This report contains a summary of cell type annotation results for library SCPCL000001. The goal of this report is to provide more detailed information about cell type annotation results as an initial evaluation of their quality and reliability.

Performing cell type annotation is an inherently challenging task with high levels of uncertainty, especially when using automated annotation methods. One way to address this is to use multiple cell type annotation approaches and compare the results, as we have begun to do in this report.

When multiple methods annotate a given cell as the same or similar cell type, this may qualitatively indicate a more robust annotation.

Note that the contents of this report will vary based on which cell type annotations are present. Further, be aware that different cell type annotation methods may assign different labels to the same or similar cell type (e.g., the different string representations B cell naive and Naive B cell), due to use of different underlying reference datasets. Please note that all cell type annotation reference datasets are derived from normal (not tumor) tissue.

This library contains the following cell type annotations:

  • Annotations from SingleR, a reference-based approach (Looney et al. 2019). The BlueprintEncodeData dataset, obtained from the celldex package, was used for reference annotations.
  • Annotations from CellAssign, a marker-gene-based approach (Zhang et al. 2019). Marker genes for cell types were obtained from PanglaoDB and compiled into a reference named brain-compartment. This reference includes the following organs and tissue compartments: brain, embryo, immune system, and vasculature.

Cell type Annotation Summary

The plots and tables included here detail the results from performing cell type annotation.

Statistics

When reprocessing this library, old results from running SingleR and CellAssign were used. This means results were calculated with a slightly different set of cells. Any cells that are missing have been annotated as Unclassified cell in the object and will not be shown in any plots.

SingleR cell type annotations

In this table, cells labeled “Unknown cell type” are those which SingleR pruned due to low-quality assignments. In the processed result files, these cells are labeled NA.
Annotated cell type Number of cells Percent of cells
neuron 2315 88.19%
macrophage 196 7.47%
monocyte 12 0.46%
effector memory CD8-positive, alpha-beta T cell 11 0.42%
CD4-positive, alpha-beta T cell 10 0.38%
mesangial cell 8 0.3%
fat cell 6 0.23%
central memory CD8-positive, alpha-beta T cell 5 0.19%
naive B cell 5 0.19%
central memory CD4-positive, alpha-beta T cell 4 0.15%
chondrocyte 4 0.15%
melanocyte 4 0.15%
natural killer cell 4 0.15%
class switched memory B cell 3 0.11%
erythrocyte 3 0.11%
astrocyte 2 0.08%
CD8-positive, alpha-beta T cell 1 0.04%
common lymphoid progenitor 1 0.04%
granulocyte monocyte progenitor cell 1 0.04%
hematopoietic stem cell 1 0.04%
muscle cell 1 0.04%
plasma cell 1 0.04%
Unknown cell type 27 1.03%

CellAssign cell type annotations

In this table, cells labeled “Unknown cell type” are those which CellAssign could not confidently assign to a label in the reference list. In the processed result files, these cells are labeled "other".
Annotated cell type Number of cells Percent of cells
Purkinje neurons 1872 71.31%
Microglia 200 7.62%
Gamma delta T cells 111 4.23%
T memory cells 25 0.95%
Oligodendrocytes 15 0.57%
Dendritic cells 13 0.5%
B cells 12 0.46%
Satellite glial cells 11 0.42%
Schwann cells 8 0.3%
Meningeal cells 7 0.27%
T cells 7 0.27%
NK cells 6 0.23%
Astrocytes 4 0.15%
Macrophages 4 0.15%
Megakaryocytes 3 0.11%
Monocytes 3 0.11%
Neural stem/precursor cells 3 0.11%
Tanycytes 3 0.11%
Endothelial cells 2 0.08%
Natural killer T cells 2 0.08%
Neutrophils 2 0.08%
Nuocytes 2 0.08%
Choroid plexus cells 1 0.04%
Dopaminergic neurons 1 0.04%
Endothelial cells (blood brain barrier) 1 0.04%
Epiblast cells 1 0.04%
GABAergic neurons 1 0.04%
Oligodendrocyte progenitor cells 1 0.04%
Serotonergic neurons 1 0.04%
Trophoblast cells 1 0.04%
Unknown cell type 302 11.5%

UMAPs

In this section, we show UMAPs colored by clusters. Clusters were calculated using the graph-based Louvain algorithm with Jaccard weighting.

Next, we show UMAPs colored by cell types. For each cell typing method, we show a separate faceted UMAP. In each panel, cells that were assigned the given cell type label are colored, while all other cells are in grey.

For legibility, only the seven most common cell types are shown. All other cell types are grouped together and labeled “All remaining cell types” (not to be confused with “Unknown cell type” which represents cells that could not be classified).

Cell label comparison plots

This section displays heatmaps comparing cell labels from various methods.

We use the Jaccard similarity index to display the agreement between between pairs of labels assigned by different annotation methods.

The Jaccard index reflects the degree of overlap between the two labels and ranges from 0 to 1.

  • If the labels are assigned to identical sets of cells, the Jaccard index will be 1.
  • If the labels are assigned to completely non-overlapping sets of cells, the Jaccard index will be 0.

High agreement between methods qualitatively indicates higher confidence in the cell type annotation.

Unsupervised clustering

Here we show the labels from unsupervised clustering compared to cell type annotations. Cluster assignment was performed using the Louvain algorithm.

Automated annotations

This section displays a heatmap directly comparing SingleR and CellAssign cell type annotations. Note that due to different annotations references, these methods may use different names for similar cell types.

Quality assessments of automated annotations

SingleR annotations

To assess the quality of SingleR cell type annotations, we use the delta median statistic.

  • Delta median is calculated for each cell as the difference between the SingleR score of the annotated cell type label and the median score of the other cell type labels in the reference dataset.
  • Higher delta median values indicate higher quality cell type annotations.

You can interpret this plot as follows:

  • Each point represents the delta median statistic of a given cell whose SingleR annotation is shown on the y-axis.
  • The point style indicates SingleR’s quality assessment of the annotation:
    • High-quality cell annotations are shown as closed points.
    • Low-quality cell annotations are shown as open points. In other sections of this report, these cells are labeled as Unknown cell types.
    • For more information on how SingleR calculates annotation quality, please refer to this SingleR documentation.
  • Diamonds represent the median of the delta median statistic specifically among high-quality annotations for the given cell type annotation.

CellAssign annotations

To assess the quality of CellAssign cell type annotations, we consider the probability associated with the annotated cell type. These probabilities are provided directly by CellAssign:

  • CellAssign first calculates the probability of each cell being annotated as each cell type present in the reference.
  • CellAssign then annotates cells by selecting the cell type with the highest probability among all cell types considered.
  • These probabilities range from 0 to 1, with larger values indicating greater confidence in a given cell type label. We therefore expect reliable labels to have values close to 1.

The plot below shows the distribution of CellAssign-calculated probabilities for the final cell type labels. Line segments represent individual values that comprise each distribution.

For cell types with 2 or fewer labeled cells, only the individual value line segments are shown. Line segments are also taller for any cell type label with 5 or fewer cells.

Session Info

R session information

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
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##  os       Ubuntu 22.04.3 LTS
##  system   x86_64, linux-gnu
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       Etc/UTC
##  date     2024-03-18
##  pandoc   2.9.2.1 @ /usr/bin/ (via rmarkdown)
## 
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## 
##  [1] /usr/local/lib/R/site-library
##  [2] /usr/local/lib/R/library
## 
## ──────────────────────────────────────────────────────────────────────────────
---
params:
  library: Example
  processed_sce: NULL
  date: !r Sys.Date()
title: "`r glue::glue('ScPCA Supplemental cell type report for {params$library}')`"
author: "Childhood Cancer Data Lab"
date: "`r params$date`"
output:
  html_document:
    toc: true
    toc_depth: 2
    toc_float:
      collapsed: false
    number_sections: false
    code_download: true
---

```{r setup, message = FALSE, echo = FALSE}
# knitr options
knitr::opts_chunk$set(
  echo = FALSE
)

library(SingleCellExperiment)
library(ggplot2)

# Set default ggplot theme
theme_set(
  theme_bw() +
    theme(
      plot.margin = margin(rep(20, 4)),
      strip.background = element_rect(fill = "transparent")
    )
)

# This _is_ the supplemental report
# we should not print the chunk from celltypes_qc.rmd recapitulating bullets
#  and pointing users to this supp report
is_supplemental <- TRUE
```

<!-- Import shared functions for cell type wrangling -->
```{r, child='utils/celltype_functions.rmd'}
```
```{r, child='utils/report_functions.rmd'}
```


<!-- Define helper functions for calculating Jaccard matrices -->
```{r}
#' Function to calculate Jaccard similarity on two vectors
#'
#' @param vec1 First vector
#' @param vec2 Second vector
#'
#' @return Jaccard similarity between the vectors
jaccard <- function(vec1, vec2) {
  length(intersect(vec1, vec2)) / length(union(vec1, vec2))
}


# Wrapper function to calculate jaccard similarity matrix for two categorical variables
#'
#' @param celltype_df The celltype_df data frame which must contain these columns:
#'   `colname1`, `colname2`, and `barcodes`
#' @param colname1 Column name, as a string, of first categorical variable of interest
#' @param colname2 Column name, as a string, of second categorical variable of interest
#'
#' @return Jaccard similarity matrix for the two columns. `colname1` values will
#'   be row names and `colname2` values will be column names in the final matrix
make_jaccard_matrix <- function(celltype_df, colname1, colname2) {
  # make lists of barcodes for each category, named by the category
  id1_list <- split(celltype_df$barcodes, celltype_df[[colname1]])
  id2_list <- split(celltype_df$barcodes, celltype_df[[colname2]])

  # create the grid of comparisons
  cross_df <- tidyr::expand_grid(id1 = names(id1_list), id2 = names(id2_list))

  # calculate a single Jaccard index for each combination using split lists & ids
  jaccard_scores <- cross_df |>
    purrr::pmap_dbl(\(id1, id2){
      jaccard(id1_list[[id1]], id2_list[[id2]])
    })

  # add scores to the comparison grid and convert to matrix
  jaccard_matrix <- cross_df |>
    dplyr::mutate(jaccard = jaccard_scores) |>
    # convert to matrix
    tidyr::pivot_wider(
      names_from = "id2",
      values_from = "jaccard"
    ) |>
    tibble::column_to_rownames(var = "id1") |>
    as.matrix()

  return(jaccard_matrix)
}
```


<!-- Define variables, options, and functions for plotting heatmaps -->
```{r}
# Define color ramp for shared use in the heatmap
heatmap_col_fun <- circlize::colorRamp2(c(0, 1), colors = c("white", "darkslateblue"))

# Set heatmap padding option
heatmap_padding <- 0.2
ComplexHeatmap::ht_opt(TITLE_PADDING = grid::unit(heatmap_padding, "in"))

#' Create a ComplexHeatmap from a matrix
#'
#' @param mat Matrix to create heatmap from.
#' @param row_title Label for row title.
#' @param column_title Label for column title.
#' @param labels_font_size Font size to use for rows and column labels.
#' @param keep_legend_name The name to use in the legend
#' @param col_fun Color function for the heatmap palette. Default is `heatmap_col_fun`.
#' @param ... Additional arguments to pass to `ComplexHeatmap::Heatmap()`
#'
#' @return A ComplexHeatmap object
create_single_heatmap <- function(
    mat,
    row_title,
    column_title,
    labels_font_size,
    keep_legend_name,
    col_fun = heatmap_col_fun,
    ...) {
  heat <- ComplexHeatmap::Heatmap(
    t(mat), # transpose because matrix rows are in common & we want a vertical arrangement
    col = col_fun,
    border = TRUE, # each heatmap gets its own outline
    ## Row parameters
    cluster_rows = FALSE,
    row_title = row_title, # each heatmap gets its own title
    row_title_gp = grid::gpar(fontsize = 10),
    row_title_side = "right",
    row_names_side = "left",
    row_names_gp = grid::gpar(fontsize = labels_font_size),
    ## Column parameters
    cluster_columns = FALSE,
    column_title = column_title,
    column_title_gp = grid::gpar(fontsize = 10),
    column_names_side = "bottom",
    column_names_gp = grid::gpar(fontsize = labels_font_size),
    ### passed in args
    ...,
    ## Legend parameters
    heatmap_legend_param = list(
      title = "Jaccard index",
      direction = "horizontal",
      legend_width = unit(1.5, "in")
    ),
    # only keep legends that match `keep_legend_name`
    show_heatmap_legend = row_title == keep_legend_name,
  )

  return(heat)
}

#' Function to plot a vertically-stacked ComplexHeatmap from a list of matrices
#'
#' @param matrix_list List of matrices to plot in a vertical layout
#' @param column_title Title to use for columns, shared among all heatmaps
#' @param labels_font_size Font size to use for rows and column labels
#' @param col_fun Color function for the heatmap palette. Default is `heatmat_col_fun`
#' @param ... Additional arguments to pass to `ComplexHeatmap::Heatmap()`
#'
#' @return A list of ComplexHeatmap objects
create_heatmap_list <- function(
    matrix_list,
    column_title,
    labels_font_size,
    col_fun = heatmap_col_fun,
    ...) {
  # We only want one shared legend in the end, arbitrarily grab the first one
  keep_legend_name <- names(matrix_list)[1]

  heatmap_list <- matrix_list |>
    purrr::imap(
      \(mat, name) create_single_heatmap(
        mat,
        name,
        column_title,
        labels_font_size,
        keep_legend_name,
        col_fun,
        ...
      )
    ) |>
    # concatenate vertically into HeatmapList object
    purrr::reduce(ComplexHeatmap::`%v%`)

  return(heatmap_list)
}


#' Determine the label font size based on # characters in cell type labels
#'
#' @param input_labels Vector of cell type labels
#'
#' @return Font size numeric as determined by longest cell type label
find_label_size <- function(input_labels) {
  longest_name <- input_labels |>
    stringr::str_length() |>
    max()

  labels_font_size <- dplyr::case_when(
    longest_name < 35 ~ 9.5,
    longest_name < 45 ~ 8.5,
    longest_name < 60 ~ 7.5,
    .default = 6.5
  )

  return(labels_font_size)
}
```


```{r}
#' Function to calculate optimal heatmap plot view height
#'
#' @param row_names Vector of rownames
#' @param col_names Vector of column names
#' @param n_spacers Number of spacers between stacked heatmaps
#' @param spacer_size Spacer size in inches
#' @param min_height Minimize height required. Default 4"
#'
#' @return Heatmap height in inches
calculate_plot_height <- function(
    row_names,
    col_names,
    n_spacers,
    spacer_size = heatmap_padding,
    min_height = 4) {
  # first, based on number of cells in row_names:
  #   6 cell types to an inch
  heat_height <- length(row_names) / 6

  # next, based on nchar of col_names:
  #   10 characters to an inch
  # using stringr::str_length() in case col_names is a factor vector
  heat_height <- heat_height + max(stringr::str_length(col_names)) / 10

  # finally, add in any padding
  heat_height <- heat_height + n_spacers * spacer_size

  return(
    max(c(min_height, heat_height))
  )
}
```


```{r, message = FALSE, warning = FALSE, echo = FALSE}
# define library and sce object
library_id <- params$library
processed_sce <- params$processed_sce

# check for annotation methods
has_singler <- "singler" %in% metadata(processed_sce)$celltype_methods
has_cellassign <- "cellassign" %in% metadata(processed_sce)$celltype_methods
has_submitter <- "submitter" %in% metadata(processed_sce)$celltype_methods &&
  !all(is.na(processed_sce$submitter_celltype_annotation)) # make sure they aren't all NA

# check for umap
has_umap <- "UMAP" %in% reducedDimNames(processed_sce)

# what celltypes are available?
available_celltypes <- c(
  ifelse(has_submitter, "Submitter", NA),
  ifelse(has_singler, "SingleR", NA),
  ifelse(has_cellassign, "CellAssign", NA)
) |>
  na.omit() |>
  as.character()

# was cellassign run?
cellassign_not_run <- check_cellassign_not_run(
  has_cellassign,
  colData(processed_sce)
)

# This variable need to be defined for edge conditions when certain cell types
#  aren't available:
#  When we have chunks with `eval=<has_method>`, knitr seems to need all other
#  variables used in chunk params defined, including plot dimension variables
#  which would have only been created in chunks which also had an associated
#  eval=<has_method>`. Since those chunks won't have been eval'd if FALSE, we
#  need to define this variable to prevent errors, but this value will never
#  actually be used.
plot_height <- 1

# Determine if library is multiplexed:
#  sample_id should be defined with length > 1
sample_id <- metadata(processed_sce)$sample_id
has_multiplex <- length(sample_id) > 1

# determine UMAP point sizing
umap_points_sizes <- determine_umap_point_size(ncol(processed_sce))
umap_point_size <- umap_points_sizes[1]
umap_facet_point_size <- umap_points_sizes[2]
```

<!-- If multiplexed, open with warning  --> 
```{r, eval = has_multiplex, results='asis'}
# convert sample id to bullet separated list
multiplex_samples <- paste0("<li>", paste(sample_id, collapse = "</li><li>", "</li>"))
glue::glue("
 <div class=\"alert alert-warning\">

 This library is multiplexed and contains data from more than one sample.
 Data from the following samples are included in this library:

    {multiplex_samples}

  </div>
")
```

This report contains a summary of cell type annotation results for library `r library_id`.
The goal of this report is to provide more detailed information about cell type annotation results as an initial evaluation of their quality and reliability.

Performing cell type annotation is an inherently challenging task with high levels of uncertainty, especially when using automated annotation methods.
One way to address this is to use multiple cell type annotation approaches and compare the results, as we have begun to do in this report.

When multiple methods annotate a given cell as the same or similar cell type, this may qualitatively indicate a more robust annotation.

Note that the contents of this report will vary based on which cell type annotations are present.
Further, be aware that different cell type annotation methods may assign different labels to the same or similar cell type (e.g., the different string representations `B cell naive` and `Naive B cell`), due to use of different underlying reference datasets.
Please note that all cell type annotation reference datasets are derived from normal (not tumor) tissue.

<!--Note that the specific contents of this report will depend on which cell type annotations are present.-->
**This library contains the following cell type annotations:**

```{r results='asis'}
# build up this string as we go:
methods_text <- ""

if (has_submitter) {
  methods_text <- glue::glue(
    "{methods_text}
    * Submitter-provided cell type annotation, generated by the original lab which produced this data.\n"
  )
}

if (has_singler) {
  methods_text <- glue::glue(
    "{methods_text}
    * Annotations from [`SingleR`](https://bioconductor.org/packages/release/bioc/html/SingleR.html), a reference-based approach ([Looney _et al._ 2019](https://doi.org/10.1038/s41590-018-0276-y)).
    The `{metadata(processed_sce)$singler_reference}` dataset, obtained from the [`celldex` package](http://bioconductor.org/packages/release/data/experiment/html/celldex.html), was used for reference annotations.\n"
  )
}

if (has_cellassign) {
  organs <- metadata(processed_sce)$cellassign_reference_organs |>
    stringr::str_to_lower()
  # split up and add an `and` before the final organ
  organs_string <- stringr::str_split_1(organs, pattern = ", ") |>
    stringr::str_flatten_comma(last = ", and ")

  ref_name <- metadata(processed_sce)$cellassign_reference

  methods_text <- glue::glue(
    "{methods_text}
    * Annotations from [`CellAssign`](https://github.com/Irrationone/cellassign), a marker-gene-based approach ([Zhang _et al._ 2019](https://doi.org/10.1038/s41592-019-0529-1)).
    Marker genes for cell types were obtained from [PanglaoDB](https://panglaodb.se/) and compiled into a reference named `{ref_name}`.
    This reference includes the following organs and tissue compartments: {organs_string}.\n"
  )
}

glue::glue("{methods_text}")
```

```{r eval=cellassign_not_run, results='asis'}
glue::glue("
   <div class=\"alert alert-info\">
   Cell type annotation with [`CellAssign`](https://github.com/Irrationone/cellassign), a marker-gene-based approach ([Zhang _et al._ 2019](https://doi.org/10.1038/s41592-019-0529-1)), was attempted but not run due to an insufficient number of cells.
  </div>
")
```


<!------- Call the celltypes_qc report section from the main report ----------->
```{r, child='celltypes_qc.rmd'}
```


# Cell label comparison plots

This section displays heatmaps comparing cell labels from various methods.

We use the [Jaccard similarity index](https://en.wikipedia.org/wiki/Jaccard_index) to display the agreement between between pairs of labels assigned by different annotation methods.

The Jaccard index reflects the degree of overlap between the two labels and ranges from 0 to 1.

* If the labels are assigned to identical sets of cells, the Jaccard index will be 1.
* If the labels are assigned to completely non-overlapping sets of cells, the Jaccard index will be 0.

High agreement between methods qualitatively indicates higher confidence in the cell type annotation.





<!-- If multiplexed, show info alert instead of this heatmap --> 
```{r, eval = has_multiplex, results='asis'}
glue::glue("
 <div class=\"alert alert-info\">

  No heatmap comparing cluster and cell type annotation labels is shown because the presence of multiple samples may confound cluster assignment.

  </div>
")
```

<!-- If not multiplexed, show the header, text, and heatmap --> 
```{r, eval = !has_multiplex, results='asis'}
glue::glue("
  ## Unsupervised clustering

  Here we show the labels from unsupervised clustering compared to cell type annotations.
  Cluster assignment was performed using the `{metadata(processed_sce)$cluster_algorithm}` algorithm.
")

# Calculate all jaccard matrices of interest for input to heatmap
jaccard_cluster_matrices <- available_celltypes |>
  stringr::str_to_lower() |>
  purrr::set_names(available_celltypes) |>
  purrr::map(\(name) {
    make_jaccard_matrix(
      celltype_df,
      "cluster",
      glue::glue("{name}_celltype_annotation")
    )
  })

all_celltypes <- jaccard_cluster_matrices |>
  purrr::map(colnames) |>
  unlist()

plot_height <- calculate_plot_height(
  all_celltypes,
  unique(celltype_df$cluster),
  length(jaccard_cluster_matrices) - 1
)
```


```{r, eval = !has_multiplex, fig.height = plot_height, fig.width = 8.5, warning = FALSE}
jaccard_cluster_matrices |>
  create_heatmap_list(
    column_title = "Clusters",
    labels_font_size = find_label_size(all_celltypes),
    ## additional arguments
    column_names_rot = 0
  ) |>
  ComplexHeatmap::draw(
    heatmap_legend_side = "bottom"
  )
```



<!-------------------------- Submitter heatmaps ------------------------------->
```{r, eval = has_submitter & (has_cellassign | has_singler)}
# don't compare submitter to submitter
available_celltypes <- available_celltypes[!(available_celltypes == "Submitter")]

methods_string <- available_celltypes |>
  stringr::str_replace_all("^", "`") |>
  stringr::str_replace_all("$", "`") |>
  stringr::str_flatten_comma(", and ")

knitr::asis_output(
  glue::glue(
    "## Submitter-provided annotations

  This section displays heatmaps comparing submitter-provided cell type annotations to those obtained from {methods_string}."
  )
)
```

```{r, eval = has_submitter & (has_cellassign | has_singler)}
# calculate matrices comparing to submitter
jaccard_submitter_matrices <- available_celltypes |>
  stringr::str_to_lower() |>
  purrr::map(\(name) {
    make_jaccard_matrix(
      celltype_df,
      "submitter_celltype_annotation",
      glue::glue("{name}_celltype_annotation")
    )
  }) |>
  purrr::set_names(available_celltypes)

# how many cell types?
all_celltypes <- jaccard_submitter_matrices |>
  purrr::map(colnames) |>
  unlist()

plot_height <- calculate_plot_height(
  all_celltypes,
  unique(celltype_df$submitter_celltype_annotation),
  length(jaccard_submitter_matrices) - 1
)
```

```{r, eval = has_submitter & (has_cellassign | has_singler), fig.height = plot_height, fig.width = 8.5, warning = FALSE}
jaccard_submitter_matrices |>
  create_heatmap_list(
    column_title = "Submitter-provided annotations",
    labels_font_size = find_label_size(all_celltypes),
    # additional arguments
    column_names_rot = 90
  ) |>
  ComplexHeatmap::draw(
    heatmap_legend_side = "bottom"
  )
```


<!---------------------- SingleR/CellAssign heatmap  -------------------------->
```{r, eval = has_cellassign & has_singler, fig.height=7, fig.width=8}
knitr::asis_output("
## Automated annotations

This section displays a heatmap directly comparing `SingleR` and `CellAssign` cell type annotations.
Note that due to different annotations references, these methods may use different names for similar cell types.
")

# Set plot dimensions based on number of SingleR cell types
all_celltypes <- levels(celltype_df$singler_celltype_annotation)
plot_height <- calculate_plot_height(
  unique(celltype_df$singler_celltype_annotation),
  unique(celltype_df$cellassign_celltype_annotation),
  0
)
```


```{r, eval = has_cellassign & has_singler, fig.height = plot_height, fig.width = 8.5}
# Calculate jaccard matrix
singler_cellassign_matrix <- make_jaccard_matrix(
  celltype_df,
  "singler_celltype_annotation",
  "cellassign_celltype_annotation"
)

create_single_heatmap(
  singler_cellassign_matrix,
  row_title = "CellAssign annotations",
  column_title = "SingleR annotations",
  labels_font_size = find_label_size(all_celltypes),
  keep_legend_name = "CellAssign annotations",
  col_fun = heatmap_col_fun,
  # additional arguments
  column_names_rot = 90
) |>
  ComplexHeatmap::draw(
    heatmap_legend_side = "bottom"
  )
```

<!---------------------------- Diagnostic plots  ------------------------------>
```{r, eval = has_singler | has_cellassign }
knitr::asis_output("
# Quality assessments of automated annotations
")
```


<!------------------------ SingleR delta median  ------------------------------>
```{r, eval = has_singler}
knitr::asis_output("
## `SingleR` annotations

To assess the quality of `SingleR` cell type annotations, we use the delta median statistic.

- Delta median is calculated for each cell as the difference between the `SingleR` score of the annotated cell type label and the median score of the other cell type labels in the reference dataset.
- Higher delta median values indicate higher quality cell type annotations.
  - Values can range from 0-1.
  - Note that there is no universal threshold for calling absolute high vs. low quality, as described in the [`SingleR` book section on 'Annotation diagnostics'](https://bioconductor.org/books/release/SingleRBook/annotation-diagnostics.html#annotation-diagnostics).

You can interpret this plot as follows:

- Each point represents the delta median statistic of a given cell whose `SingleR` annotation is shown on the y-axis.
- The point style indicates `SingleR`'s quality assessment of the annotation:
  - High-quality cell annotations are shown as closed points.
  - Low-quality cell annotations are shown as open points.
  In other sections of this report, these cells are labeled as `Unknown cell types`.
  - For more information on how `SingleR` calculates annotation quality, please refer to [this `SingleR` documentation](https://rdrr.io/bioc/SingleR/man/pruneScores.html).
- Diamonds represent the median of the delta median statistic specifically among high-quality annotations for the given cell type annotation.
")
```


```{r, eval = has_singler, warning=FALSE, message=FALSE}
# Prepare SingleR scores for plot

# extract scores into matrix
singler_scores <- metadata(processed_sce)$singler_result$scores

# Create data frame for plotting with delta median and the full *non-pruned* cell labels
delta_median_df <- tibble::tibble(
  delta_median = rowMaxs(singler_scores) - rowMedians(singler_scores),
  # Need to grab the non-pruned label for this plot
  full_labels = metadata(processed_sce)$singler_result$labels,
  # if pruned.labels are NA ==> low confidence
  # so, negate for this variable:
  confident = !is.na(metadata(processed_sce)$singler_result$pruned.labels)
) |>
  dplyr::mutate(
    confident = ifelse(confident, "High-quality", "Low-quality")
  )

# If ontologies were used for `full_labels`, we'll need to map back to cell type names
#  for the plot itself.
if ("singler_celltype_ontology" %in% names(celltype_df)) {
  # we use inner_join b/c the above tibble does NOT contain "Unknown cell type", which
  #  we do not want to display here
  delta_median_df <- delta_median_df |>
    dplyr::inner_join(
      tibble::tibble(
        full_labels = celltype_df$singler_celltype_ontology,
        celltype = celltype_df$singler_celltype_annotation
      ) |> dplyr::distinct()
    ) |>
    dplyr::select(-full_labels)
} else {
  # otherwise, full_labels already contain what we want to plot, so just rename it
  delta_median_df <- delta_median_df |>
    dplyr::rename(celltype = full_labels)
  # still need to add levels:
  # Note that the level "Unknown cell type" will still be present, but there is no such value in the data, so it won't matter
  levels(delta_median_df$celltype) <- levels(celltype_df$singler_celltype_annotation)
}

# Ensure we have no "Unknown cell type" values left:
if (any(delta_median_df$celltype == "Unknown cell type")) {
  stop("Failed to process SingleR data for diagnostic plot.")
}

# add column with ordered levels with wrapped labels for visualization
delta_median_df$annotation_wrapped <- factor(
  delta_median_df$celltype,
  # rev() so large groups are at the TOP of the plot
  levels = rev(levels(delta_median_df$celltype)),
  labels = rev(stringr::str_wrap(levels(delta_median_df$celltype), 30))
)

# Subset the data to just confident points for median+/-IQR
delta_median_confident_df <- delta_median_df |>
  dplyr::filter(confident == "High-quality")

# Determine height for plot area based on number of cells
plot_height <- length(unique(delta_median_df$celltype)) / 2.5
```

```{r, eval = has_singler, warning=FALSE, message=FALSE, fig.height = plot_height, fig.width = 8}
# Plot delta_median across celltypes colored by pruning
ggplot(delta_median_df) +
  aes(
    x = delta_median,
    y = annotation_wrapped,
    shape = confident,
    alpha = confident
  ) +
  ggforce::geom_sina(
    size = 0.8,
    color = "black", # will get applied to all confident points and non-confident outline
    fill = "white", # will apply to non-confident fill only
    position = position_dodge(width = 0.05) # Keep both types of points mostly in line
  ) +
  # Handle points aesthetics:
  #  confident are closed black with alpha = 0.5
  #  not confident are open black with alpha = 1
  scale_shape_manual(values = c(19, 21)) +
  scale_alpha_manual(values = c(0.5, 1)) +
  labs(
    x = "Delta median statistic",
    y = "Cell type annotation",
    shape = "Cell type annotation quality"
  ) +
  # add median diamond for confident points only
  stat_summary(
    data = delta_median_confident_df,
    color = "red",
    geom = "point",
    fun = "median",
    shape = 18,
    size = 2.25,
    alpha = 0.9
  ) +
  guides(
    alpha = FALSE,
    shape = guide_legend(override.aes = list(size = 1.5, alpha = 0.55))
  ) +
  theme(
    legend.position = "bottom"
  )
```


<!---------------------- CellAssign probability plot  ------------------------->
```{r, eval = has_cellassign}
knitr::asis_output("
## `CellAssign` annotations

To assess the quality of `CellAssign` cell type annotations, we consider the probability associated with the annotated cell type.
These probabilities are provided directly by `CellAssign`:

- `CellAssign` first calculates the probability of each cell being annotated as each cell type present in the reference.
- `CellAssign` then annotates cells by selecting the cell type with the highest probability among all cell types considered.
- These probabilities range from 0 to 1, with larger values indicating greater confidence in a given cell type label.
We therefore expect reliable labels to have values close to 1.

The plot below shows the distribution of `CellAssign`-calculated probabilities for the final cell type labels.
Line segments represent individual values that comprise each distribution.

For cell types with 2 or fewer labeled cells, only the individual value line segments are shown.
Line segments are also taller for any cell type label with 5 or fewer cells.
")

# Determine height in inches for plot area
plot_height <- ceiling(length(unique(celltype_df$cellassign_celltype_annotation)) / 2.5)
```


```{r, eval = has_cellassign, warning=FALSE, message=FALSE, fig.height = plot_height, fig.width = 8}
# define bandwidth for all calculations
density_bw <- 0.03

# find the maximum density across all distributions, and
#  save the maximum for determining geom_segment height
y_max <- celltype_df$cellassign_max_prediction |>
  split(celltype_df$cellassign_celltype_annotation) |>
  # make sure we get rid of any small groups
  purrr::discard(\(x) sum(is.finite(x)) <= 2) |>
  # remove any NA's that may have slipped in
  purrr::map_dbl(
    \(x) max(density(x, bw = density_bw, na.rm = TRUE)$y)
  ) |>
  max(na.rm = TRUE)

# add count to celltype_df for setting alpha and yend values
celltype_df <- celltype_df |>
  dplyr::add_count(cellassign_celltype_annotation)

# make the plot!
ggplot(celltype_df) +
  aes(x = cellassign_max_prediction) +
  geom_density(
    bw = density_bw,
    fill = "grey65",
    linewidth = 0.25,
    bounds = c(0, 1)
  ) +
  geom_segment(
    aes(
      # set alpha to vary based on the number of points in the row such that
      #  rows with more points are more transparent
      alpha = pmax(0.2, 1 - 0.01 * n),
      xend = cellassign_max_prediction,
      # set yend as either 0 for rows with many points, or y_max/2.5 for
      #  rows with few points
      yend = ifelse(n > 5, 0, y_max / 2.5),
      y = -Inf
    ),
    color = "blue"
  ) +
  labs(
    x = "Probability of annotated cell type",
    y = "Cell type annotation"
  ) +
  scale_x_continuous(limits = c(0, 1)) +
  scale_y_continuous(expand = c(0.1, 0.1)) +
  scale_alpha_identity() +
  facet_grid(
    rows = vars(cellassign_celltype_annotation),
    switch = "y"
  ) +
  theme(
    strip.background = element_blank(),
    strip.text.y.left = element_text(
      angle = 0,
      hjust = 1
    ),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.spacing = unit(0.02, "in")
  )
```


# Session Info
<details>
<summary>R session information</summary>
```{r session_info}
if (requireNamespace("sessioninfo", quietly = TRUE)) {
  sessioninfo::session_info()
} else {
  sessionInfo()
}
```
</details>
