Connecting dots and lines in degPlotCluster()

[CroixJeremy2]

Hello, Thanks for your amazing tool! I have a very mild issue regarding the degPlotCluster() function.

When I use the function with the option color = "cell_state" for example, the dots and the the boxplots are actually separated based on what I put in time = "treatment" for example, which is good because this is what I want (and what is expected). However, as you can see, the lines are not connected to the dots of the same color... They are not "dodged" whereas the dots and the boxplots are. Would that be possible to update the degPlotCluster() function in order to connect dots and lines please?

Thanks in advance for your response, Best regards,

Here are an example of how it looks on my dataset:

> sessionInfo()

R version 4.4.0 (2024-04-24) Platform: x86_64-apple-darwin20 Running under: macOS Monterey 12.7.6

Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0

locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Paris tzcode source: internal

attached base packages: [1] stats4 stats graphics grDevices utils datasets methods
[8] base

other attached packages: [1] scales_1.3.0 cowplot_1.1.3
[3] DEGreport_1.40.1 plyr_1.8.9
[5] htmlwidgets_1.6.4 plotly_4.10.4
[7] genefilter_1.86.0 ashr_2.2-63
[9] glmpca_0.2.0 PoiClaClu_1.0.2.1
[11] pheatmap_1.0.12 dplyr_1.1.4
[13] vsn_3.72.0 RColorBrewer_1.1-3
[15] BiocManager_1.30.23 patchwork_1.2.0
[17] ggplot2_3.5.1 DESeq2_1.44.0
[19] SummarizedExperiment_1.34.0 Biobase_2.64.0
[21] MatrixGenerics_1.16.0 matrixStats_1.3.0
[23] GenomicRanges_1.56.1 GenomeInfoDb_1.40.1
[25] IRanges_2.38.0 S4Vectors_0.42.0
[27] BiocGenerics_0.50.0

loaded via a namespace (and not attached): [1] ggdendro_0.2.0 jsonlite_1.8.8
[3] shape_1.4.6.1 magrittr_2.0.3
[5] farver_2.1.2 GlobalOptions_0.1.2
[7] zlibbioc_1.50.0 vctrs_0.6.5
[9] memoise_2.0.1 SQUAREM_2021.1
[11] mixsqp_0.3-54 htmltools_0.5.8.1
[13] S4Arrays_1.4.1 truncnorm_1.0-9
[15] broom_1.0.6 SparseArray_1.4.8
[17] cachem_1.1.0 lifecycle_1.0.4
[19] iterators_1.0.14 pkgconfig_2.0.3
[21] Matrix_1.7-0 R6_2.5.1
[23] fastmap_1.2.0 GenomeInfoDbData_1.2.12
[25] clue_0.3-65 digest_0.6.36
[27] colorspace_2.1-0 reshape_0.8.9
[29] AnnotationDbi_1.66.0 irlba_2.3.5.1
[31] RSQLite_2.3.7 labeling_0.4.3
[33] invgamma_1.1 fansi_1.0.6
[35] mgcv_1.9-1 httr_1.4.7
[37] abind_1.4-5 compiler_4.4.0
[39] bit64_4.0.5 withr_3.0.0
[41] doParallel_1.0.17 ConsensusClusterPlus_1.68.0 [43] backports_1.5.0 BiocParallel_1.38.0
[45] DBI_1.2.3 psych_2.4.3
[47] MASS_7.3-61 DelayedArray_0.30.1
[49] rjson_0.2.21 tools_4.4.0
[51] glue_1.7.0 nlme_3.1-165
[53] grid_4.4.0 cluster_2.1.6
[55] generics_0.1.3 gtable_0.3.5
[57] preprocessCore_1.66.0 tidyr_1.3.1
[59] data.table_1.15.4 utf8_1.2.4
[61] XVector_0.44.0 ggrepel_0.9.5
[63] foreach_1.5.2 pillar_1.9.0
[65] stringr_1.5.1 limma_3.60.3
[67] logging_0.10-108 circlize_0.4.16
[69] splines_4.4.0 lattice_0.22-6
[71] survival_3.7-0 bit_4.0.5
[73] annotate_1.82.0 tidyselect_1.2.1
[75] ComplexHeatmap_2.20.0 locfit_1.5-9.9
[77] Biostrings_2.72.1 knitr_1.47
[79] edgeR_4.2.0 xfun_0.45
[81] statmod_1.5.0 stringi_1.8.4
[83] UCSC.utils_1.0.0 lazyeval_0.2.2
[85] codetools_0.2-20 tibble_3.2.1
[87] cli_3.6.3 affyio_1.74.0
[89] xtable_1.8-4 munsell_0.5.1
[91] Rcpp_1.0.12 png_0.1-8
[93] XML_3.99-0.16.1 parallel_4.4.0
[95] blob_1.2.4 viridisLite_0.4.2
[97] affy_1.82.0 purrr_1.0.2
[99] crayon_1.5.3 GetoptLong_1.0.5
[101] rlang_1.1.4 KEGGREST_1.44.1
[103] mnormt_2.1.1