We create a toy data frame that maps tissues (BRAIN, STOMACH, HEART, T CELL, B CELL) to clustering (1-4)
df <- data.frame(
tissue = c(
"BRAIN", "BRAIN", "BRAIN",
"STOMACH", "STOMACH", "STOMACH", "STOMACH", "STOMACH", "STOMACH",
"HEART", "HEART", "HEART", "HEART", "HEART", "HEART", "HEART",
"T CELL", "T CELL",
"B CELL", "B CELL", "B CELL", "B CELL", "B CELL", "B CELL", "B CELL", "B CELL", "B CELL"
),
cluster = c(
1, 1, 2,
1, 2, 2, 2, 2, 2,
1, 3, 3, 3, 3, 3, 3,
4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4
)
)
graphing_columns <- c("tissue", "cluster")
# write.csv(df, file = "vignette_intro_df_ungrouped.csv", row.names = FALSE, quote = FALSE)
print(df)## tissue cluster
## 1 BRAIN 1
## 2 BRAIN 1
## 3 BRAIN 2
## 4 STOMACH 1
## 5 STOMACH 2
## 6 STOMACH 2
## 7 STOMACH 2
## 8 STOMACH 2
## 9 STOMACH 2
## 10 HEART 1
## 11 HEART 3
## 12 HEART 3
## 13 HEART 3
## 14 HEART 3
## 15 HEART 3
## 16 HEART 3
## 17 T CELL 4
## 18 T CELL 4
## 19 B CELL 4
## 20 B CELL 4
## 21 B CELL 4
## 22 B CELL 4
## 23 B CELL 4
## 24 B CELL 4
## 25 B CELL 4
## 26 B CELL 4
## 27 B CELL 4Group by tissue and cluster, counting the values in a new column value
clus_df_gather <- prep_for_lodes(df, cols = graphing_columns)
# write.csv(clus_df_gather, file = "vignette_intro_df_grouped.csv", row.names = FALSE, quote = FALSE)
print(clus_df_gather)## # A tibble: 8 × 3
## tissue cluster value
## <chr> <chr> <int>
## 1 B CELL 4 9
## 2 BRAIN 1 2
## 3 BRAIN 2 1
## 4 HEART 1 1
## 5 HEART 3 6
## 6 STOMACH 1 1
## 7 STOMACH 2 5
## 8 T CELL 4 2clus_df_gather_tsp <- sort_to_uncross(clus_df_gather, cols = graphing_columns, wt = "value", method = "tsp")
print(clus_df_gather_tsp)## # A tibble: 8 × 3
## tissue cluster value
## <fct> <fct> <int>
## 1 B CELL 4 9
## 2 BRAIN 1 2
## 3 BRAIN 2 1
## 4 HEART 1 1
## 5 HEART 3 6
## 6 STOMACH 1 1
## 7 STOMACH 2 5
## 8 T CELL 4 2# clus_df_gather_tsp <- clus_df_gather_tsp
stratum_to_color_mapping <- get_lode_clusters(clus_df_gather_tsp, cols = graphing_columns, wt = "value", method = "advanced")
print(stratum_to_color_mapping)## $cluster
## $cluster$`4`
## [1] 1
##
## $cluster$`1`
## [1] 2
##
## $cluster$`2`
## [1] 2
##
## $cluster$`3`
## [1] 3
##
##
## $tissue
## $tissue$`B CELL`
## [1] 1
##
## $tissue$BRAIN
## [1] 2
##
## $tissue$HEART
## [1] 3
##
## $tissue$STOMACH
## [1] 2
##
## $tissue$`T CELL`
## [1] 1crossing_edges_out <- compute_crossing_objective(clus_df_gather_tsp, cols = graphing_columns, wt = "value")
print(crossing_edges_out$output_objective)## [1] 1## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 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-r0.3.32.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=en_US.UTF-8
## [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] wompwomp_1.0.0 ggalluvial_0.12.6 ggplot2_4.0.3 dplyr_1.2.1
## [5] rmarkdown_2.32
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.10 utf8_1.2.6 generics_0.1.4 tidyr_1.3.2
## [5] stringi_1.8.9 digest_0.6.39 magrittr_2.0.5 evaluate_1.0.5
## [9] grid_4.6.1 RColorBrewer_1.1-3 iterators_1.0.14 fastmap_1.2.0
## [13] foreach_1.5.2 jsonlite_2.0.0 purrr_1.2.2 scales_1.4.0
## [17] tweenr_2.0.3 codetools_0.2-20 jquerylib_0.1.4 cli_3.6.6
## [21] rlang_1.3.0 polyclip_1.10-7 withr_3.0.3 cachem_1.1.0
## [25] yaml_2.3.12 otel_0.2.0 tools_4.6.1 buildtools_1.0.0
## [29] vctrs_0.7.3 TSP_1.2.7 R6_2.6.1 lifecycle_1.0.5
## [33] stringr_1.6.0 MASS_7.3-66 pkgconfig_2.0.3 pillar_1.11.1
## [37] bslib_0.12.0 gtable_0.3.6 glue_1.8.1 Rcpp_1.1.2
## [41] ggforce_0.5.0 xfun_0.62 tibble_3.3.1 tidyselect_1.2.1
## [45] sys_3.4.3 knitr_1.52 farver_2.1.2 htmltools_0.5.9
## [49] igraph_2.3.4 maketools_1.3.2 labeling_0.4.3 compiler_4.6.1
## [53] S7_0.2.2