecycle_1.0.1 R.methodsS3_1.8.1 yaml_2.2.2
[61] MASS_7.3-54 zlibbioc_1.40.0 plyr_1.8.6 grid_4.1.2 parallel_4.1.2
[66] crayon_1.4.2 lattice_0.20-45 splines_4.1.2 tmvnsim_1.0-2 knitr_1.37
[71] pillar_1.7.0 fda_5.5.1 corpcor_1.6.10 stats4_4.1.2 XML_3.99-0.8
[76] glue_1.6.1 latticeExtra_0.6-29 data.table_1.14.2 RcppParallel_5.1.5 deSolve_1.30
[81] png_0.1-7 vctrs_0.3.8 gtable_0.3.0 aws.s3_0.3.21 purrr_0.3.4
[86] clue_0.3-60 assertthat_0.2.1 ks_1.13.4 xfun_0.29 fds_1.8
[91] pracma_2.3.6 IDPmisc_1.1.20 pcaPP_1.9-74 tibble_3.1.6 cytolib_2.6.2
[96] aws.signature_0.6.0 flowViz_1.58.0 ellipse_0.4.2 cluster_2.1.2 ellipsis_0.3.2
[101] hdrcde_3.4 Additional context
Thanks for taking a look at this!