From 6e9a6114dad74ae5686f63161666f7bec85bf86e Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Thu, 12 Dec 2024 12:55:37 -0600 Subject: [PATCH] rendered notebook --- .../01-reference-exploration.html | 3015 +++++++++++++++++ 1 file changed, 3015 insertions(+) create mode 100644 analyses/cell-type-consensus/exploratory-notebooks/01-reference-exploration.html diff --git a/analyses/cell-type-consensus/exploratory-notebooks/01-reference-exploration.html b/analyses/cell-type-consensus/exploratory-notebooks/01-reference-exploration.html new file mode 100644 index 000000000..474052d82 --- /dev/null +++ b/analyses/cell-type-consensus/exploratory-notebooks/01-reference-exploration.html @@ -0,0 +1,3015 @@ + + + + + + + + + + + + + + + +Summary of cell type ontologies in reference files + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + +

This notebook aims to identify a set of consensus labels between cell +types in the PanglaoDB and Blueprint Encode references.

+ +
+

Setup

+
suppressPackageStartupMessages({
+  # load required packages
+  library(ggplot2)
+})
+
+# Set default ggplot theme
+theme_set(
+  theme_bw()
+)
+
# The base path for the OpenScPCA repository, found by its (hidden) .git directory
+repository_base <- rprojroot::find_root(rprojroot::is_git_root)
+
+# The path to this module
+ref_dir <- file.path(repository_base, "analyses", "cell-type-consensus", "references")
+
+# path to ref file for panglao 
+panglao_file <- file.path(ref_dir, "panglao-cell-type-ontologies.tsv")
+
# grab obo file 
+cl_ont <- ontologyIndex::get_ontology("http://purl.obolibrary.org/obo/cl-basic.obo")
+
+# read in panglao file 
+panglao_df <- readr::read_tsv(panglao_file) |>
+  # rename columns to have panglao in them for easy joining later 
+  dplyr::select(
+    panglao_ontology = "ontology_id",
+    panglao_annotation = "human_readable_value"
+  )
+
## Rows: 178 Columns: 3
+## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
+## Delimiter: "\t"
+## chr (3): ontology_id, human_readable_value, panglao_cell_type
+## 
+## ℹ Use `spec()` to retrieve the full column specification for this data.
+## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
+
# grab singler ref from celldex
+blueprint_ref <- celldex::BlueprintEncodeData() 
+# get ontologies and human readable name into data frame
+blueprint_df <- data.frame(
+  blueprint_ontology = blueprint_ref$label.ont,
+  blueprint_annotation_main = blueprint_ref$label.main,
+  blueprint_annotation_fine = blueprint_ref$label.fine
+) |> 
+  unique()
+
+
+

Full cell ontology

+

Below I will calculate the total number of ancestors and the total +number of descendants for each term in the full cell type ontology and +then show the distributions for those statistics. This will give us an +idea of the range of values we expect to see when looking at the +PanglaoDB and Blueprint Encode references.

+
# turn cl_ont into data frame with one row per term 
+cl_df <- data.frame(
+  cl_ontology = cl_ont$id,
+  cl_annotation = cl_ont$name
+) |> 
+  dplyr::rowwise() |> 
+  dplyr::mutate(
+    # list all ancestors and descendants calculate total
+    ancestors = list(ontologyIndex::get_ancestors(cl_ont, cl_ontology)),
+    total_ancestors = length(ancestors),
+    descendants = list(ontologyIndex::get_descendants(cl_ont, cl_ontology)),
+    total_descendants = length(descendants)
+  )
+

The vertical lines in the below plot indicate the value for cell +types of varying granularity.

+
celltypes_of_interest <- c("eukaryotic cell", "lymphocyte", "leukocyte", "hematopoietic cell", "T cell", "endothelial cell", "smooth muscle cell", "memory T cell")
+line_df <- cl_df |> 
+  dplyr::filter(cl_annotation %in% celltypes_of_interest) |> 
+  dplyr::select(cl_annotation, total_descendants, total_ancestors) |> 
+  unique()
+
+# group any labels that have the same number of ancestors 
+ancestor_labels_df <- line_df |> 
+  dplyr::group_by(total_ancestors) |> 
+  dplyr::summarise(cl_annotation = paste(cl_annotation, collapse = ","))
+
# make density plots showing distribution of ancestors and descendants 
+ggplot(cl_df, aes(x = total_ancestors)) +
+  geom_density(fill = "#00274C", alpha = 0.5) +
+  geom_vline(data = ancestor_labels_df,
+             mapping = aes(xintercept = total_ancestors), 
+             lty = 2) +
+  geom_text(
+    data = ancestor_labels_df,
+    mapping = aes(x = total_ancestors, y = 0.04, label = cl_annotation),
+    angle = 90, 
+    vjust = -0.5
+  ) +
+  labs(
+    x = "Number of ancestors",
+    y = "Density"
+  )
+

+

Generally it looks like as the cell types get more specific we see a +greater number of ancestors. However, the range of values is small and +we see some cell types have the same value and probably not the same +level of granularity.

+

Below we will look at total number of descendants.

+
ggplot(cl_df, aes(x = total_descendants)) +
+  geom_density(fill = "#FFCB05", alpha = 0.5) +
+  geom_vline(data = line_df,
+             mapping = aes(xintercept = total_descendants), 
+             lty = 2) +
+  geom_text(
+    data = line_df,
+    mapping = aes(x = total_descendants, y = 0.6, label = cl_annotation),
+    angle = 90, 
+    vjust = -0.5
+  ) +
+  labs(
+    x = "Number of descendants",
+    y = "Density"
+  )
+

+

It looks like most cell types have very few descendants, so let’s +zoom into the area below 500 to get a better look.

+
ggplot(cl_df, aes(x = total_descendants)) +
+  geom_density(fill = "#FFCB05", alpha = 0.5) +
+  geom_vline(data = line_df,
+             mapping = aes(xintercept = total_descendants), 
+             lty = 2) +
+  geom_text(
+    data = line_df,
+    mapping = aes(x = total_descendants, y = 0.6, label = cl_annotation),
+    angle = 90, 
+    vjust = -0.5
+  ) +
+  labs(
+    x = "Number of descendants",
+    y = "Density"
+  ) +
+  xlim(c(0,500))
+
## Warning: Removed 14 rows containing non-finite outside the scale range (`stat_density()`).
+
## Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_vline()`).
+
## Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
+

+

Here we see a much larger range of values and that cell types become +more general as the number of descendants goes up. However, this +distribution alone is probably not helpful in determining a cutoff. The +next section we will look at this distribution specifically for cell +types present in our references, PanglaoDB and Blueprint encode.

+
+
+

Latest common ancestor (LCA) between PanglaoDB and Blueprint +encode

+

This section will look at identifying the latest common ancestor +(LCA) between all possible combinations of terms from PanglaoDB (used +for assigning cell types with CellAssign) and the +BlueprintEncodeData reference from celldex +(used for assigning cell types with SingleR). The LCA +refers to the latest term in the cell ontology heirarchy that is common +between two terms. I will use the ontoProc::findCommonAncestors() +function to get the LCA for each combination.

+

Note that it is possible to have more than one LCA for a set of +terms. To start, I will keep all LCA terms found.

+

For each LCA, I will again look at the total number of ancestors and +descendants and see if I can identify an appropriate cutoff. Ultimately, +I would like to see if we can use that cutoff to decide if we should +keep the LCA term as the consensus label or use “Unknown”.

+
# first set up the graph from cl ont
+parent_terms <- cl$parents
+cl_graph <- igraph::make_graph(rbind(unlist(parent_terms), rep(names(parent_terms), lengths(parent_terms))))
+
# get a data frame with all combinations of panglao and blueprint terms
+# one row for each combination 
+all_ref_df <- expand.grid(panglao_df$panglao_ontology,
+                          blueprint_df$blueprint_ontology) |> 
+  dplyr::rename(
+    panglao_ontology = "Var1",
+    blueprint_ontology = "Var2"
+  ) |> 
+  # add in the human readable values for each ontology term
+  dplyr::left_join(blueprint_df, by = "blueprint_ontology") |> 
+  dplyr::left_join(panglao_df, by = "panglao_ontology") |> 
+  tidyr::drop_na() |> 
+  dplyr::rowwise() |> 
+  dplyr::mutate(
+    # least common shared ancestor
+    lca = list(rownames(ontoProc::findCommonAncestors(blueprint_ontology, panglao_ontology, g = g)))
+  )
+
## Warning in dplyr::left_join(dplyr::left_join(dplyr::rename(expand.grid(panglao_df$panglao_ontology, : Detected an unexpected many-to-many relationship between `x` and `y`.
+## ℹ Row 49 of `x` matches multiple rows in `y`.
+## ℹ Row 99 of `y` matches multiple rows in `x`.
+## ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.
+
## Warning: There were 23859 warnings in `dplyr::mutate()`.
+## The first warning was:
+## ℹ In argument: `lca = list(...)`.
+## ℹ In row 1.
+## Caused by warning in `dim()`:
+## ! The dim() method for DataFrameList objects is deprecated. Please use dims() on these objects instead.
+## ℹ Run `dplyr::last_dplyr_warnings()` to see the 23858 remaining warnings.
+
lca_df <- all_ref_df |> 
+  dplyr::mutate(
+    total_lca = length(lca), # max is three terms 
+    lca = paste0(lca, collapse = ",") # make it easier to split the df 
+  ) |>
+  # split each lca term into its own column 
+  tidyr::separate(lca, into = c("lca_1", "lca_2", "lca_3"), sep = ",") |> 
+  tidyr::pivot_longer(
+    cols = dplyr::starts_with("lca"),
+    names_to = "lca_number",
+    values_to = "lca"
+  ) |> 
+  tidyr::drop_na() |> 
+  dplyr::select(-lca_number) |> 
+  # account for any cases where the ontology IDs are exact matches 
+  # r complains about doing this earlier since the lca column holds lists until now
+  dplyr::mutate(lca = dplyr::if_else(blueprint_ontology == panglao_ontology, blueprint_ontology, lca)) |> 
+  # join in information for each of the lca terms including name, number of ancestors and descendants 
+  dplyr::left_join(cl_df, by = c("lca" = "cl_ontology"))
+
## Warning: Expected 3 pieces. Missing pieces filled with `NA` in 7967 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+## 18, 19, 20, ...].
+
+

Distribution of ancestors and descendants

+
ggplot(lca_df, aes(x = total_ancestors)) +
+  geom_density() +
+  geom_vline(data = ancestor_labels_df,
+             mapping = aes(xintercept = total_ancestors), 
+             lty = 2) +
+  geom_text(
+    data = ancestor_labels_df,
+    mapping = aes(x = total_ancestors, y = 0.6, label = cl_annotation),
+    angle = 90, 
+    vjust = -0.5
+  ) +
+  labs(
+    x = "Total number of ancestors",
+    y = "Density"
+  )
+

+
ggplot(lca_df, aes(x = total_descendants)) +
+  geom_density() +
+  geom_vline(data = line_df,
+             mapping = aes(xintercept = total_descendants), 
+             lty = 2) +
+  geom_text(
+    data = line_df,
+    mapping = aes(x = total_descendants, y = 0.002, label = cl_annotation),
+    angle = 90, 
+    vjust = -0.5
+  ) +
+  labs(
+    x = "Total number of descendants",
+    y = "Density"
+  )
+

+

Let’s zoom into the area below 1000, since we already know we would +want to exlude anything above that based on this plot.

+
ggplot(lca_df, aes(x = total_descendants)) +
+  geom_density() +
+  geom_vline(data = line_df,
+             mapping = aes(xintercept = total_descendants), 
+             lty = 2) +
+  geom_text(
+    data = line_df,
+    mapping = aes(x = total_descendants, y = 0.002, label = cl_annotation),
+    angle = 90, 
+    vjust = -0.5
+  ) +
+  xlim(c(0, 1000)) +
+  labs(
+    x = "Total number of descendants",
+    y = "Density"
+  )
+
## Warning: Removed 6856 rows containing non-finite outside the scale range (`stat_density()`).
+
## Warning: Removed 1 row containing missing values or values outside the scale range (`geom_vline()`).
+
## Warning: Removed 1 row containing missing values or values outside the scale range (`geom_text()`).
+

+

We can use the vertical lines for cells of interest to help us define +a potential cutoff based on the granularity we would like to see in our +consensus label. We want to be able to label things like T cell, but we +don’t want to label anything as lymphocyte as that’s probably not +helpful. I don’t see any obvious cutoffs that may be present in the +total number of ancestors, but the number of descendants is likely to be +informative. I think it might be a good idea to start by drawing a line +at the local maxima between the T cell and lymphocyte lines on the +number of descendants graph.

+
+
+

Defining a cutoff for number of descendants

+

First we will find the value for the first peak shown in the +distribution. This is likely to be a good cutoff for deciding which LCA +labels to keep.

+
peak_idx <- splus2R::peaks(lca_df$total_descendants)
+cutoff <- lca_df$total_descendants[peak_idx] |> 
+  min() # find the smallest peak and use that as the cutoff for number of descendants
+

Below is the list of all consensus cell type labels that we will be +keeping if we were to just use this cutoff.

+
celltypes_to_keep <- lca_df |> 
+  dplyr::filter(total_descendants <= cutoff) |> 
+  dplyr::pull(cl_annotation) |> 
+  unique()
+
+celltypes_to_keep
+
##  [1] "myeloid leukocyte"                   "granulocyte"                         "neutrophil"                         
+##  [4] "blood cell"                          "mononuclear phagocyte"               "progenitor cell"                    
+##  [7] "monocyte"                            "hematopoietic precursor cell"        "T cell"                             
+## [10] "CD4-positive, alpha-beta T cell"     "mature alpha-beta T cell"            "mature T cell"                      
+## [13] "regulatory T cell"                   "memory T cell"                       "natural killer cell"                
+## [16] "innate lymphoid cell"                "B cell"                              "lymphocyte of B lineage"            
+## [19] "mature B cell"                       "naive B cell"                        "memory B cell"                      
+## [22] "somatic stem cell"                   "stem cell"                           "hematopoietic stem cell"            
+## [25] "bone cell"                           "macrophage"                          "erythroid lineage cell"             
+## [28] "megakaryocyte"                       "endothelial cell"                    "lining cell"                        
+## [31] "dendritic cell"                      "eosinophil"                          "plasma cell"                        
+## [34] "chondrocyte"                         "stromal cell"                        "extracellular matrix secreting cell"
+## [37] "fibroblast"                          "smooth muscle cell"                  "muscle cell"                        
+## [40] "melanocyte"                          "cell of skeletal muscle"             "ecto-epithelial cell"               
+## [43] "keratinocyte"                        "squamous epithelial cell"            "epidermal cell"                     
+## [46] "blood vessel endothelial cell"       "microvascular endothelial cell"      "adipocyte"                          
+## [49] "pericyte"                            "perivascular cell"                   "supporting cell"                    
+## [52] "astrocyte"                           "glial cell"                          "macroglial cell"                    
+## [55] "neuron associated cell"              "mesangial cell"
+

We can also look at all the cell types we are keeping and the total +number of descendants to see if there are any that may be we don’t want +to include because the term is too braod.

+
# pull out the cell types and total descendants for cell types to keep
+plot_celltype_df <- lca_df |>
+  dplyr::filter(cl_annotation %in% celltypes_to_keep) |>
+  dplyr::select(cl_annotation, total_descendants) |> 
+  unique()
+
+# bar chart showing total number of descendants for each cell type
+ggplot(plot_celltype_df, aes(x = reorder(cl_annotation, total_descendants), y = total_descendants)) +
+  geom_bar(stat = "identity") +
+  theme(
+    axis.text.x = element_text(angle = 90)
+  ) +
+  labs(
+    x = "cell type",
+    y = "Total descendants"
+  )
+

+

There are a few terms that I think might be more broad than we want +like blood cell, bone cell, +supporting cell, and lining cell. I’m on the +fence about keeping myeloid leukocyte and +progenitor cell. I think if we wanted to remove those terms +we could move our cutoff to be the same number of descendants as +T cell, since we do want to keep that.

+

One could also argue to remove stromal cell or +extracellular matrix secreting cell.

+

Below are tables that look specifically at the combinations of cell +type annotations that resulted in some of the terms that I might +consider removing.

+
+

Blood cell

+
print_df <- lca_df |> 
+  dplyr::select(blueprint_ontology, blueprint_annotation_main, blueprint_annotation_fine, panglao_ontology, panglao_annotation, total_lca, lca, cl_annotation)
+
+# blood cell
+print_df |> 
+  dplyr::filter(cl_annotation == "blood cell")
+
+ ++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
blueprint_ontologyblueprint_annotation_mainblueprint_annotation_finepanglao_ontologypanglao_annotationtotal_lcalcacl_annotation
CL:0000775NeutrophilsNeutrophilsCL:0000233platelet2CL:0000081blood cell
CL:0000232ErythrocytesErythrocytesCL:0000767basophil2CL:0000081blood cell
CL:0000232ErythrocytesErythrocytesCL:0000771eosinophil2CL:0000081blood cell
CL:0000232ErythrocytesErythrocytesCL:0000775neutrophil2CL:0000081blood cell
CL:0000232ErythrocytesErythrocytesCL:0000233platelet2CL:0000081blood cell
CL:0000771EosinophilsEosinophilsCL:0000233platelet2CL:0000081blood cell
+
+

I think I’m in favor of not having a “blood cell” label, since I’m +not sure that it’s helpful. Also, if two different methods label +something a platelet and a neutrophil, then perhaps that label is +inaccurate and it’s really a tumor cell.

+
+
+

Bone cell

+
# bone cell
+print_df |> 
+  dplyr::filter(cl_annotation == "bone cell")
+
+ ++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
blueprint_ontologyblueprint_annotation_mainblueprint_annotation_finepanglao_ontologypanglao_annotationtotal_lcalcacl_annotation
CL:0000557HSCGMPCL:0000092osteoclast2CL:0001035bone cell
CL:0000557HSCGMPCL:0000137osteocyte1CL:0001035bone cell
+
+

I think I would also remove bone cell, since hematopoietic stem cells +and osteoclasts seem pretty different to me.

+
+
+

Myeloid leukocyte

+
# myeloid leukocyte cell
+print_df |> 
+  dplyr::filter(cl_annotation == "myeloid leukocyte")
+
+ ++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
blueprint_ontologyblueprint_annotation_mainblueprint_annotation_finepanglao_ontologypanglao_annotationtotal_lcalcacl_annotation
CL:0000775NeutrophilsNeutrophilsCL:0000583alveolar macrophage1CL:0000766myeloid leukocyte
CL:0000775NeutrophilsNeutrophilsCL:0000235macrophage1CL:0000766myeloid leukocyte
CL:0000775NeutrophilsNeutrophilsCL:0000097mast cell1CL:0000766myeloid leukocyte
CL:0000775NeutrophilsNeutrophilsCL:0000576monocyte1CL:0000766myeloid leukocyte
CL:0000775NeutrophilsNeutrophilsCL:0000576monocyte1CL:0000766myeloid leukocyte
CL:0000775NeutrophilsNeutrophilsCL:0000092osteoclast1CL:0000766myeloid leukocyte
CL:0000775NeutrophilsNeutrophilsCL:0000091Kupffer cell1CL:0000766myeloid leukocyte
CL:0000775NeutrophilsNeutrophilsCL:0000453Langerhans cell1CL:0000766myeloid leukocyte
CL:0000775NeutrophilsNeutrophilsCL:0000129microglial cell1CL:0000766myeloid leukocyte
CL:0000775NeutrophilsNeutrophilsCL:0000889myeloid suppressor cell1CL:0000766myeloid leukocyte
CL:0000775NeutrophilsNeutrophilsCL:0000576monocyte1CL:0000766myeloid leukocyte
CL:0000775NeutrophilsNeutrophilsCL:0000576monocyte1CL:0000766myeloid leukocyte
CL:0000775NeutrophilsNeutrophilsCL:0000874splenic red pulp macrophage1CL:0000766myeloid leukocyte
CL:0000576MonocytesMonocytesCL:0000583alveolar macrophage2CL:0000766myeloid leukocyte
CL:0000576MonocytesMonocytesCL:0000767basophil1CL:0000766myeloid leukocyte
CL:0000576MonocytesMonocytesCL:0000771eosinophil1CL:0000766myeloid leukocyte
CL:0000576MonocytesMonocytesCL:0000235macrophage2CL:0000766myeloid leukocyte
CL:0000576MonocytesMonocytesCL:0000097mast cell1CL:0000766myeloid leukocyte
CL:0000576MonocytesMonocytesCL:0000775neutrophil1CL:0000766myeloid leukocyte
CL:0000576MonocytesMonocytesCL:0000092osteoclast2CL:0000766myeloid leukocyte
CL:0000576MonocytesMonocytesCL:0000091Kupffer cell2CL:0000766myeloid leukocyte
CL:0000576MonocytesMonocytesCL:0000453Langerhans cell2CL:0000766myeloid leukocyte
CL:0000576MonocytesMonocytesCL:0000129microglial cell2CL:0000766myeloid leukocyte
CL:0000576MonocytesMonocytesCL:0000889myeloid suppressor cell1CL:0000766myeloid leukocyte
CL:0000576MonocytesMonocytesCL:0000874splenic red pulp macrophage2CL:0000766myeloid leukocyte
CL:0000235MacrophagesMacrophagesCL:0000767basophil1CL:0000766myeloid leukocyte
CL:0000235MacrophagesMacrophagesCL:0000771eosinophil1CL:0000766myeloid leukocyte
CL:0000235MacrophagesMacrophagesCL:0000097mast cell1CL:0000766myeloid leukocyte
CL:0000235MacrophagesMacrophagesCL:0000576monocyte2CL:0000766myeloid leukocyte
CL:0000235MacrophagesMacrophagesCL:0000576monocyte2CL:0000766myeloid leukocyte
CL:0000235MacrophagesMacrophagesCL:0000775neutrophil1CL:0000766myeloid leukocyte
CL:0000235MacrophagesMacrophagesCL:0000092osteoclast2CL:0000766myeloid leukocyte
CL:0000235MacrophagesMacrophagesCL:0000453Langerhans cell3CL:0000766myeloid leukocyte
CL:0000235MacrophagesMacrophagesCL:0000889myeloid suppressor cell1CL:0000766myeloid leukocyte
CL:0000235MacrophagesMacrophagesCL:0000576monocyte2CL:0000766myeloid leukocyte
CL:0000235MacrophagesMacrophagesCL:0000576monocyte2CL:0000766myeloid leukocyte
CL:0000863MacrophagesMacrophages M1CL:0000767basophil1CL:0000766myeloid leukocyte
CL:0000863MacrophagesMacrophages M1CL:0000771eosinophil1CL:0000766myeloid leukocyte
CL:0000863MacrophagesMacrophages M1CL:0000097mast cell1CL:0000766myeloid leukocyte
CL:0000863MacrophagesMacrophages M1CL:0000576monocyte2CL:0000766myeloid leukocyte
CL:0000863MacrophagesMacrophages M1CL:0000576monocyte2CL:0000766myeloid leukocyte
CL:0000863MacrophagesMacrophages M1CL:0000775neutrophil1CL:0000766myeloid leukocyte
CL:0000863MacrophagesMacrophages M1CL:0000092osteoclast2CL:0000766myeloid leukocyte
CL:0000863MacrophagesMacrophages M1CL:0000453Langerhans cell3CL:0000766myeloid leukocyte
CL:0000863MacrophagesMacrophages M1CL:0000889myeloid suppressor cell1CL:0000766myeloid leukocyte
CL:0000863MacrophagesMacrophages M1CL:0000576monocyte2CL:0000766myeloid leukocyte
CL:0000863MacrophagesMacrophages M1CL:0000576monocyte2CL:0000766myeloid leukocyte
CL:0000890MacrophagesMacrophages M2CL:0000767basophil1CL:0000766myeloid leukocyte
CL:0000890MacrophagesMacrophages M2CL:0000771eosinophil1CL:0000766myeloid leukocyte
CL:0000890MacrophagesMacrophages M2CL:0000097mast cell1CL:0000766myeloid leukocyte
CL:0000890MacrophagesMacrophages M2CL:0000576monocyte2CL:0000766myeloid leukocyte
CL:0000890MacrophagesMacrophages M2CL:0000576monocyte2CL:0000766myeloid leukocyte
CL:0000890MacrophagesMacrophages M2CL:0000775neutrophil1CL:0000766myeloid leukocyte
CL:0000890MacrophagesMacrophages M2CL:0000092osteoclast2CL:0000766myeloid leukocyte
CL:0000890MacrophagesMacrophages M2CL:0000453Langerhans cell3CL:0000766myeloid leukocyte
CL:0000890MacrophagesMacrophages M2CL:0000889myeloid suppressor cell1CL:0000766myeloid leukocyte
CL:0000890MacrophagesMacrophages M2CL:0000576monocyte2CL:0000766myeloid leukocyte
CL:0000890MacrophagesMacrophages M2CL:0000576monocyte2CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000583alveolar macrophage1CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000235macrophage1CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000097mast cell1CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000576monocyte1CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000576monocyte1CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000092osteoclast1CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000091Kupffer cell1CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000453Langerhans cell1CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000129microglial cell1CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000889myeloid suppressor cell1CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000576monocyte1CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000576monocyte1CL:0000766myeloid leukocyte
CL:0000771EosinophilsEosinophilsCL:0000874splenic red pulp macrophage1CL:0000766myeloid leukocyte
+
+

I’m torn on this one, because I do think it’s helpful to know if +something is of the myeloid lineage, but if we aren’t keeping lymphocyte +then I would argue we shouldn’t keep myeloid leukocyte.

+
+
+

Progenitor cell

+
# progenitor cell
+print_df |> 
+  dplyr::filter(cl_annotation == "progenitor cell") |> 
+  head(n=15) # there's a lot of these so let's only print out some
+
+ ++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
blueprint_ontologyblueprint_annotation_mainblueprint_annotation_finepanglao_ontologypanglao_annotationtotal_lcalcacl_annotation
CL:0000576MonocytesMonocytesCL:0000765erythroblast2CL:0011026progenitor cell
CL:0000576MonocytesMonocytesCL:0000037hematopoietic stem cell2CL:0011026progenitor cell
CL:0000576MonocytesMonocytesCL:0000062osteoblast1CL:0011026progenitor cell
CL:0000576MonocytesMonocytesCL:0000158club cell1CL:0011026progenitor cell
CL:0000576MonocytesMonocytesCL:0000038erythroid progenitor cell2CL:0011026progenitor cell
CL:0000576MonocytesMonocytesCL:4042021neuronal-restricted precursor1CL:0011026progenitor cell
CL:0000576MonocytesMonocytesCL:0002453oligodendrocyte precursor cell1CL:0011026progenitor cell
CL:0000576MonocytesMonocytesCL:0002351progenitor cell of endocrine pancreas1CL:0011026progenitor cell
CL:0000050HSCMEPCL:0000765erythroblast2CL:0011026progenitor cell
CL:0000050HSCMEPCL:0000037hematopoietic stem cell2CL:0011026progenitor cell
CL:0000050HSCMEPCL:0000576monocyte2CL:0011026progenitor cell
CL:0000050HSCMEPCL:0000576monocyte2CL:0011026progenitor cell
CL:0000050HSCMEPCL:0000062osteoblast1CL:0011026progenitor cell
CL:0000050HSCMEPCL:0000158club cell1CL:0011026progenitor cell
CL:0000050HSCMEPCL:0000038erythroid progenitor cell3CL:0011026progenitor cell
+
+

Same with progenitor cell, I do think it could be +helpful to know that something may be a progenitor cell, but when you +have a cell with the label for HSC and the label for cells like +monocytes or osteoblasts, then maybe we are talking about a tumor cell +instead.

+

Along those same lines, I think the below terms, +lining cell and supporting cell, are too broad +even though they have few descendants.

+
+
+

Lining cell

+
# lining cell 
+print_df |> 
+  dplyr::filter(cl_annotation == "lining cell")
+
+ ++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
blueprint_ontologyblueprint_annotation_mainblueprint_annotation_finepanglao_ontologypanglao_annotationtotal_lcalcacl_annotation
CL:0000115Endothelial cellsEndothelial cellsCL:0000077mesothelial cell2CL:0000213lining cell
CL:0000115Endothelial cellsEndothelial cellsCL:0002481peritubular myoid cell2CL:0000213lining cell
CL:0000115Endothelial cellsEndothelial cellsCL:0000216Sertoli cell2CL:0000213lining cell
CL:2000008Endothelial cellsmv Endothelial cellsCL:0000077mesothelial cell2CL:0000213lining cell
CL:2000008Endothelial cellsmv Endothelial cellsCL:0002481peritubular myoid cell2CL:0000213lining cell
CL:2000008Endothelial cellsmv Endothelial cellsCL:0000216Sertoli cell2CL:0000213lining cell
+
+
+
+

Supporting cell

+
# supporting cell 
+print_df |> 
+  dplyr::filter(cl_annotation == "supporting cell")
+
+ ++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
blueprint_ontologyblueprint_annotation_mainblueprint_annotation_finepanglao_ontologypanglao_annotationtotal_lcalcacl_annotation
CL:0000669PericytesPericytesCL:0000216Sertoli cell2CL:0000630supporting cell
CL:0000650Mesangial cellsMesangial cellsCL:0000216Sertoli cell2CL:0000630supporting cell
+
+
+
+
+

Discarded cell types

+

We can also look at what cell type labels we are excluding when using +this cut off to see if there are any terms we might actually want to +keep instead.

+
lca_df |> 
+  dplyr::filter(total_descendants > cutoff) |> 
+  dplyr::pull(cl_annotation) |> 
+  unique()
+
##  [1] "leukocyte"                            "eukaryotic cell"                      "myeloid cell"                        
+##  [4] "cell"                                 "hematopoietic cell"                   "mononuclear cell"                    
+##  [7] "stuff accumulating cell"              "precursor cell"                       "phagocyte (sensu Vertebrata)"        
+## [10] "defensive cell"                       "lymphocyte"                           "professional antigen presenting cell"
+## [13] "secretory cell"                       "connective tissue cell"               "electrically responsive cell"        
+## [16] "contractile cell"                     "epithelial cell"                      "neuron"                              
+## [19] "neural cell"
+

The only term in this list that I would be concerned about losing is +“neuron”. Let’s look at those combinations.

+
+

Neuron

+
# blood cell
+print_df |> 
+  dplyr::filter(cl_annotation == "neuron")
+
+ ++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
blueprint_ontologyblueprint_annotation_mainblueprint_annotation_finepanglao_ontologypanglao_annotationtotal_lcalcacl_annotation
CL:0000540NeuronsNeuronsCL:0000109adrenergic neuron1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000108cholinergic neuron1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000166chromaffin cell1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000700dopaminergic neuron1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0007011enteric neuron1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:1001509glycinergic neuron1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000099interneuron1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000100motor neuron1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000165neuroendocrine cell1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000540neuron0CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0008025noradrenergic neuron1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000210photoreceptor cell1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000740retinal ganglion cell1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000850serotonergic neuron1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:4023169trigeminal neuron1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000695Cajal-Retzius cell1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000617GABAergic neuron1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000679glutamatergic neuron1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000121Purkinje cell1CL:0000540neuron
CL:0000540NeuronsNeuronsCL:0000598pyramidal neuron1CL:0000540neuron
+
+

It looks like there are a lot of types of neurons in the PanglaoDB +reference and only “neuron” as a term in Blueprint. Even though neuron +has ~ 500 descendants, I think we should keep these labels.

+
+
+
+

Removing anything with more than 1 LCA

+

One thing I noticed when looking at the labels that have less than +the cutoff is that most of them are from scenarios where we have +multiple LCAs. Maybe in the case where we have multiple LCAs we are +already too broad and we should just eliminate those matches from the +beginning. Here I’m looking at the total number of descendants for all +terms that show up because a term has multiple LCAs.

+
lca_df |> 
+  dplyr::filter(total_lca > 1) |> 
+  dplyr::select(cl_annotation, total_descendants) |> 
+  unique() |>
+  dplyr::arrange(total_descendants)
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
cl_annotationtotal_descendants
bone cell39
blood cell42
perivascular cell42
stromal cell54
supporting cell62
hematopoietic precursor cell106
lining cell121
myeloid leukocyte166
progenitor cell166
mononuclear phagocyte170
phagocyte (sensu Vertebrata)176
contractile cell178
defensive cell200
professional antigen presenting cell213
connective tissue cell224
myeloid cell248
stuff accumulating cell267
precursor cell272
secretory cell458
mononuclear cell504
leukocyte541
electrically responsive cell674
hematopoietic cell685
eukaryotic cell2646
+
+

It looks like most of these terms are pretty broad and are either +much higher than the cutoff or right around the cutoff with a few +exceptions. Things like “bone cell” and “supporting cell” have few +descendants, but I would still argue these are very broad terms and not +useful.

+

I’m going to filter out any matches that show two LCA terms first and +then use the cutoff to define labels we would keep. I’ll also look to +see what cell types we lose when we add this extra filtering step to be +sure they are ones that we want to lose.

+
# remove any combinations with more than one lca
+filtered_lca_df <- lca_df |> 
+  dplyr::filter(total_lca < 2)
+
+# get a list of cell types to keep based on cutoff 
+updated_celltypes <- filtered_lca_df |> 
+  dplyr::filter(total_descendants <= cutoff) |> 
+  dplyr::pull(cl_annotation) |> 
+  unique()
+
+# which cell types are now missing from the list to keep
+setdiff(celltypes_to_keep, updated_celltypes)
+
## [1] "blood cell"                   "hematopoietic precursor cell" "lining cell"                 
+## [4] "perivascular cell"            "supporting cell"
+

It looks like I am losing a few terms I already said were not +specific and then a few other terms, like “hematopoietic precursor cell” +and “perivascular cell”. I’ll look at both of those to confirm we would +not want them.

+
+

Hematopoietic precursor cell

+
print_df |> 
+  dplyr::filter(cl_annotation == "hematopoietic precursor cell")
+
+ ++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
blueprint_ontologyblueprint_annotation_mainblueprint_annotation_finepanglao_ontologypanglao_annotationtotal_lcalcacl_annotation
CL:0000050HSCMEPCL:0000037hematopoietic stem cell2CL:0008001hematopoietic precursor cell
CL:0000050HSCMEPCL:0000038erythroid progenitor cell3CL:0008001hematopoietic precursor cell
CL:0000037HSCHSCCL:0000038erythroid progenitor cell2CL:0008001hematopoietic precursor cell
CL:0000837HSCMPPCL:0000037hematopoietic stem cell2CL:0008001hematopoietic precursor cell
CL:0000837HSCMPPCL:0000038erythroid progenitor cell2CL:0008001hematopoietic precursor cell
CL:0000051HSCCLPCL:0000037hematopoietic stem cell2CL:0008001hematopoietic precursor cell
CL:0000051HSCCLPCL:0000038erythroid progenitor cell2CL:0008001hematopoietic precursor cell
CL:0000557HSCGMPCL:0000037hematopoietic stem cell2CL:0008001hematopoietic precursor cell
CL:0000557HSCGMPCL:0000038erythroid progenitor cell3CL:0008001hematopoietic precursor cell
CL:0000049HSCCMPCL:0000037hematopoietic stem cell2CL:0008001hematopoietic precursor cell
CL:0000049HSCCMPCL:0000038erythroid progenitor cell2CL:0008001hematopoietic precursor cell
+
+

It looks like here we should be keeping these matches because both +references have these labels as hematopoietic stem and progenitor cells. +I think in the context of pediatric cancer having this label would be +helpful, so maybe we shouldn’t remove all terms that have 2 LCAs.

+

Let’s look at what the other LCA is for an example set.

+
lca_df |> 
+  dplyr::filter(panglao_ontology == "CL:0000037" & blueprint_ontology == "CL:0000050") |> 
+  dplyr::select(blueprint_annotation_main, blueprint_annotation_fine, panglao_annotation, cl_annotation)
+
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
blueprint_annotation_mainblueprint_annotation_finepanglao_annotationcl_annotation
HSCMEPhematopoietic stem cellhematopoietic precursor cell
HSCMEPhematopoietic stem cellprogenitor cell
+
+

It looks like these terms have both +hematopoietic precursor cell and +progenitor cell as LCAs. Personally, I would keep the term +for hematopoietic precursor cell because I think it’s more +informative and specific to the type of progenitor cell.

+
+
+

Perivascular cell

+
print_df |> 
+  dplyr::filter(cl_annotation == "perivascular cell")
+
+ ++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
blueprint_ontologyblueprint_annotation_mainblueprint_annotation_finepanglao_ontologypanglao_annotationtotal_lcalcacl_annotation
CL:0000669PericytesPericytesCL:0000359vascular associated smooth muscle cell3CL:4033054perivascular cell
CL:0000669PericytesPericytesCL:0000359vascular associated smooth muscle cell3CL:4033054perivascular cell
CL:0000669PericytesPericytesCL:0000359vascular associated smooth muscle cell3CL:4033054perivascular cell
CL:0000669PericytesPericytesCL:0000359vascular associated smooth muscle cell3CL:4033054perivascular cell
CL:0000650Mesangial cellsMesangial cellsCL:0000359vascular associated smooth muscle cell3CL:4033054perivascular cell
CL:0000650Mesangial cellsMesangial cellsCL:0000359vascular associated smooth muscle cell3CL:4033054perivascular cell
CL:0000650Mesangial cellsMesangial cellsCL:0000359vascular associated smooth muscle cell3CL:4033054perivascular cell
CL:0000650Mesangial cellsMesangial cellsCL:0000359vascular associated smooth muscle cell3CL:4033054perivascular cell
+
+

I would remove perivascular cell, since the cell type +labels from PanglaoDB and Blueprint are pretty different from each +other.

+
+
+
+
+

Similarity index

+

An alternative approach would be to calculate the similarity +index between each set of terms and define a cutoff for which set of +terms are similar. This is a value on a 0-1 scale where 0 indicates no +similarity and 1 indicates the terms are equal.

+

Although this could provide a metric that we could use to define +similar cell types, we would still have to identify the label to use +which would most likely be the LCA. Even if the similarity index is +close to 1, if the LCA term is not informative then I don’t know that we +would want to use that.

+

However, we could use this to finalize the actual pairs of terms that +we trust. For example, if the LCA for a pair is T cell we +can look at the similarity index to confirm that specific pair of terms +has high similarity.

+

Below I’ll calculate the similarity index for each set of terms and +plot the distribution. Then we will look at the values for pairs that +have an LCA that pass the total descendants threshold we set to see if +those pairs have a higher similarity index.

+
information_content <- ontologySimilarity::descendants_IC(cl_ont)
+
+# get similarity index for each set of terms 
+si_df <- lca_df |> 
+  dplyr::rowwise() |> 
+  dplyr::mutate(
+    similarity_index = ontologySimilarity::get_sim_grid(ontology = cl_ont,
+                                                        term_sets = list(panglao_ontology, blueprint_ontology)) |> 
+      ontologySimilarity::get_sim()
+  )
+
si_df <- si_df |> 
+  dplyr::mutate(
+    lca_threshold = dplyr::if_else(total_descendants < cutoff, "PASS", "FAIL")
+  )
+
+ggplot(si_df, aes(x = similarity_index, fill = lca_threshold)) +
+  geom_density(bw = 0.05, alpha = 0.5) +
+  labs(
+    x = "Similarity index",
+    y = "Density"
+  )
+

+

This looks as I expected with most of the pairs that pass the total +descendants cutoff having a higher similarity index than those that do +not pass. There is still some overlap though so perhaps even if a set of +terms shares an LCA that passes the threshold, the actual terms being +compared may be further apart than we would like.

+

Now let’s look at the similarity index for various LCA terms. Here +each LCA term is its own plot and the vertical lines are the similarity +index for each pair of terms that results in that LCA.

+
celltypes_to_plot <- c("myeloid leukocyte", "T cell", "cell", "supporting cell", "B cell")
+
+celltypes_to_plot |> 
+  purrr::map(\(celltype){
+    line_df <- si_df |> 
+      dplyr::filter(cl_annotation == celltype) |> 
+      dplyr::select(cl_annotation, similarity_index) |> 
+      unique()
+    
+    ggplot(si_df, aes(x = similarity_index)) +
+      geom_density() +
+      geom_vline(data = line_df,
+                 mapping = aes(xintercept = similarity_index), 
+                 lty = 2) +
+      labs(
+        x = "Similarity index",
+        y = "Density",
+        title = celltype
+      )
+    
+  })
+
## [[1]]
+

+
## 
+## [[2]]
+

+
## 
+## [[3]]
+

+
## 
+## [[4]]
+

+
## 
+## [[5]]
+

+

It looks like terms that are more granular like T and B cell have +higher similarity index values than terms that are less granular which +is what we would expect. However, within terms like myeloid leukocyte +and even T cell we do see a range of values. We could dig deeper into +which pairs are resulting in which similarity index values if we wanted +to, but I think that might be a future direction if we feel like the +similarity index is something that could be useful.

+
+
+

Conclusions

+

Based on these findings, I think it might be best to create a +reference that has all possible pairs of labels between PanglaoDB and +Blueprint Encode and the resulting consensus label for those pairs. To +do this we could come up with a whitelist of LCA terms that we would be +comfortable including and all other cell types would be unknowns. I +would use the following criteria to come up with my whitelist:

+
    +
  • Pairs should not have more than 1 LCA, with the exception of the +matches that have the label hematopoietic precursor cell.
    • +
  • The LCA should have equal to or less than 170 total +descendants.
    • +
  • We whould include the term for neuron even though it +has 500 descendants.
    • +
  • Terms that are too broad (like supporting cell, +blood cell, bone cell, +lining cell) should be removed.
    • +
+

Alternatively, rather than eliminate terms that are too broad we +could look at the similarity index for individual matches and decide on +a case by case basis if those should be allowed. Although I still think +having a term that is too braod, even if it’s a good match, is not super +informative.

+
+
+

Session info

+
sessionInfo()
+
## R version 4.4.2 (2024-10-31)
+## Platform: aarch64-apple-darwin20
+## Running under: macOS Sonoma 14.4
+## 
+## Matrix products: default
+## BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
+## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/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: America/Chicago
+## tzcode source: internal
+## 
+## attached base packages:
+## [1] stats     graphics  grDevices datasets  utils     methods   base     
+## 
+## other attached packages:
+## [1] ggplot2_3.5.1
+## 
+## loaded via a namespace (and not attached):
+##   [1] RColorBrewer_1.1-3          jsonlite_1.8.9              magrittr_2.0.3              gypsum_1.2.0               
+##   [5] farver_2.1.2                rmarkdown_2.29              zlibbioc_1.52.0             vctrs_0.6.5                
+##   [9] memoise_2.0.1               DelayedMatrixStats_1.28.0   htmltools_0.5.8.1           S4Arrays_1.6.0             
+##  [13] polynom_1.4-1               AnnotationHub_3.14.0        curl_6.0.1                  Rhdf5lib_1.28.0            
+##  [17] SparseArray_1.6.0           rhdf5_2.50.0                sass_0.4.9                  alabaster.base_1.6.1       
+##  [21] bslib_0.8.0                 htmlwidgets_1.6.4           httr2_1.0.7                 cachem_1.1.0               
+##  [25] igraph_2.1.1                mime_0.12                   lifecycle_1.0.4             pkgconfig_2.0.3            
+##  [29] Matrix_1.7-1                R6_2.5.1                    fastmap_1.2.0               GenomeInfoDbData_1.2.13    
+##  [33] MatrixGenerics_1.18.0       shiny_1.9.1                 digest_0.6.37               colorspace_2.1-1           
+##  [37] AnnotationDbi_1.68.0        S4Vectors_0.44.0            rprojroot_2.0.4             ExperimentHub_2.14.0       
+##  [41] GenomicRanges_1.58.0        RSQLite_2.3.9               filelock_1.0.3              labeling_0.4.3             
+##  [45] fansi_1.0.6                 httr_1.4.7                  polyclip_1.10-7             abind_1.4-8                
+##  [49] compiler_4.4.2              bit64_4.5.2                 withr_3.0.2                 DBI_1.2.3                  
+##  [53] ontologySimilarity_2.7      HDF5Array_1.34.0            ggforce_0.4.2               alabaster.ranges_1.6.0     
+##  [57] alabaster.schemas_1.6.0     MASS_7.3-61                 quantreg_5.99.1             rappdirs_0.3.3             
+##  [61] DelayedArray_0.32.0         ggpp_0.5.8-1                tools_4.4.2                 httpuv_1.6.15              
+##  [65] glue_1.8.0                  rhdf5filters_1.18.0         promises_1.3.2              grid_4.4.2                 
+##  [69] generics_0.1.3              gtable_0.3.6                tzdb_0.4.0                  tidyr_1.3.1                
+##  [73] hms_1.1.3                   utf8_1.2.4                  XVector_0.46.0              BiocGenerics_0.52.0        
+##  [77] BiocVersion_3.20.0          pillar_1.9.0                stringr_1.5.1               vroom_1.6.5                
+##  [81] later_1.4.1                 splines_4.4.2               dplyr_1.1.4                 tweenr_2.0.3               
+##  [85] BiocFileCache_2.14.0        lattice_0.22-6              survival_3.7-0              renv_1.0.11                
+##  [89] bit_4.5.0.1                 SparseM_1.84-2              tidyselect_1.2.1            Biostrings_2.74.0          
+##  [93] knitr_1.49                  ggpmisc_0.6.1               IRanges_2.40.0              ontologyPlot_1.7           
+##  [97] SummarizedExperiment_1.36.0 stats4_4.4.2                xfun_0.49                   Biobase_2.66.0             
+## [101] matrixStats_1.4.1           DT_0.33                     stringi_1.8.4               UCSC.utils_1.2.0           
+## [105] paintmap_1.0                yaml_2.3.10                 evaluate_1.0.1              tibble_3.2.1               
+## [109] Rgraphviz_2.50.0            alabaster.matrix_1.6.1      BiocManager_1.30.25         graph_1.84.0               
+## [113] cli_3.6.3                   ontologyIndex_2.12          xtable_1.8-4                reticulate_1.40.0          
+## [117] jquerylib_0.1.4             munsell_0.5.1               Rcpp_1.0.13-1               GenomeInfoDb_1.42.1        
+## [121] dbplyr_2.5.0                ontoProc_2.0.0              png_0.1-8                   parallel_4.4.2             
+## [125] MatrixModels_0.5-3          readr_2.1.5                 blob_1.2.4                  splus2R_1.3-5              
+## [129] sparseMatrixStats_1.18.0    alabaster.se_1.6.0          scales_1.3.0                purrr_1.0.2                
+## [133] crayon_1.5.3                rlang_1.1.4                 KEGGREST_1.46.0             celldex_1.16.0
+
+ + + + +
+ + + + + + + + + + + + + + + +