I am currently working on a CyTOF workflow in R using CATALYST. I have been using the plotExprHeatmap function to generate heatmap images, and I'm seeking guidance on how to modify the colors of the 'condition' bars, in my case, the ”type“ bar in these images.
Specifically, I aim to adjust the bar colors associated with different types displayed in the heatmap. Could you kindly provide insights or examples on how to implement this modification within the R code? Thank you very much for your time and assistance. I look forward to your guidance on this matter.
Dear Helena L. Crowell,
I am currently working on a CyTOF workflow in R using CATALYST. I have been using the plotExprHeatmap function to generate heatmap images, and I'm seeking guidance on how to modify the colors of the 'condition' bars, in my case, the ”type“ bar in these images.
Specifically, I aim to adjust the bar colors associated with different types displayed in the heatmap. Could you kindly provide insights or examples on how to implement this modification within the R code? Thank you very much for your time and assistance. I look forward to your guidance on this matter.
p <- plotExprHeatmap(sce, bin_anno = TRUE, row_anno = TRUE, row_clust = FALSE )
sessionInfo() R version 4.4.0 (2024-04-24 ucrt) Platform: x86_64-w64-mingw32/x64 Running under: Windows 11 x64 (build 22631) Matrix products: default locale: [1] LC_COLLATE=Chinese (Simplified)_China.utf8 LC_CTYPE=Chinese (Simplified)_China.utf8 LC_MONETARY=Chinese (Simplified)_China.utf8 [4] LC_NUMERIC=C LC_TIME=Chinese (Simplified)_China.utf8 time zone: Asia/Shanghai tzcode source: internal attached base packages: [1] grid stats4 stats graphics grDevices utils datasets methods base other attached packages: [1] cowplot_1.1.3 ggpubr_0.6.0 scater_1.32.0 scuttle_1.14.0 CATALYST_1.28.0 flowCore_2.16.0 [7] ComplexHeatmap_2.20.0 SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 Biobase_2.64.0 GenomicRanges_1.56.0 GenomeInfoDb_1.40.1 [13] IRanges_2.38.0 S4Vectors_0.42.0 BiocGenerics_0.50.0 MatrixGenerics_1.16.0 matrixStats_1.3.0 draw_1.0.0 [19] ggplot2_3.5.1 xlsx_0.6.5 readxl_1.4.3 loaded via a namespace (and not attached): [1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.8 shape_1.4.6.1 magrittr_2.0.3 TH.data_1.1-2 [7] ggbeeswarm_0.7.2 farver_2.1.2 GlobalOptions_0.1.2 zlibbioc_1.50.0 vctrs_0.6.5 Cairo_1.6-2 [13] DelayedMatrixStats_1.26.0 rstatix_0.7.2 S4Arrays_1.4.1 plotrix_3.8-4 BiocNeighbors_1.22.0 broom_1.0.6 [19] cellranger_1.1.0 SparseArray_1.4.5 plyr_1.8.9 sandwich_3.1-0 zoo_1.8-12 igraph_2.0.3 [25] lifecycle_1.0.4 iterators_1.0.14 pkgconfig_2.0.3 rsvd_1.0.5 Matrix_1.7-0 R6_2.5.1 [31] GenomeInfoDbData_1.2.12 clue_0.3-65 digest_0.6.35 colorspace_2.1-0 ggnewscale_0.4.10 irlba_2.3.5.1 [37] pkgload_1.3.4 beachmat_2.20.0 labeling_0.4.3 cytolib_2.16.0 fansi_1.0.6 colorRamps_2.3.4 [43] nnls_1.5 httr_1.4.7 polyclip_1.10-6 abind_1.4-5 compiler_4.4.0 withr_3.0.0 [49] doParallel_1.0.17 ConsensusClusterPlus_1.68.0 backports_1.5.0 BiocParallel_1.38.0 carData_3.0-5 viridis_0.6.5 [55] ggforce_0.4.2 ggsignif_0.6.4 MASS_7.3-60.2 drc_3.0-1 DelayedArray_0.30.1 rjson_0.2.21 [61] FlowSOM_2.12.0 gtools_3.9.5 tools_4.4.0 vipor_0.4.7 beeswarm_0.4.0 glue_1.7.0 [67] Rtsne_0.17 cluster_2.1.6 reshape2_1.4.4 generics_0.1.3 gtable_0.3.5 tidyr_1.3.1 [73] data.table_1.15.4 BiocSingular_1.20.0 ScaledMatrix_1.12.0 car_3.1-2 utf8_1.2.4 XVector_0.44.0 [79] ggrepel_0.9.5 foreach_1.5.2 pillar_1.9.0 stringr_1.5.1 rJava_1.0-11 circlize_0.4.16 [85] splines_4.4.0 dplyr_1.1.4 tweenr_2.0.3 lattice_0.22-6 survival_3.7-0 RProtoBufLib_2.16.0 [91] tidyselect_1.2.1 gridExtra_2.3 stringi_1.8.4 UCSC.utils_1.0.0 xlsxjars_0.6.1 codetools_0.2-20 [97] tibble_3.2.1 cli_3.6.2 munsell_0.5.1 Rcpp_1.0.12 png_0.1-8 XML_3.99-0.16.1 [103] parallel_4.4.0 sparseMatrixStats_1.16.0 viridisLite_0.4.2 mvtnorm_1.2-5 scales_1.3.0 ggridges_0.5.6 [109] purrr_1.0.2 crayon_1.5.2 GetoptLong_1.0.5 rlang_1.1.4 multcomp_1.4-25