From c53fac57bf300b39f773612be32bc338fafa265a Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Thu, 9 Jan 2025 09:53:07 -0600 Subject: [PATCH 01/15] initiate notebook for looking at tumor cell states --- .../06-tumor-cell-state-assignment.Rmd | 273 ++++++++++++++++++ .../scripts/utils/tumor-validation-helpers.R | 5 +- 2 files changed, 276 insertions(+), 2 deletions(-) create mode 100644 analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd diff --git a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd new file mode 100644 index 000000000..9acf0c2c3 --- /dev/null +++ b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd @@ -0,0 +1,273 @@ +--- +title: "Tumor cell state annotation: `SCPCL000822` and `SCPCL000824`" +author: Ally Hawkins +date: "`r Sys.Date()`" +output: + html_document: + toc: true + toc_depth: 3 + code_folding: "hide" +--- + +## Setup + +```{r packages} +suppressPackageStartupMessages({ + # load required packages + library(SingleCellExperiment) + library(ggplot2) +}) + +# Set default ggplot theme +theme_set( + theme_bw() +) + +# set seed +set.seed(2024) +``` + + +```{r base paths} +# The base path for the OpenScPCA repository, found by its (hidden) .git directory +repository_base <- rprojroot::find_root(rprojroot::is_git_root) + +# The current data directory, found within the repository base directory +data_dir <- file.path(repository_base, "data", "current", "SCPCP000015") + +# The path to this module +module_base <- file.path(repository_base, "analyses", "cell-type-ewings") + +# path to marker genes file +marker_genes_file <- file.path(module_base, "references", "tumor-cell-state-markers.tsv") +``` + +```{r} +# source in helper functions for make_jaccard_matrix() and jaccard() +jaccard_functions <- file.path(module_base, "scripts", "utils", "jaccard-functions.R") +source(jaccard_functions) + +# source in helper functions: plot_density() and calculate_sum_markers() +validation_functions <- file.path(module_base, "scripts", "utils", "tumor-validation-helpers.R") +source(validation_functions) +``` + +## Prepare data + +```{r} +# sample and library ids +sample_ids <- c("SCPCS000490", "SCPCS000492") +library_ids <- c("SCPCL000822", "SCPCL000824") + +# define input sce files +sce_file_names <- glue::glue("{library_ids}_processed.rds") +sce_files <- file.path(data_dir, sample_ids, sce_file_names) |> + purrr::set_names(library_ids) + +# path to cell type classification files +singler_results_dir <- file.path(module_base, "results", "aucell_singler_annotation") +classification_file_names <- glue::glue("{library_ids}_singler-classifications.tsv") +classification_files <- file.path(singler_results_dir, sample_ids, classification_file_names) |> + purrr::set_names(library_ids) + +# path to clustering results files +cluster_results_dir <- file.path(module_base, "results", "clustering", sample_ids) +cluster_file_names <- glue::glue("{library_ids}_cluster-results.tsv") +cluster_files <- file.path(cluster_results_dir, cluster_file_names) |> + purrr::set_names(library_ids) +``` + +```{r} +# read in both SCEs +sce_list <- sce_files |> + purrr::map(readr::read_rds) + +# read in classification data frame with SingleR results and combine into one +classification_df <- classification_files |> + purrr::map(readr::read_tsv) |> + dplyr::bind_rows(.id = "library_id") |> + dplyr::mutate( + # first grab anything that is tumor and label it tumor + # NA should be unknown + singler_annotation = dplyr::case_when( + stringr::str_detect(singler_annotation, "tumor") ~ "tumor", + is.na(singler_annotation) ~ "unknown", # make sure to separate out unknown labels + .default = singler_annotation + ) |> + forcats::fct_relevel("tumor", after = 0), + # get the top cell types for plotting later + singler_lumped = singler_annotation |> + forcats::fct_lump_n(7, other_level = "All remaining cell types", ties.method = "first") |> + forcats::fct_infreq() |> + forcats::fct_relevel("All remaining cell types", after = Inf) + ) + +# read in clustering results and select cluster assignments of interest +# here we keep leiden-mod, 0.5 res, 20 nn for both libraries +cluster_df <- cluster_files |> + purrr::map(readr::read_tsv) |> + dplyr::bind_rows(.id = "library_id") |> + dplyr::filter( + cluster_method == "leiden_mod", + nn == 20, + resolution == 0.5 + ) |> + dplyr::select( + barcodes = cell_id, + library_id, + cluster + ) +``` + +```{r} +# read in marker genes table +marker_genes_df <- readr::read_tsv(marker_genes_file, show_col_types = FALSE) |> + dplyr::select(cell_state, ensembl_gene_id, gene_symbol) + +# get list of all cell types found +cell_states <- unique(marker_genes_df$cell_state) + +# get the sum of expression of all genes for each cell type +gene_exp_df <- sce_list |> + purrr::map(\(sce) { + cell_states |> + purrr::map(\(state){ + calculate_sum_markers(marker_genes_df, sce, state, cell_state) + }) |> + purrr::reduce(dplyr::inner_join, by = "barcodes") + }) |> + dplyr::bind_rows(.id = "library_id") + +# get umap embeddings and combine into a data frame with gene exp, cluster, and cell type assignments +umap_df <- sce_list |> + purrr::map(\(sce){ + df <- sce |> + scuttle::makePerCellDF(use.dimred = "UMAP") |> + # replace UMAP.1 with UMAP1 and get rid of excess columns + dplyr::select(barcodes, UMAP1 = UMAP.1, UMAP2 = UMAP.2) + }) |> + dplyr::bind_rows(.id = "library_id") |> + # add in classifications + dplyr::left_join(classification_df, by = c("library_id", "barcodes")) |> + dplyr::left_join(gene_exp_df, by = c("barcodes", "library_id")) |> + # add in cluster assignments + dplyr::left_join(cluster_df, by = c("barcodes", "library_id")) +``` + + +assign cells as tumor cells if they belong to a cluster with high tumor marker gene expression + +![SCPCL000822](../plots/SCPCL000822_cluster-marker-gene-exp.png) +![SCPCL000824](../plots/SCPCL000824_cluster-marker-gene-exp.png) +```{r, fig.height=10} +library_ids |> + purrr::map(\(id){ + + + p1 <- cluster_classification_df |> + dplyr::filter(library_id == id) |> + ggplot(aes(x = UMAP1, y = UMAP2, color = singler_lumped)) + + geom_point(alpha = 0.5, size = 0.1) + + theme( + aspect.ratio = 1 + ) + + scale_color_brewer(palette = "Dark2") + + labs(title = id) + + guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5))) + + p2 <- cluster_classification_df |> + dplyr::filter(library_id == id) |> + ggplot(aes(x = UMAP1, y = UMAP2, color = as.factor(cluster))) + + geom_point(alpha = 0.5, size = 0.1) + + theme( + aspect.ratio = 1 + ) + + #scale_color_brewer(palette = "Dark2") + + labs(title = id, color = "cluster") + + guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5))) + + patchwork::wrap_plots(p1, p2, nrow = 2) + }) +``` + + +For SCPCL000822, clusters 1, 3, and 4 are tumor +For SCPCL000824, clustrs 2, 3, 4, 5, 6, 7 are tumor + +```{r} +cluster_classification_df <- umap_df |> + dplyr::mutate( + cluster_classification = dplyr::case_when( + library_id == "SCPCL000822" & cluster %in% c(1, 3, 4) ~ "tumor", + library_id == "SCPCL000822" & cluster == 6 ~ "endothelial cell", + # cluster 2 has predominantly chondrocytes and has MSC marker gene expression + library_id == "SCPCL000822" & cluster == 2 ~ "chondrocyte", + # clusters with high "immune" marker gene expression are macrophage + library_id == "SCPCL000822" & cluster == 5 ~ "macrophage", + # cluster 8 has high MSC marker gene expression and a mix of MSC like cell types + library_id == "SCPCL000822" & cluster == 7 ~ "mesenchymal-like cell", + library_id == "SCPCL000824" & cluster %in% c(2, 3, 4, 5, 6, 7) ~ "tumor", + library_id == "SCPCL000824" & cluster == 8 ~ "endothelial cell", + # cluster 9 has high immune markers and is mostly macrohpages + library_id == "SCPCL000824" & cluster == 9 ~ "macrophage", + # cluster 1 has high MSC marker gene expression and a mix of MSC like cell types + library_id == "SCPCL000824" & cluster == 1 ~ "mesenchymal-like cell", + # if they didn't have high expression of any markers, we label them as unknown + .default = "unknown" + ) + ) +``` + +```{r} +library_ids |> + purrr::map(\(id){ + cluster_classification_df |> + dplyr::filter(library_id == id) |> + ggplot(aes(x = UMAP1, y = UMAP2, color = cluster_classification)) + + geom_point(alpha = 0.5, size = 0.1) + + theme( + aspect.ratio = 1 + ) + + labs(title = id) + + scale_color_brewer(palette = "Dark2") + + guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5))) + }) +``` + +```{r} +library_ids |> + purrr::map(\(id){ + cluster_classification_df |> + dplyr::filter(library_id == id) |> + ggplot(aes(x = UMAP1, y = UMAP2, color = proliferative_sum)) + + geom_point(alpha = 0.5, size = 0.1) + + theme( + aspect.ratio = 1 + ) + + labs(title = id) + + scale_color_viridis_c() + }) +``` + + +pull out just tumor cells + +```{r} +tumor_cells_df <- cluster_classification_df |> + dplyr::filter(cluster_classification == "tumor") + +library_ids |> + purrr::map(\(id){ + tumor_cells_df |> + dplyr::filter(library_id == id) |> + ggplot(aes(x = UMAP1, y = UMAP2, color = `EWS-low_sum`)) + + geom_point(alpha = 0.5, size = 0.1) + + theme( + aspect.ratio = 1 + ) + + labs(title = id) + + scale_color_viridis_c() + }) +``` + + diff --git a/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R b/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R index 965854630..01b4e5899 100644 --- a/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R +++ b/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R @@ -123,10 +123,11 @@ create_marker_gene_df <- function( # output is a data frame with barcodes and `{cell_type}_sum` calculate_sum_markers <- function(marker_genes_df, sce, - type) { + type, + cell_type_column = cell_type) { # get list of marker genes to use marker_genes <- marker_genes_df |> - dplyr::filter(cell_type == type) |> + dplyr::filter({{cell_type_column}} == type) |> dplyr::pull(ensembl_gene_id) # get the gene expression counts for all marker genes From 988db2cabc8bbd26397125202809d803f622de93 Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Thu, 9 Jan 2025 09:53:22 -0600 Subject: [PATCH 02/15] add some helpful images for defining tumor cells --- .../SCPCL000822_cluster-marker-gene-exp.png | Bin 0 -> 171479 bytes .../SCPCL000824_cluster-marker-gene-exp.png | Bin 0 -> 176553 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 analyses/cell-type-ewings/plots/SCPCL000822_cluster-marker-gene-exp.png create mode 100644 analyses/cell-type-ewings/plots/SCPCL000824_cluster-marker-gene-exp.png diff --git a/analyses/cell-type-ewings/plots/SCPCL000822_cluster-marker-gene-exp.png b/analyses/cell-type-ewings/plots/SCPCL000822_cluster-marker-gene-exp.png 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z6reu{nHM8P{onWUw}>MhVAwb@U!MQ^Oh`F>Ilw9%|Md6&jLq*$3L@dBsd50^&;R>< w@FKK;|GNozIsZ2jr_=2JM&h55cz=T0ysjRkehrlX1^$x|mAzjmtoQ8y0XeUkBLDyZ literal 0 HcmV?d00001 From 294b8dd8754e0f668eacf6446446ccf20eba740c Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Fri, 10 Jan 2025 11:41:59 -0600 Subject: [PATCH 03/15] subclustering and more plots --- .../06-tumor-cell-state-assignment.Rmd | 385 ++++++++++++++++-- .../scripts/utils/tumor-validation-helpers.R | 6 +- 2 files changed, 362 insertions(+), 29 deletions(-) diff --git a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd index 9acf0c2c3..86092081c 100644 --- a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd +++ b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd @@ -1,5 +1,5 @@ --- -title: "Tumor cell state annotation: `SCPCL000822` and `SCPCL000824`" +title: "Explore expression of marker genes for tumor cell states" author: Ally Hawkins date: "`r Sys.Date()`" output: @@ -9,6 +9,15 @@ output: code_folding: "hide" --- +In this notebook we look at expression of potential markers for tumor cell states in two libraries from `SCPCP000015`: `SCPCL000822` and `SCPCL000824`. +The expression of specific marker genes are then used to classify tumor cells into three different tumor cell states: `EWS-low`, `EWS-mid`, and `EWS-high`. + +We did this by: + +- Evaluating marker gene expression for different tumor cell states across all cells. +- Identifying tumor cells and getting new cluster assignments for just tumor cells. +- Looking at expression of tumor cell state marker genes across clusters and individual cells. + ## Setup ```{r packages} @@ -20,7 +29,7 @@ suppressPackageStartupMessages({ # Set default ggplot theme theme_set( - theme_bw() + theme_classic() ) # set seed @@ -40,13 +49,17 @@ module_base <- file.path(repository_base, "analyses", "cell-type-ewings") # path to marker genes file marker_genes_file <- file.path(module_base, "references", "tumor-cell-state-markers.tsv") + +# gene signatures +aynaud_file <- file.path(module_base, "references", "gene_signatures", "aynaud-ews-targets.tsv") +wrenn_file <- file.path(module_base, "references","gene_signatures", "wrenn-nt5e-genes.tsv") + +# path to save annotations tsv in results +results_dir <- file.path(module_base, "results", "tumor-cell-state-classifications") +fs::dir_create(results_dir) ``` ```{r} -# source in helper functions for make_jaccard_matrix() and jaccard() -jaccard_functions <- file.path(module_base, "scripts", "utils", "jaccard-functions.R") -source(jaccard_functions) - # source in helper functions: plot_density() and calculate_sum_markers() validation_functions <- file.path(module_base, "scripts", "utils", "tumor-validation-helpers.R") source(validation_functions) @@ -77,7 +90,14 @@ cluster_files <- file.path(cluster_results_dir, cluster_file_names) |> purrr::set_names(library_ids) ``` -```{r} +Below we set up the data for plotting throughout this notebook. + +1. The output from `aucell-singler-annotation.sh` is read in and annotations are lumped so that we can easily plot the top cell types. +2. Clustering results from `evaluate-clusters.sh` is read in and subset to the desired parameters we would like to use for assigning clusters (leiden, modularity, resolution 0.5, nearest neighbors 20). +3. The marker genes for tumor cell states and custom gene signatures are used to calculate the sum of all marker genes in each gene set for each library. +4. UMAP embeddings for each library is pulled out into a data frame and combined with `SingleR` annotations, cluster assignments, and marker gene expression for all marker gene sets. + +```{r, message=FALSE} # read in both SCEs sce_list <- sce_files |> purrr::map(readr::read_rds) @@ -119,20 +139,32 @@ cluster_df <- cluster_files |> ) ``` -```{r} +```{r, message=FALSE} # read in marker genes table marker_genes_df <- readr::read_tsv(marker_genes_file, show_col_types = FALSE) |> dplyr::select(cell_state, ensembl_gene_id, gene_symbol) -# get list of all cell types found -cell_states <- unique(marker_genes_df$cell_state) +# get individual gene signatures +aynaud_genes <- readr::read_tsv(aynaud_file) |> + dplyr::mutate(cell_state = "aynaud-ews-high") |> + tidyr::drop_na() + +wrenn_genes <- readr::read_tsv(wrenn_file) |> + dplyr::mutate(cell_state = "wrenn-ews-low") |> + tidyr::drop_na() + +# combine all genes into a single df +all_markers_df <- dplyr::bind_rows(list(marker_genes_df, aynaud_genes, wrenn_genes)) + +# get list of all cell states/ gene lists +cell_states <- unique(all_markers_df$cell_state) -# get the sum of expression of all genes for each cell type +# get the sum of expression of all genes for each cell state gene_exp_df <- sce_list |> purrr::map(\(sce) { cell_states |> purrr::map(\(state){ - calculate_sum_markers(marker_genes_df, sce, state, cell_state) + calculate_sum_markers(all_markers_df, sce, state, cell_state) }) |> purrr::reduce(dplyr::inner_join, by = "barcodes") }) |> @@ -154,16 +186,24 @@ umap_df <- sce_list |> dplyr::left_join(cluster_df, by = c("barcodes", "library_id")) ``` +## Identify tumor cells -assign cells as tumor cells if they belong to a cluster with high tumor marker gene expression +To label tumor cell states, we first want to pull out the cells that are tumor cells and then look at the expression of marker genes for each tumor cell state in those cells. +We have used a variety of methods to identify tumor cells so we will look at the output from two orthogonal approaches to identify the group of tumor cells we want to further classify into cell states. +Let's start by looking at the intersection of tumor cells assigned by the `aucell-singler-annotation.sh` workflow and the clusters obtained from the `evaluate-clusters.sh` workflow that have high expression of tumor marker genes. + +Below are two plots that we generated as part of the `evaluate-clusters.sh` workflow that tells us which clusters express tumor marker genes for each of these libraries. +We can use this information to help identify tumor cells to classify. ![SCPCL000822](../plots/SCPCL000822_cluster-marker-gene-exp.png) + ![SCPCL000824](../plots/SCPCL000824_cluster-marker-gene-exp.png) + + ```{r, fig.height=10} library_ids |> purrr::map(\(id){ - p1 <- cluster_classification_df |> dplyr::filter(library_id == id) |> ggplot(aes(x = UMAP1, y = UMAP2, color = singler_lumped)) + @@ -190,9 +230,9 @@ library_ids |> }) ``` - -For SCPCL000822, clusters 1, 3, and 4 are tumor -For SCPCL000824, clustrs 2, 3, 4, 5, 6, 7 are tumor +Looking at the above plots, we can assign some general cell types using clusters. +This is the same approach we took in `05-cluster-exploration.Rmd` but with slightly different parameters. +Based on the UMAPs and the density plots we see that tumor cells are in cluster 1, 3, and 4 for `SCPCL000822` and cluster 2, 3, 4, 5, 6, and 7 for `SCPCL000824`. ```{r} cluster_classification_df <- umap_df |> @@ -234,40 +274,333 @@ library_ids |> }) ``` +Looking at this I think the cells that are classified as tumor cells using the cluster assignments mostly line up with the `SingleR` annotations. +So we can label any cells from clusters that have high tumor marker gene expression as tumor cells. + +### Look at marker gene expression for tumor cell states in all cells + +Below we look at the expression of genes for the tumor cell states across all cells. +This will show us how the expression of these gene sets varies across cells in each library and help ensure we have the correct population of tumor cells. + +The value for each of the marker gene sets corresponds to the sum of the expression of all genes in the given gene list. +(Note that I also looked at mean and it had similar output) + +The gene sets that we use are: + +- `EWS-low`: Marker genes from `tumor-cell-state-markers.tsv` that correspond to the `EWS-low` cell state. +- `EWS-high`: Marker genes from `tumor-cell-state-markers.tsv` that correspond to the `EWS-high` cell state. +- `proliferative` : Marker genes from `tumor-cell-state-markers.tsv` that correspond to the `proliferative` cell state. +Note that a lot of the Ewing sarcoma literature argues that `EWS-FLI1` high cells are proliferative. +- `aynaud-ews-high`: Marker genes from `gene_signatures/aynaud-ews-targets.tsv` that correspond to genes activated by `EWS-FLI1`. +- `wrenn-ews-low`: Marker genes from `gene_signatures/wrenn-nt5e-genes.tsv` that correspond to genes repressed by `EWS-FLI1`. + +```{r, fig.height=7, fig.width=10} +# pull out columns that have sum of marker gene expression +marker_gene_cols <- colnames(cluster_classification_df)[stringr::str_detect(colnames(cluster_classification_df), "_sum$")] + +# look at the expression of all gene sets across both libraries +marker_gene_cols |> + purrr::map(\(geneset){ + cluster_classification_df |> + ggplot(aes(x = UMAP1, y = UMAP2, color = !!sym(geneset))) + + geom_point(alpha = 0.5, size = 0.1) + + facet_wrap(vars(library_id)) + + scale_color_viridis_c() + + theme( + aspect.ratio = 1 + ) + }) |> + patchwork::wrap_plots(ncol = 2) +``` + +It looks like there's a cluster of cells in both libraries that have expression of the `EWS-low` and `wrenn-ews-low` signatures. +Those cells correspond to the cells that we are labeling as "chondrocyte" in `SCPCL000822` and "mesenchymal-like" in `SCPCL000824`. +The chondrocytes also express high markers of mesenchymal-like genes and the literature talks about tumor cells with low `EWS-FLI1` expression being "MSC-like". +I think there's a possibility that these are tumor cells, so let's relabel them. + +```{r} +# create a new tumor classification that takes all tumor clusters plus the clusters with EWS-FLI1 low expression +cluster_classification_df <- cluster_classification_df |> + dplyr::mutate( + tumor_classification = dplyr::case_when( + cluster_classification == "tumor" ~ "tumor", + library_id == "SCPCL000822" & cluster_classification == "chondrocyte" ~ "tumor", + library_id == "SCPCL000824" & cluster_classification == "mesenchymal-like cell" ~ "tumor", + .default = cluster_classification + ) + ) +``` + + +## Classify tumor cell states + +Now that we have identified a subset of cells that are tumor cells, we can attempt to group them into various cell states. +The main thing that we are interested in is grouping cells into levels of `EWS-FLI1` expression. +Let's start by looking at the expression of the gene sets across all tumor cells. + +To do this, we will make a density plot that looks at gene set expression by cluster using the cluster assignments calculated using all cells. + +```{r} +tumor_cells_df <- cluster_classification_df |> + dplyr::filter(tumor_classification == "tumor") +``` + +```{r, fig.height=10} +# density plot showing expression of each gene set by cluster +library_ids |> + purrr::map(\(id){ + + density_df <- tumor_cells_df |> + dplyr::filter(library_id == id) + + marker_gene_cols |> + purrr::map(\(column){ + plot_density( + density_df, + column, + "cluster" + ) + }) |> + patchwork::wrap_plots(ncol = 1) + + patchwork::plot_annotation(id) + + }) +``` + +A few notes from this: + +- It doesn't look like very many cells express proliferative markers, so perhaps that gene set will not be helpful for these libraries? +- There are some very obvious `EWS-FLI1` low groups of cells. +- The `EWS-high` and `aynaud-EWS-high` show clusters that have high expression of `EWS-FLI1` targets, but the `aynaud-EWS-high` appears to have some more separation across clusters. +That tells me that gene signature might be more helpful in classifying cells across the `EWS-FLI1` continuum. + +### Sub cluster tumor cells + +We would expect that tumor cells that are in similar cell states would cluster together. +So here I am going to take just the tumor cells and re-assign clusters. +I'm using the same parameters we use for the whole sample, leiden, modularity, resolution of 0.5, and nearest neighbors of 20. +I think if we really wanted to be stringent we would evaluate clusters here and find the best parameters to use, but I think starting with these is good. + + +```{r} +# get a data frame of all tumor cells and re assigned clusters +subcluster_df <- sce_list |> + purrr::imap(\(sce, id){ + + # pull out the barcodes that are from tumor cells + tumor_cells <- tumor_cells_df |> + dplyr::filter(library_id == id) |> + dplyr::pull(barcodes) + + # filter the sce to only those barcodes + filtered_sce <- sce[ , tumor_cells] + + # grab pcs for clustering + pcs <- reducedDim(filtered_sce) + + # get the clusters + clusters <- bluster::clusterRows( + pcs, + bluster::NNGraphParam( + k = 20, + type = "jaccard", + cluster.fun = "leiden", + cluster.args = list(objective_function = "modularity", + resolution = 0.5) + ) + ) + + # combine into a data frame with barcodes + cluster_df <- data.frame( + barcodes = rownames(pcs), + sub_cluster = clusters + ) + }) |> + # get clusters for both samples in one data frame + dplyr::bind_rows(.id = "library_id") + +# add clusters to tumor cells df for plotting +tumor_cells_df <- tumor_cells_df |> + dplyr::left_join(subcluster_df, by = c("library_id", "barcodes")) +``` + +The first thing we will do is plot the cluster assignments on the UMAP. + ```{r} library_ids |> purrr::map(\(id){ - cluster_classification_df |> + tumor_cells_df |> dplyr::filter(library_id == id) |> - ggplot(aes(x = UMAP1, y = UMAP2, color = proliferative_sum)) + + ggplot(aes(x = UMAP1, y = UMAP2, color = sub_cluster)) + geom_point(alpha = 0.5, size = 0.1) + theme( aspect.ratio = 1 ) + labs(title = id) + - scale_color_viridis_c() + scale_color_brewer(palette = "Dark2") + + guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5))) }) ``` +Now we will look at the marker gene expression across all clusters for each sample. +First using a density plot and then a heatmap. + +```{r, fig.height=10} +library_ids |> + purrr::map(\(id){ + + density_df <- tumor_cells_df |> + dplyr::filter(library_id == id) + + marker_gene_cols |> + purrr::map(\(column){ + plot_density( + density_df, + column, + "sub_cluster" + ) + }) |> + patchwork::wrap_plots(ncol = 1) + + patchwork::plot_annotation(id) + + }) +``` -pull out just tumor cells ```{r} -tumor_cells_df <- cluster_classification_df |> - dplyr::filter(cluster_classification == "tumor") +library_ids |> + purrr::map(\(id){ + + filtered_df <- tumor_cells_df |> + dplyr::filter(library_id == id) + + full_celltype_heatmap(filtered_df, marker_gene_cols, "sub_cluster", plot_name = id) + + }) + +``` + +It looks like for both samples we have some clear division between EWS high and EWS low cells. +Additionally, there are some cells with really strong expression of the EWS high markers and others that look like the genes are expressed but to a lesser degree. +This fits with the findings from the literature that `EWS-FLI1` expression lies on a continuum. +Between these two plots I think we can try and group cells into EWS low, EWS middle, and EWS high cells. +I don't think the proliferative markers are very informative, so I don't think we can use that to categorize any cells. + +Based on this I would make the following assignments: + +`SCPCL000822`: +`EWS-FLI1` low - 2 +`EWS-FLI1` middle - 1 & 4 +`EWS-FLI1` high - 3 + +`SCPCL000824` : +`EWS-FLI1` low - 1 +`EWS-FLI1` middle - 6 +`EWS-FLI1` high - 2, 3, 4, 5, 7 + +```{r} +# categorize tumor cells based on EWS expression +tumor_cells_df <- tumor_cells_df |> + dplyr::mutate( + cell_state = dplyr::case_when( + library_id == "SCPCL000822" & sub_cluster == 2 ~ "EWS-low", + library_id == "SCPCL000822" & sub_cluster %in% c(1, 4) ~ "EWS-mid", + library_id == "SCPCL000822" & sub_cluster == 3 ~ "EWS-high", + library_id == "SCPCL000824" & sub_cluster == 1 ~ "EWS-low", + library_id == "SCPCL000824" & sub_cluster %in% c(6, 8) ~ "EWS-mid", + library_id == "SCPCL000824" & sub_cluster %in% c(2, 3, 4, 5, 7) ~ "EWS-high", + ) + ) +``` + +Let's re-plot the heatmap but label by tumor cell state rather than cluster. + +```{r} +library_ids |> + purrr::map(\(id){ + + filtered_df <- tumor_cells_df |> + dplyr::filter(library_id == id) + + full_celltype_heatmap(filtered_df, marker_gene_cols, "cell_state", plot_name = id) + + }) +``` + +I think that looks pretty reasonable. +I'm not sure we can really make more definitive calls given the gene lists that we have, unless we use fancier approaches. +We could try something like `AUCell`, but without a nice bimodal distribution, I don't think we would get anything but low/high cells. + +## Combined classifications + +As a final step, we'll add the tumor cell states back into the full object and look at them on the UMAP. +Here I am plotting the classification of cells based on the cluster because otherwise the normal cells get a little wild with colors and I want to be able to see visualize the tumor cells. + + +```{r} +# select the columns we want to join back into the main classification df +cell_state_classifications <- tumor_cells_df |> + dplyr::select(library_id, barcodes, cell_state, sub_cluster) + +# add cell states to classification +final_classification_df <- cluster_classification_df |> + dplyr::left_join(cell_state_classifications, by = c("library_id", "barcodes")) |> + dplyr::mutate(full_classification = dplyr::if_else(is.na(cell_state), singler_lumped, cell_state), + cluster_classification = dplyr::if_else(is.na(cell_state), cluster_classification, cell_state)) + +# make some umaps library_ids |> purrr::map(\(id){ - tumor_cells_df |> + final_classification_df |> dplyr::filter(library_id == id) |> - ggplot(aes(x = UMAP1, y = UMAP2, color = `EWS-low_sum`)) + + ggplot(aes(x = UMAP1, y = UMAP2, color = cluster_classification)) + geom_point(alpha = 0.5, size = 0.1) + theme( aspect.ratio = 1 ) + labs(title = id) + - scale_color_viridis_c() + scale_color_brewer(palette = "Dark2") + + guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5))) + }) +``` + +```{r} +# export the final classifications in case we want to use them in the future +# save all classification columns +final_classification_df |> + split(final_classification_df$library_id) |> + purrr::iwalk(\(df, id){ + + filename <- glue::glue("{id}_tumor-cell-state-annotations.tsv") + + df |> + dplyr::select( + library_id, + barcodes, + singler_annotation, + aucell_annotation, + cluster, + cluster_classification, + tumor_sub_cluster = sub_cluster, + tumor_cell_state = cell_state, + full_classification + ) |> + readr::write_tsv(file.path(results_dir, filename)) + }) ``` +## Conclusions + +- There is a clear division between cells that are `EWS-FLI` high and `EWS-FLI1` low. +- Most tumor cells in both samples are `EWS-FLI` high, which is consistent with the literature. +- There may be a class of cells that live in between high and low, but if we want to define those we probably need to be more stringent with our approach and identify a mechanism to bin cells based on expression of both `EWS-FLI1` activated and repressed genes, which is outside the scope of this notebook. + +## Session info + +```{r session info} +# record the versions of the packages used in this analysis and other environment information +sessionInfo() +``` diff --git a/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R b/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R index 01b4e5899..51db14975 100644 --- a/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R +++ b/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R @@ -250,9 +250,9 @@ full_celltype_heatmap <- function(classification_df, # create annotation for heatmap annotation <- ComplexHeatmap::columnAnnotation( - singler = classification_df[[annotation_column]], + annotation = classification_df[[annotation_column]], col = list( - singler = colors + annotation = colors ) ) @@ -294,7 +294,7 @@ plot_density <- function(classification_df, ) + labs( x = "Gene set expression", - y = "Cell type annotation", + y = annotation_column, title = geneset_name ) + scale_alpha_identity() + From 2f599effc05a06cce92767f4a7cf0e9ef156d984 Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Fri, 10 Jan 2025 11:42:08 -0600 Subject: [PATCH 04/15] render the notebook --- .../06-tumor-cell-state-assignment.html | 1193 +++++++++++++++++ 1 file changed, 1193 insertions(+) create mode 100644 analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html diff --git a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html new file mode 100644 index 000000000..fbc82c611 --- /dev/null +++ b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html @@ -0,0 +1,1193 @@ + + + + + + + + + + + + + + + +Explore expression of marker genes for tumor cell states + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ + + + + + + + +

In this notebook we look at expression of potential markers for tumor +cell states in two libraries from SCPCP000015: +SCPCL000822 and SCPCL000824. The expression of +specific marker genes are then used to classify tumor cells into three +different tumor cell states: EWS-low, EWS-mid, +and EWS-high.

+

We did this by:

+
    +
  • Evaluating marker gene expression for different tumor cell states +across all cells.
    • +
  • Identifying tumor cells and getting new cluster assignments for just +tumor cells.
    • +
  • Looking at expression of tumor cell state marker genes across +clusters and individual cells.
    • +
+
+

Setup

+
suppressPackageStartupMessages({
+  # load required packages
+  library(SingleCellExperiment)
+  library(ggplot2)
+})
+
+# Set default ggplot theme
+theme_set(
+  theme_classic()
+)
+
+# set seed
+set.seed(2024)
+
# The base path for the OpenScPCA repository, found by its (hidden) .git directory
+repository_base <- rprojroot::find_root(rprojroot::is_git_root)
+
+# The current data directory, found within the repository base directory
+data_dir <- file.path(repository_base, "data", "current", "SCPCP000015")
+
+# The path to this module
+module_base <- file.path(repository_base, "analyses", "cell-type-ewings")
+
+# path to marker genes file
+marker_genes_file <- file.path(module_base, "references", "tumor-cell-state-markers.tsv")
+
+# gene signatures
+aynaud_file <- file.path(module_base, "references", "gene_signatures", "aynaud-ews-targets.tsv")
+wrenn_file <- file.path(module_base, "references","gene_signatures", "wrenn-nt5e-genes.tsv")
+
+# path to save annotations tsv in results 
+results_dir <- file.path(module_base, "results", "tumor-cell-state-classifications")
+fs::dir_create(results_dir)
+
# source in helper functions: plot_density() and calculate_sum_markers()
+validation_functions <- file.path(module_base, "scripts", "utils", "tumor-validation-helpers.R")
+source(validation_functions)
+
+
+

Prepare data

+
# sample and library ids
+sample_ids <- c("SCPCS000490", "SCPCS000492")
+library_ids <- c("SCPCL000822", "SCPCL000824")
+
+# define input sce files
+sce_file_names <- glue::glue("{library_ids}_processed.rds")
+sce_files <- file.path(data_dir, sample_ids, sce_file_names) |>
+  purrr::set_names(library_ids)
+
+# path to cell type classification files
+singler_results_dir <- file.path(module_base, "results", "aucell_singler_annotation")
+classification_file_names <- glue::glue("{library_ids}_singler-classifications.tsv")
+classification_files <- file.path(singler_results_dir, sample_ids, classification_file_names) |>
+  purrr::set_names(library_ids)
+
+# path to clustering results files
+cluster_results_dir <- file.path(module_base, "results", "clustering", sample_ids)
+cluster_file_names <- glue::glue("{library_ids}_cluster-results.tsv")
+cluster_files <- file.path(cluster_results_dir, cluster_file_names) |>
+  purrr::set_names(library_ids)
+

Below we set up the data for plotting throughout this notebook.

+
    +
  1. The output from aucell-singler-annotation.sh is read in +and annotations are lumped so that we can easily plot the top cell +types.
    2. +
  2. Clustering results from evaluate-clusters.sh is read in +and subset to the desired parameters we would like to use for assigning +clusters (leiden, modularity, resolution 0.5, nearest neighbors +20).
    3. +
  3. The marker genes for tumor cell states and custom gene signatures +are used to calculate the sum of all marker genes in each gene set for +each library.
    4. +
  4. UMAP embeddings for each library is pulled out into a data frame and +combined with SingleR annotations, cluster assignments, and +marker gene expression for all marker gene sets.
    5. +
+
# read in both SCEs
+sce_list <- sce_files |>
+  purrr::map(readr::read_rds)
+
+# read in classification data frame with SingleR results and combine into one
+classification_df <- classification_files |>
+  purrr::map(readr::read_tsv) |>
+  dplyr::bind_rows(.id = "library_id") |> 
+  dplyr::mutate(
+    # first grab anything that is tumor and label it tumor
+    # NA should be unknown
+    singler_annotation = dplyr::case_when(
+      stringr::str_detect(singler_annotation, "tumor") ~ "tumor",
+      is.na(singler_annotation) ~ "unknown", # make sure to separate out unknown labels
+      .default = singler_annotation
+    ) |>
+      forcats::fct_relevel("tumor", after = 0),
+    # get the top cell types for plotting later
+    singler_lumped = singler_annotation |>
+      forcats::fct_lump_n(7, other_level = "All remaining cell types", ties.method = "first") |>
+      forcats::fct_infreq() |>
+      forcats::fct_relevel("All remaining cell types", after = Inf)
+  )
+
+# read in clustering results and select cluster assignments of interest 
+# here we keep leiden-mod, 0.5 res, 20 nn for both libraries 
+cluster_df <- cluster_files |> 
+  purrr::map(readr::read_tsv) |> 
+  dplyr::bind_rows(.id = "library_id") |> 
+  dplyr::filter(
+    cluster_method == "leiden_mod",
+    nn == 20,
+    resolution == 0.5
+  ) |> 
+  dplyr::select(
+    barcodes = cell_id,
+    library_id, 
+    cluster
+  )
+
# read in marker genes table
+marker_genes_df <- readr::read_tsv(marker_genes_file, show_col_types = FALSE) |>
+  dplyr::select(cell_state, ensembl_gene_id, gene_symbol) 
+
+# get individual gene signatures
+aynaud_genes <- readr::read_tsv(aynaud_file) |> 
+  dplyr::mutate(cell_state = "aynaud-ews-high") |>
+  tidyr::drop_na()
+
+wrenn_genes <- readr::read_tsv(wrenn_file) |> 
+  dplyr::mutate(cell_state = "wrenn-ews-low") |>
+  tidyr::drop_na()
+
+# combine all genes into a single df 
+all_markers_df <- dplyr::bind_rows(list(marker_genes_df, aynaud_genes, wrenn_genes))
+
+# get list of all cell states/ gene lists 
+cell_states <- unique(all_markers_df$cell_state)
+
+# get the sum of expression of all genes for each cell state
+gene_exp_df <- sce_list |>
+  purrr::map(\(sce) {
+    cell_states |>
+      purrr::map(\(state){
+        calculate_sum_markers(all_markers_df, sce, state, cell_state)
+      }) |>
+      purrr::reduce(dplyr::inner_join, by = "barcodes")
+  }) |>
+  dplyr::bind_rows(.id = "library_id")
+
+# get umap embeddings and combine into a data frame with gene exp, cluster, and cell type assignments 
+umap_df <- sce_list |>
+  purrr::map(\(sce){
+    df <- sce |>
+      scuttle::makePerCellDF(use.dimred = "UMAP") |>
+      # replace UMAP.1 with UMAP1 and get rid of excess columns
+      dplyr::select(barcodes, UMAP1 = UMAP.1, UMAP2 = UMAP.2)
+  }) |>
+  dplyr::bind_rows(.id = "library_id") |>
+  # add in classifications 
+  dplyr::left_join(classification_df, by = c("library_id", "barcodes")) |> 
+  dplyr::left_join(gene_exp_df, by = c("barcodes", "library_id")) |> 
+  # add in cluster assignments 
+  dplyr::left_join(cluster_df, by = c("barcodes", "library_id"))
+
+
+

Identify tumor cells

+

To label tumor cell states, we first want to pull out the cells that +are tumor cells and then look at the expression of marker genes for each +tumor cell state in those cells. We have used a variety of methods to +identify tumor cells so we will look at the output from two orthogonal +approaches to identify the group of tumor cells we want to further +classify into cell states. Let’s start by looking at the intersection of +tumor cells assigned by the aucell-singler-annotation.sh +workflow and the clusters obtained from the +evaluate-clusters.sh workflow that have high expression of +tumor marker genes.

+

Below are two plots that we generated as part of the +evaluate-clusters.sh workflow that tells us which clusters +express tumor marker genes for each of these libraries. We can use this +information to help identify tumor cells to classify.

+
+SCPCL000822 +
SCPCL000822
+
+
+SCPCL000824 +
SCPCL000824
+
+
library_ids |>
+  purrr::map(\(id){
+    
+    p1 <- cluster_classification_df |>
+      dplyr::filter(library_id == id) |>
+      ggplot(aes(x = UMAP1, y = UMAP2, color = singler_lumped)) +
+      geom_point(alpha = 0.5, size = 0.1) +
+      theme(
+        aspect.ratio = 1
+      ) +
+      scale_color_brewer(palette = "Dark2") +
+      labs(title = id) +
+      guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5)))
+    
+    p2 <- cluster_classification_df |>
+      dplyr::filter(library_id == id) |>
+      ggplot(aes(x = UMAP1, y = UMAP2, color = as.factor(cluster))) +
+      geom_point(alpha = 0.5, size = 0.1) +
+      theme(
+        aspect.ratio = 1
+      ) +
+      #scale_color_brewer(palette = "Dark2") +
+      labs(title = id, color = "cluster") +
+      guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5)))
+    
+    patchwork::wrap_plots(p1, p2, nrow = 2)
+  })
+
## [[1]]
+
## Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_point()`).
+

+
## 
+## [[2]]
+
## Warning: Removed 1 row containing missing values or values outside the scale range (`geom_point()`).
+

+

Looking at the above plots, we can assign some general cell types +using clusters. This is the same approach we took in +05-cluster-exploration.Rmd but with slightly different +parameters. Based on the UMAPs and the density plots we see that tumor +cells are in cluster 1, 3, and 4 for SCPCL000822 and +cluster 2, 3, 4, 5, 6, and 7 for SCPCL000824.

+
cluster_classification_df <- umap_df |> 
+  dplyr::mutate(
+    cluster_classification = dplyr::case_when(
+      library_id == "SCPCL000822" & cluster %in% c(1, 3, 4) ~ "tumor",
+      library_id == "SCPCL000822" & cluster == 6 ~ "endothelial cell",
+      # cluster 2 has predominantly chondrocytes and has MSC marker gene expression
+      library_id == "SCPCL000822" & cluster == 2 ~ "chondrocyte",
+      # clusters with high "immune" marker gene expression are macrophage
+      library_id == "SCPCL000822" & cluster == 5 ~ "macrophage",
+      # cluster 8 has high MSC marker gene expression and a mix of MSC like cell types
+      library_id == "SCPCL000822" & cluster == 7 ~ "mesenchymal-like cell",
+      library_id == "SCPCL000824" & cluster %in% c(2, 3, 4, 5, 6, 7) ~ "tumor", 
+      library_id == "SCPCL000824" & cluster == 8 ~ "endothelial cell",
+      # cluster 9 has high immune markers and is mostly macrohpages
+      library_id == "SCPCL000824" & cluster == 9 ~ "macrophage",
+      # cluster 1 has high MSC marker gene expression and a mix of MSC like cell types
+      library_id == "SCPCL000824" & cluster == 1 ~ "mesenchymal-like cell",
+      # if they didn't have high expression of any markers, we label them as unknown
+      .default = "unknown"
+    )
+  )
+
library_ids |>
+  purrr::map(\(id){
+    cluster_classification_df |>
+      dplyr::filter(library_id == id) |>
+      ggplot(aes(x = UMAP1, y = UMAP2, color = cluster_classification)) +
+      geom_point(alpha = 0.5, size = 0.1) +
+      theme(
+        aspect.ratio = 1
+      ) +
+      labs(title = id) +
+      scale_color_brewer(palette = "Dark2") +
+      guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5)))
+  })
+
## [[1]]
+

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

+

Looking at this I think the cells that are classified as tumor cells +using the cluster assignments mostly line up with the +SingleR annotations. So we can label any cells from +clusters that have high tumor marker gene expression as tumor cells.

+
+

Look at marker gene expression for tumor cell states in all +cells

+

Below we look at the expression of genes for the tumor cell states +across all cells. This will show us how the expression of these gene +sets varies across cells in each library and help ensure we have the +correct population of tumor cells.

+

The value for each of the marker gene sets corresponds to the sum of +the expression of all genes in the given gene list. (Note that I also +looked at mean and it had similar output)

+

The gene sets that we use are:

+
    +
  • EWS-low: Marker genes from +tumor-cell-state-markers.tsv that correspond to the +EWS-low cell state.
    • +
  • EWS-high: Marker genes from +tumor-cell-state-markers.tsv that correspond to the +EWS-high cell state.
    • +
  • proliferative : Marker genes from +tumor-cell-state-markers.tsv that correspond to the +proliferative cell state. Note that a lot of the Ewing +sarcoma literature argues that EWS-FLI1 high cells are +proliferative.
    • +
  • aynaud-ews-high: Marker genes from +gene_signatures/aynaud-ews-targets.tsv that correspond to +genes activated by EWS-FLI1.
    • +
  • wrenn-ews-low: Marker genes from +gene_signatures/wrenn-nt5e-genes.tsv that correspond to +genes repressed by EWS-FLI1.
    • +
+
# pull out columns that have sum of marker gene expression 
+marker_gene_cols <- colnames(cluster_classification_df)[stringr::str_detect(colnames(cluster_classification_df), "_sum$")]
+
+# look at the expression of all gene sets across both libraries 
+marker_gene_cols |>
+  purrr::map(\(geneset){
+    cluster_classification_df |>
+      ggplot(aes(x = UMAP1, y = UMAP2, color = !!sym(geneset))) +
+      geom_point(alpha = 0.5, size = 0.1) +
+      facet_wrap(vars(library_id)) +
+      scale_color_viridis_c() +
+      theme(
+        aspect.ratio = 1
+      )
+  }) |> 
+  patchwork::wrap_plots(ncol = 2)
+

+

It looks like there’s a cluster of cells in both libraries that have +expression of the EWS-low and wrenn-ews-low +signatures. Those cells correspond to the cells that we are labeling as +“chondrocyte” in SCPCL000822 and “mesenchymal-like” in +SCPCL000824. The chondrocytes also express high markers of +mesenchymal-like genes and the literature talks about tumor cells with +low EWS-FLI1 expression being “MSC-like”. I think there’s a +possibility that these are tumor cells, so let’s relabel them.

+
# create a new tumor classification that takes all tumor clusters plus the clusters with EWS-FLI1 low expression 
+cluster_classification_df <- cluster_classification_df |> 
+  dplyr::mutate(
+    tumor_classification = dplyr::case_when(
+      cluster_classification == "tumor" ~ "tumor",
+      library_id == "SCPCL000822" & cluster_classification == "chondrocyte" ~ "tumor",
+      library_id == "SCPCL000824" & cluster_classification == "mesenchymal-like cell" ~ "tumor",
+      .default = cluster_classification
+    )
+  )
+
+
+
+

Classify tumor cell states

+

Now that we have identified a subset of cells that are tumor cells, +we can attempt to group them into various cell states. The main thing +that we are interested in is grouping cells into levels of +EWS-FLI1 expression. Let’s start by looking at the +expression of the gene sets across all tumor cells.

+

To do this, we will make a density plot that looks at gene set +expression by cluster using the cluster assignments calculated using all +cells.

+
tumor_cells_df <- cluster_classification_df |> 
+  dplyr::filter(tumor_classification == "tumor")
+
# density plot showing expression of each gene set by cluster
+library_ids |> 
+  purrr::map(\(id){
+    
+    density_df <- tumor_cells_df |> 
+      dplyr::filter(library_id == id)
+    
+    marker_gene_cols |>
+    purrr::map(\(column){
+      plot_density(
+        density_df,
+        column,
+        "cluster"
+      ) 
+    }) |>
+    patchwork::wrap_plots(ncol = 1) +
+      patchwork::plot_annotation(id)
+    
+  })
+
## [[1]]
+

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

+

A few notes from this:

+
    +
  • It doesn’t look like very many cells express proliferative markers, +so perhaps that gene set will not be helpful for these libraries?
    • +
  • There are some very obvious EWS-FLI1 low groups of +cells.
    • +
  • The EWS-high and aynaud-EWS-high show +clusters that have high expression of EWS-FLI1 targets, but +the aynaud-EWS-high appears to have some more separation +across clusters. That tells me that gene signature might be more helpful +in classifying cells across the EWS-FLI1 continuum.
    • +
+
+

Sub cluster tumor cells

+

We would expect that tumor cells that are in similar cell states +would cluster together. So here I am going to take just the tumor cells +and re-assign clusters. I’m using the same parameters we use for the +whole sample, leiden, modularity, resolution of 0.5, and nearest +neighbors of 20. I think if we really wanted to be stringent we would +evaluate clusters here and find the best parameters to use, but I think +starting with these is good.

+
# get a data frame of all tumor cells and re assigned clusters 
+subcluster_df <- sce_list |>
+  purrr::imap(\(sce, id){
+    
+    # pull out the barcodes that are from tumor cells
+    tumor_cells <- tumor_cells_df |> 
+      dplyr::filter(library_id == id) |> 
+      dplyr::pull(barcodes)
+    
+    # filter the sce to only those barcodes
+    filtered_sce <- sce[ , tumor_cells]
+    
+    # grab pcs for clustering
+    pcs <- reducedDim(filtered_sce)
+    
+    # get the clusters 
+    clusters <- bluster::clusterRows(
+      pcs,
+      bluster::NNGraphParam(
+        k = 20,
+        type = "jaccard",
+        cluster.fun = "leiden",
+        cluster.args = list(objective_function = "modularity",
+                            resolution = 0.5)
+      )
+    )
+    
+    # combine into a data frame with barcodes
+    cluster_df <- data.frame(
+      barcodes = rownames(pcs),
+      sub_cluster = clusters
+    )
+  }) |> 
+  # get clusters for both samples in one data frame
+  dplyr::bind_rows(.id = "library_id")
+
+# add clusters to tumor cells df for plotting
+tumor_cells_df <- tumor_cells_df |> 
+  dplyr::left_join(subcluster_df, by = c("library_id", "barcodes"))
+

The first thing we will do is plot the cluster assignments on the +UMAP.

+
library_ids |>
+  purrr::map(\(id){
+    tumor_cells_df |>
+      dplyr::filter(library_id == id) |>
+      ggplot(aes(x = UMAP1, y = UMAP2, color = sub_cluster)) +
+      geom_point(alpha = 0.5, size = 0.1) +
+      theme(
+        aspect.ratio = 1
+      ) +
+      labs(title = id) +
+      scale_color_brewer(palette = "Dark2") +
+      guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5)))
+  })
+
## [[1]]
+

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

+

Now we will look at the marker gene expression across all clusters +for each sample. First using a density plot and then a heatmap.

+
library_ids |> 
+  purrr::map(\(id){
+    
+    density_df <- tumor_cells_df |> 
+      dplyr::filter(library_id == id)
+    
+    marker_gene_cols |>
+    purrr::map(\(column){
+      plot_density(
+        density_df,
+        column,
+        "sub_cluster"
+      ) 
+    }) |>
+    patchwork::wrap_plots(ncol = 1) +
+      patchwork::plot_annotation(id)
+    
+  })
+
## [[1]]
+

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

+
library_ids |> 
+  purrr::map(\(id){
+    
+    filtered_df <- tumor_cells_df |> 
+      dplyr::filter(library_id == id)
+    
+    full_celltype_heatmap(filtered_df, marker_gene_cols, "sub_cluster", plot_name = id)
+    
+  })
+
## [[1]]
+

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

+

It looks like for both samples we have some clear division between +EWS high and EWS low cells. Additionally, there are some cells with +really strong expression of the EWS high markers and others that look +like the genes are expressed but to a lesser degree. This fits with the +findings from the literature that EWS-FLI1 expression lies +on a continuum.

+

Between these two plots I think we can try and group cells into EWS +low, EWS middle, and EWS high cells. I don’t think the proliferative +markers are very informative, so I don’t think we can use that to +categorize any cells.

+

Based on this I would make the following assignments:

+

SCPCL000822: EWS-FLI1 low - 2 +EWS-FLI1 middle - 1 & 4 EWS-FLI1 high - +3

+

SCPCL000824 : EWS-FLI1 low - 1 +EWS-FLI1 middle - 6 EWS-FLI1 high - 2, 3, 4, +5, 7

+
# categorize tumor cells based on EWS expression 
+tumor_cells_df <- tumor_cells_df |> 
+  dplyr::mutate(
+    cell_state = dplyr::case_when(
+      library_id == "SCPCL000822" & sub_cluster == 2 ~ "EWS-low",
+      library_id == "SCPCL000822" & sub_cluster %in% c(1, 4) ~ "EWS-mid",
+      library_id == "SCPCL000822" & sub_cluster == 3 ~ "EWS-high",
+      library_id == "SCPCL000824" & sub_cluster == 1 ~ "EWS-low",
+      library_id == "SCPCL000824" & sub_cluster %in% c(6, 8) ~ "EWS-mid",
+      library_id == "SCPCL000824" & sub_cluster %in% c(2, 3, 4, 5, 7) ~ "EWS-high",
+    )
+  )
+

Let’s re-plot the heatmap but label by tumor cell state rather than +cluster.

+
library_ids |> 
+  purrr::map(\(id){
+    
+    filtered_df <- tumor_cells_df |> 
+      dplyr::filter(library_id == id)
+    
+    full_celltype_heatmap(filtered_df, marker_gene_cols, "cell_state", plot_name = id)
+    
+  })
+
## [[1]]
+

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

+

I think that looks pretty reasonable. I’m not sure we can really make +more definitive calls given the gene lists that we have, unless we use +fancier approaches. We could try something like AUCell, but +without a nice bimodal distribution, I don’t think we would get anything +but low/high cells.

+
+
+
+

Combined classifications

+

As a final step, we’ll add the tumor cell states back into the full +object and look at them on the UMAP. Here I am plotting the +classification of cells based on the cluster because otherwise the +normal cells get a little wild with colors and I want to be able to see +visualize the tumor cells.

+
# select the columns we want to join back into the main classification df 
+cell_state_classifications <- tumor_cells_df |> 
+  dplyr::select(library_id, barcodes, cell_state, sub_cluster)
+
+# add cell states to classification 
+final_classification_df <- cluster_classification_df |> 
+  dplyr::left_join(cell_state_classifications, by = c("library_id", "barcodes")) |> 
+  dplyr::mutate(full_classification = dplyr::if_else(is.na(cell_state), singler_lumped, cell_state),
+                cluster_classification = dplyr::if_else(is.na(cell_state), cluster_classification, cell_state))
+
+# make some umaps 
+library_ids |>
+  purrr::map(\(id){
+    final_classification_df |>
+      dplyr::filter(library_id == id) |>
+      ggplot(aes(x = UMAP1, y = UMAP2, color = cluster_classification)) +
+      geom_point(alpha = 0.5, size = 0.1) +
+      theme(
+        aspect.ratio = 1
+      ) +
+      labs(title = id) +
+      scale_color_brewer(palette = "Dark2") +
+      guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5)))
+  })
+
## [[1]]
+

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

+
# export the final classifications in case we want to use them in the future 
+# save all classification columns 
+final_classification_df |> 
+  split(final_classification_df$library_id) |> 
+  purrr::iwalk(\(df, id){
+    
+    filename <- glue::glue("{id}_tumor-cell-state-annotations.tsv")
+    
+    df |> 
+      dplyr::select(
+        library_id,
+        barcodes,
+        singler_annotation,
+        aucell_annotation, 
+        cluster, 
+        cluster_classification, 
+        tumor_sub_cluster = sub_cluster, 
+        tumor_cell_state = cell_state,
+        full_classification 
+      ) |> 
+      readr::write_tsv(file.path(results_dir, filename))
+    
+  })
+
+
+

Conclusions

+
    +
  • There is a clear division between cells that are +EWS-FLI high and EWS-FLI1 low.
    • +
  • Most tumor cells in both samples are EWS-FLI high, +which is consistent with the literature.
    • +
  • There may be a class of cells that live in between high and low, but +if we want to define those we probably need to be more stringent with +our approach and identify a mechanism to bin cells based on expression +of both EWS-FLI1 activated and repressed genes, which is +outside the scope of this notebook.
    • +
+
+
+

Session info

+
# record the versions of the packages used in this analysis and other environment information
+sessionInfo()
+
## R version 4.4.2 (2024-10-31)
+## Platform: aarch64-apple-darwin20
+## Running under: macOS Sequoia 15.2
+## 
+## 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] stats4    stats     graphics  grDevices datasets  utils     methods   base     
+## 
+## other attached packages:
+##  [1] ggplot2_3.5.1               SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 Biobase_2.64.0              GenomicRanges_1.56.1       
+##  [6] GenomeInfoDb_1.40.1         IRanges_2.38.1              S4Vectors_0.42.1            BiocGenerics_0.50.0         MatrixGenerics_1.16.0      
+## [11] matrixStats_1.3.0          
+## 
+## loaded via a namespace (and not attached):
+##  [1] rlang_1.1.4               magrittr_2.0.3            clue_0.3-65               GetoptLong_1.0.5          compiler_4.4.2            DelayedMatrixStats_1.26.0
+##  [7] png_0.1-8                 vctrs_0.6.5               stringr_1.5.1             pkgconfig_2.0.3           shape_1.4.6.1             crayon_1.5.3             
+## [13] fastmap_1.2.0             XVector_0.44.0            scuttle_1.14.0            labeling_0.4.3            utf8_1.2.4                rmarkdown_2.27           
+## [19] tzdb_0.4.0                UCSC.utils_1.0.0          purrr_1.0.2               bit_4.0.5                 xfun_0.46                 bluster_1.14.0           
+## [25] cachem_1.1.0              zlibbioc_1.50.0           beachmat_2.20.0           jsonlite_1.8.8            highr_0.11                DelayedArray_0.30.1      
+## [31] BiocParallel_1.38.0       parallel_4.4.2            cluster_2.1.6             R6_2.5.1                  bslib_0.8.0               stringi_1.8.4            
+## [37] RColorBrewer_1.1-3        jquerylib_0.1.4           Rcpp_1.0.13               iterators_1.0.14          knitr_1.48                readr_2.1.5              
+## [43] Matrix_1.7-0              igraph_2.0.3              tidyselect_1.2.1          abind_1.4-5               yaml_2.3.10               doParallel_1.0.17        
+## [49] codetools_0.2-20          lattice_0.22-6            tibble_3.2.1              withr_3.0.0               evaluate_0.24.0           circlize_0.4.16          
+## [55] pillar_1.9.0              BiocManager_1.30.23       renv_1.0.7                foreach_1.5.2             generics_0.1.3            vroom_1.6.5              
+## [61] rprojroot_2.0.4           hms_1.1.3                 sparseMatrixStats_1.16.0  munsell_0.5.1             scales_1.3.0              glue_1.7.0               
+## [67] tools_4.4.2               BiocNeighbors_1.22.0      forcats_1.0.0             fs_1.6.4                  grid_4.4.2                tidyr_1.3.1              
+## [73] colorspace_2.1-1          GenomeInfoDbData_1.2.12   patchwork_1.2.0           cli_3.6.3                 fansi_1.0.6               S4Arrays_1.4.1           
+## [79] viridisLite_0.4.2         ComplexHeatmap_2.20.0     dplyr_1.1.4               gtable_0.3.5              sass_0.4.9                digest_0.6.36            
+## [85] SparseArray_1.4.8         rjson_0.2.21              farver_2.1.2              htmltools_0.5.8.1         lifecycle_1.0.4           httr_1.4.7               
+## [91] GlobalOptions_0.1.2       bit64_4.0.5
+
+ + + + +
+ + + + + + + + + + + + + + + + From 13f71b0dae56311e42ee3cb471275d24c8a76b34 Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Fri, 10 Jan 2025 12:28:14 -0600 Subject: [PATCH 05/15] update readme --- analyses/cell-type-ewings/exploratory_analysis/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/analyses/cell-type-ewings/exploratory_analysis/README.md b/analyses/cell-type-ewings/exploratory_analysis/README.md index 6ee755a3e..f9c78edcc 100644 --- a/analyses/cell-type-ewings/exploratory_analysis/README.md +++ b/analyses/cell-type-ewings/exploratory_analysis/README.md @@ -20,6 +20,9 @@ This includes comparing annotations to those obtained from marker gene annotatio 5. `05-cluster-exploration.Rmd`: This notebook looks at clustering across different parameters to choose the most optimal clusters for `SCPCL000822` and `SCPCL000824`. Additionally, we look at expression of marker genes from `references/visser-all-marker-genes.tsv` across all clusters and use that to refine tumor cell annotations obtained from running `aucell-singler-annotation.sh`. +6. `06-tumor-cell-state-assignments.Rmd`: This notebook looks at assigning tumor cell states in `SCPCL000822` and `SCPCL000824`. +Tumor cells are gruoped into `EWS-FLI` high, `EWS-FLI` low, and `EWS-FLI` middle based on expression of marker genes. + ## Annotation notebooks The `annotation_notebooks` folder contains exploratory notebooks used to explore and validate annotations in individual samples. From 5af762f378fb4723bb069465d236b7b1a93c0ad4 Mon Sep 17 00:00:00 2001 From: Ally Hawkins <54039191+allyhawkins@users.noreply.github.com> Date: Fri, 10 Jan 2025 14:36:31 -0600 Subject: [PATCH 06/15] Apply suggestions from code review Co-authored-by: Stephanie Spielman --- .../06-tumor-cell-state-assignment.Rmd | 11 ++++++----- .../cell-type-ewings/exploratory_analysis/README.md | 2 +- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd index 86092081c..d1a47bf48 100644 --- a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd +++ b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd @@ -95,7 +95,7 @@ Below we set up the data for plotting throughout this notebook. 1. The output from `aucell-singler-annotation.sh` is read in and annotations are lumped so that we can easily plot the top cell types. 2. Clustering results from `evaluate-clusters.sh` is read in and subset to the desired parameters we would like to use for assigning clusters (leiden, modularity, resolution 0.5, nearest neighbors 20). 3. The marker genes for tumor cell states and custom gene signatures are used to calculate the sum of all marker genes in each gene set for each library. -4. UMAP embeddings for each library is pulled out into a data frame and combined with `SingleR` annotations, cluster assignments, and marker gene expression for all marker gene sets. +4. UMAP embeddings for each library are pulled out into a data frame and combined with `SingleR` annotations, cluster assignments, and marker gene expression for all marker gene sets. ```{r, message=FALSE} # read in both SCEs @@ -238,6 +238,7 @@ Based on the UMAPs and the density plots we see that tumor cells are in cluster cluster_classification_df <- umap_df |> dplyr::mutate( cluster_classification = dplyr::case_when( + ############## Library SCPCL000822 ############### library_id == "SCPCL000822" & cluster %in% c(1, 3, 4) ~ "tumor", library_id == "SCPCL000822" & cluster == 6 ~ "endothelial cell", # cluster 2 has predominantly chondrocytes and has MSC marker gene expression @@ -246,6 +247,7 @@ cluster_classification_df <- umap_df |> library_id == "SCPCL000822" & cluster == 5 ~ "macrophage", # cluster 8 has high MSC marker gene expression and a mix of MSC like cell types library_id == "SCPCL000822" & cluster == 7 ~ "mesenchymal-like cell", + ############## Library SCPCL000824 ############### library_id == "SCPCL000824" & cluster %in% c(2, 3, 4, 5, 6, 7) ~ "tumor", library_id == "SCPCL000824" & cluster == 8 ~ "endothelial cell", # cluster 9 has high immune markers and is mostly macrohpages @@ -393,10 +395,10 @@ subcluster_df <- sce_list |> dplyr::pull(barcodes) # filter the sce to only those barcodes - filtered_sce <- sce[ , tumor_cells] + tumor_sce <- sce[ , tumor_cells] # grab pcs for clustering - pcs <- reducedDim(filtered_sce) + pcs <- reducedDim(tumor_sce) # get the clusters clusters <- bluster::clusterRows( @@ -442,8 +444,7 @@ library_ids |> }) ``` -Now we will look at the marker gene expression across all clusters for each sample. -First using a density plot and then a heatmap. +Now we will look at the marker gene expression across all clusters for each sample, first using a density plot and then a heatmap. ```{r, fig.height=10} library_ids |> diff --git a/analyses/cell-type-ewings/exploratory_analysis/README.md b/analyses/cell-type-ewings/exploratory_analysis/README.md index f9c78edcc..dd2c0d128 100644 --- a/analyses/cell-type-ewings/exploratory_analysis/README.md +++ b/analyses/cell-type-ewings/exploratory_analysis/README.md @@ -21,7 +21,7 @@ This includes comparing annotations to those obtained from marker gene annotatio Additionally, we look at expression of marker genes from `references/visser-all-marker-genes.tsv` across all clusters and use that to refine tumor cell annotations obtained from running `aucell-singler-annotation.sh`. 6. `06-tumor-cell-state-assignments.Rmd`: This notebook looks at assigning tumor cell states in `SCPCL000822` and `SCPCL000824`. -Tumor cells are gruoped into `EWS-FLI` high, `EWS-FLI` low, and `EWS-FLI` middle based on expression of marker genes. +Tumor cells are grouped into `EWS-FLI` high, `EWS-FLI` low, and `EWS-FLI` middle based on expression of marker genes. ## Annotation notebooks From 70c00e2d57de4c366505252c41e13c10c21b4be4 Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Fri, 10 Jan 2025 15:47:04 -0600 Subject: [PATCH 07/15] turn off clustering --- .../scripts/utils/tumor-validation-helpers.R | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R b/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R index 51db14975..f5ce0a2e2 100644 --- a/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R +++ b/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R @@ -164,7 +164,8 @@ plot_gene_heatmap <- function( df, row_title = "", legend_title = "", - annotation = NULL) { + annotation = NULL, + cluster_columns = TRUE) { # plot heatmap of marker genes heatmap <- ComplexHeatmap::Heatmap( df, @@ -180,7 +181,7 @@ plot_gene_heatmap <- function( row_dend_side = "right", row_names_gp = grid::gpar(fontsize = 10), ## Column parameters - cluster_columns = TRUE, + cluster_columns = cluster_columns, show_column_names = FALSE, bottom_annotation = annotation, heatmap_legend_param = list( @@ -240,7 +241,8 @@ plot_cnv_heatmap <- function( # colors are automatically determined using the `Dark2` palette and assigning to cell types listed in the `annotation_column` full_celltype_heatmap <- function(classification_df, gene_exp_columns, - annotation_column) { + annotation_column, + cluster_columns = TRUE) { # get list of all cell types being plotted and assign colors from Dark2 palette cell_types <- unique(classification_df[[annotation_column]]) num_cell_types <- length(cell_types) @@ -258,7 +260,7 @@ full_celltype_heatmap <- function(classification_df, # build matrix for heatmap cells x gene set sum or mean heatmap_mtx <- classification_df |> - dplyr::select(barcodes, gene_exp_columns) |> + dplyr::select(barcodes, all_of(gene_exp_columns)) |> tibble::column_to_rownames("barcodes") |> as.matrix() |> t() @@ -268,7 +270,8 @@ full_celltype_heatmap <- function(classification_df, plot_gene_heatmap(heatmap_mtx, row_title = "", legend_title = "Marker gene \nexpression", - annotation = annotation + annotation = annotation, + cluster_columns = cluster_columns ) } From e2e8a1a4d7198ad51ab886bdc83e1501eb8ab0a0 Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Fri, 10 Jan 2025 15:48:23 -0600 Subject: [PATCH 08/15] update umaps and heatmaps --- .../06-tumor-cell-state-assignment.Rmd | 48 +++++-- .../06-tumor-cell-state-assignment.html | 124 ++++++++++++------ 2 files changed, 119 insertions(+), 53 deletions(-) diff --git a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd index d1a47bf48..eb191e063 100644 --- a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd +++ b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd @@ -204,7 +204,7 @@ We can use this information to help identify tumor cells to classify. library_ids |> purrr::map(\(id){ - p1 <- cluster_classification_df |> + p1 <- umap_df |> dplyr::filter(library_id == id) |> ggplot(aes(x = UMAP1, y = UMAP2, color = singler_lumped)) + geom_point(alpha = 0.5, size = 0.1) + @@ -215,7 +215,7 @@ library_ids |> labs(title = id) + guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5))) - p2 <- cluster_classification_df |> + p2 <- umap_df |> dplyr::filter(library_id == id) |> ggplot(aes(x = UMAP1, y = UMAP2, color = as.factor(cluster))) + geom_point(alpha = 0.5, size = 0.1) + @@ -296,7 +296,7 @@ Note that a lot of the Ewing sarcoma literature argues that `EWS-FLI1` high cell - `aynaud-ews-high`: Marker genes from `gene_signatures/aynaud-ews-targets.tsv` that correspond to genes activated by `EWS-FLI1`. - `wrenn-ews-low`: Marker genes from `gene_signatures/wrenn-nt5e-genes.tsv` that correspond to genes repressed by `EWS-FLI1`. -```{r, fig.height=7, fig.width=10} +```{r} # pull out columns that have sum of marker gene expression marker_gene_cols <- colnames(cluster_classification_df)[stringr::str_detect(colnames(cluster_classification_df), "_sum$")] @@ -311,8 +311,7 @@ marker_gene_cols |> theme( aspect.ratio = 1 ) - }) |> - patchwork::wrap_plots(ncol = 2) + }) ``` It looks like there's a cluster of cells in both libraries that have expression of the `EWS-low` and `wrenn-ews-low` signatures. @@ -371,7 +370,8 @@ library_ids |> A few notes from this: -- It doesn't look like very many cells express proliferative markers, so perhaps that gene set will not be helpful for these libraries? +- It doesn't look like very many cells express proliferative markers in `SCPCL000822`, but cluster 2 has high expression in `SCPCL000824`. +It looks like that might coincide with high `EWS-FLI` seen in that same cluster, but we have to dive deeper to know for sure. - There are some very obvious `EWS-FLI1` low groups of cells. - The `EWS-high` and `aynaud-EWS-high` show clusters that have high expression of `EWS-FLI1` targets, but the `aynaud-EWS-high` appears to have some more separation across clusters. That tells me that gene signature might be more helpful in classifying cells across the `EWS-FLI1` continuum. @@ -444,7 +444,7 @@ library_ids |> }) ``` -Now we will look at the marker gene expression across all clusters for each sample, first using a density plot and then a heatmap. +Now we will look at the marker gene expression across all clusters for each sample, first using a density plot and then heatmaps. ```{r, fig.height=10} library_ids |> @@ -467,6 +467,7 @@ library_ids |> }) ``` +In all of the heatmaps below, the `annotation` column is the `sub_cluster`. ```{r} library_ids |> @@ -475,18 +476,36 @@ library_ids |> filtered_df <- tumor_cells_df |> dplyr::filter(library_id == id) - full_celltype_heatmap(filtered_df, marker_gene_cols, "sub_cluster", plot_name = id) + full_celltype_heatmap(filtered_df, marker_gene_cols, "sub_cluster") }) ``` +Let's make the same heatmap, but remove the column clustering. + +```{r} +library_ids |> + purrr::map(\(id){ + + filtered_df <- tumor_cells_df |> + dplyr::filter(library_id == id) |> + dplyr::arrange(sub_cluster) + + full_celltype_heatmap(filtered_df, marker_gene_cols, "sub_cluster", cluster_columns = FALSE) + + }) +``` + It looks like for both samples we have some clear division between EWS high and EWS low cells. Additionally, there are some cells with really strong expression of the EWS high markers and others that look like the genes are expressed but to a lesser degree. This fits with the findings from the literature that `EWS-FLI1` expression lies on a continuum. Between these two plots I think we can try and group cells into EWS low, EWS middle, and EWS high cells. -I don't think the proliferative markers are very informative, so I don't think we can use that to categorize any cells. + +The last thing we'll do is look at any cells that could be marked as "proliferative". +`SCPCL000822` doesn't appear to have very much expression of the proliferative markers, but `SCPCL000824` does seem to have one cluster, cluster 2, that has expression of the proliferative cells. +Perhaps we should label those cells separately as `EWS-high-proliferative`. Based on this I would make the following assignments: @@ -498,7 +517,8 @@ Based on this I would make the following assignments: `SCPCL000824` : `EWS-FLI1` low - 1 `EWS-FLI1` middle - 6 -`EWS-FLI1` high - 2, 3, 4, 5, 7 +`EWS-FLI1` high - 3, 4, 5, 7 +`EWS-FLI1` high proliferative - 2 ```{r} # categorize tumor cells based on EWS expression @@ -509,13 +529,15 @@ tumor_cells_df <- tumor_cells_df |> library_id == "SCPCL000822" & sub_cluster %in% c(1, 4) ~ "EWS-mid", library_id == "SCPCL000822" & sub_cluster == 3 ~ "EWS-high", library_id == "SCPCL000824" & sub_cluster == 1 ~ "EWS-low", - library_id == "SCPCL000824" & sub_cluster %in% c(6, 8) ~ "EWS-mid", - library_id == "SCPCL000824" & sub_cluster %in% c(2, 3, 4, 5, 7) ~ "EWS-high", + library_id == "SCPCL000824" & sub_cluster == 6 ~ "EWS-mid", + library_id == "SCPCL000824" & sub_cluster == 2 ~ "EWS-high-proliferative", + library_id == "SCPCL000824" & sub_cluster %in% c(3, 4, 5, 7) ~ "EWS-high", ) ) ``` Let's re-plot the heatmap but label by tumor cell state rather than cluster. +In this heatmap the annotation column is `cell_state`. ```{r} library_ids |> @@ -524,7 +546,7 @@ library_ids |> filtered_df <- tumor_cells_df |> dplyr::filter(library_id == id) - full_celltype_heatmap(filtered_df, marker_gene_cols, "cell_state", plot_name = id) + full_celltype_heatmap(filtered_df, marker_gene_cols, "cell_state") }) ``` diff --git a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html index fbc82c611..578802f48 100644 --- a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html +++ b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html @@ -551,9 +551,9 @@

Prepare data

  • The marker genes for tumor cell states and custom gene signatures are used to calculate the sum of all marker genes in each gene set for each library.
    • -
  • UMAP embeddings for each library is pulled out into a data frame and -combined with SingleR annotations, cluster assignments, and -marker gene expression for all marker gene sets.
    • +
  • UMAP embeddings for each library are pulled out into a data frame +and combined with SingleR annotations, cluster assignments, +and marker gene expression for all marker gene sets.
    • # read in both SCEs
 sce_list <- sce_files |>
@@ -666,7 +666,7 @@ 
    Identify tumor cells
    
 
    library_ids |>
   purrr::map(\(id){
     
-    p1 <- cluster_classification_df |>
+    p1 <- umap_df |>
       dplyr::filter(library_id == id) |>
       ggplot(aes(x = UMAP1, y = UMAP2, color = singler_lumped)) +
       geom_point(alpha = 0.5, size = 0.1) +
@@ -677,7 +677,7 @@ 
    Identify tumor cells
    
       labs(title = id) +
       guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5)))
     
-    p2 <- cluster_classification_df |>
+    p2 <- umap_df |>
       dplyr::filter(library_id == id) |>
       ggplot(aes(x = UMAP1, y = UMAP2, color = as.factor(cluster))) +
       geom_point(alpha = 0.5, size = 0.1) +
@@ -706,6 +706,7 @@ 
    Identify tumor cells
    
 
    cluster_classification_df <- umap_df |> 
   dplyr::mutate(
     cluster_classification = dplyr::case_when(
+      ############## Library SCPCL000822 ###############
       library_id == "SCPCL000822" & cluster %in% c(1, 3, 4) ~ "tumor",
       library_id == "SCPCL000822" & cluster == 6 ~ "endothelial cell",
       # cluster 2 has predominantly chondrocytes and has MSC marker gene expression
@@ -714,6 +715,7 @@ 
    Identify tumor cells
    
       library_id == "SCPCL000822" & cluster == 5 ~ "macrophage",
       # cluster 8 has high MSC marker gene expression and a mix of MSC like cell types
       library_id == "SCPCL000822" & cluster == 7 ~ "mesenchymal-like cell",
+      ############## Library SCPCL000824 ###############
       library_id == "SCPCL000824" & cluster %in% c(2, 3, 4, 5, 6, 7) ~ "tumor", 
       library_id == "SCPCL000824" & cluster == 8 ~ "endothelial cell",
       # cluster 9 has high immune markers and is mostly macrohpages
@@ -790,9 +792,21 @@ 
    Look at marker gene expression for tumor cell states in all
       theme(
         aspect.ratio = 1
       )
-  }) |> 
-  patchwork::wrap_plots(ncol = 2)
    
-
    
+  })
    
+
    ## [[1]]
    
+
    
+
    ## 
+## [[2]]
    
+
    
+
    ## 
+## [[3]]
    
+
    
+
    ## 
+## [[4]]
    
+
    
+
    ## 
+## [[5]]
    
+
    
 
    It looks like there’s a cluster of cells in both libraries that have
 expression of the EWS-low and wrenn-ews-low
 signatures. Those cells correspond to the cells that we are labeling as
@@ -851,8 +865,11 @@ 
    Classify tumor cell states
    
 
    
 
    A few notes from this:
    
 
      
-
    • It doesn’t look like very many cells express proliferative markers,
-so perhaps that gene set will not be helpful for these libraries?
      • 
+
    • It doesn’t look like very many cells express proliferative markers
+in SCPCL000822, but cluster 2 has high expression in
+SCPCL000824. It looks like that might coincide with high
+EWS-FLI seen in that same cluster, but we have to dive
+deeper to know for sure.
      • 
 
    • There are some very obvious EWS-FLI1 low groups of
 cells.
      • 
 
    • The EWS-high and aynaud-EWS-high show
@@ -880,10 +897,10 @@ 
      Sub cluster tumor cells
      
       dplyr::pull(barcodes)
     
     # filter the sce to only those barcodes
-    filtered_sce <- sce[ , tumor_cells]
+    tumor_sce <- sce[ , tumor_cells]
     
     # grab pcs for clustering
-    pcs <- reducedDim(filtered_sce)
+    pcs <- reducedDim(tumor_sce)
     
     # get the clusters 
     clusters <- bluster::clusterRows(
@@ -930,7 +947,7 @@ 
      Sub cluster tumor cells
      
 ## [[2]]

    Now we will look at the marker gene expression across all clusters -for each sample. First using a density plot and then a heatmap.

    +for each sample, first using a density plot and then heatmaps.

    library_ids |> 
   purrr::map(\(id){
     
@@ -954,13 +971,15 @@ 
    Sub cluster tumor cells
    
 
    ## 
 ## [[2]]
    
 
    
+
    In all of the heatmaps below, the annotation column is
+the sub_cluster.
    
 
    library_ids |> 
   purrr::map(\(id){
     
     filtered_df <- tumor_cells_df |> 
       dplyr::filter(library_id == id)
     
-    full_celltype_heatmap(filtered_df, marker_gene_cols, "sub_cluster", plot_name = id)
+    full_celltype_heatmap(filtered_df, marker_gene_cols, "sub_cluster")
     
   })
    
 
    ## [[1]]
    
@@ -968,6 +987,22 @@ 
    Sub cluster tumor cells
    
 
    ## 
 ## [[2]]
    
 
    
+
    Let’s make the same heatmap, but remove the column clustering.
    
+
    library_ids |> 
+  purrr::map(\(id){
+    
+    filtered_df <- tumor_cells_df |> 
+      dplyr::filter(library_id == id) |> 
+      dplyr::arrange(sub_cluster)
+    
+    full_celltype_heatmap(filtered_df, marker_gene_cols, "sub_cluster", cluster_columns = FALSE)
+    
+  })
    
+
    ## [[1]]
    
+
    
+
    ## 
+## [[2]]
    
+
    
 
    It looks like for both samples we have some clear division between
 EWS high and EWS low cells. Additionally, there are some cells with
 really strong expression of the EWS high markers and others that look
@@ -975,16 +1010,20 @@ 
    Sub cluster tumor cells
    
 findings from the literature that EWS-FLI1 expression lies
 on a continuum.
    
 
    Between these two plots I think we can try and group cells into EWS
-low, EWS middle, and EWS high cells. I don’t think the proliferative
-markers are very informative, so I don’t think we can use that to
-categorize any cells.
    
+low, EWS middle, and EWS high cells.
    
+
    The last thing we’ll do is look at any cells that could be marked as
+“proliferative”. SCPCL000822 doesn’t appear to have very
+much expression of the proliferative markers, but
+SCPCL000824 does seem to have one cluster, cluster 2, that
+has expression of the proliferative cells. Perhaps we should label those
+cells separately as EWS-high-proliferative.
    
 
    Based on this I would make the following assignments:
    
 
    SCPCL000822: EWS-FLI1 low - 2
 EWS-FLI1 middle - 1 & 4 EWS-FLI1 high -
 3
    
 
    SCPCL000824 : EWS-FLI1 low - 1
-EWS-FLI1 middle - 6 EWS-FLI1 high - 2, 3, 4,
-5, 7
    
+EWS-FLI1 middle - 6 EWS-FLI1 high - 3, 4, 5, 7
+EWS-FLI1 high proliferative - 2
    
 
    # categorize tumor cells based on EWS expression 
 tumor_cells_df <- tumor_cells_df |> 
   dplyr::mutate(
@@ -993,26 +1032,28 @@ 
    Sub cluster tumor cells
    
       library_id == "SCPCL000822" & sub_cluster %in% c(1, 4) ~ "EWS-mid",
       library_id == "SCPCL000822" & sub_cluster == 3 ~ "EWS-high",
       library_id == "SCPCL000824" & sub_cluster == 1 ~ "EWS-low",
-      library_id == "SCPCL000824" & sub_cluster %in% c(6, 8) ~ "EWS-mid",
-      library_id == "SCPCL000824" & sub_cluster %in% c(2, 3, 4, 5, 7) ~ "EWS-high",
+      library_id == "SCPCL000824" & sub_cluster == 6 ~ "EWS-mid",
+      library_id == "SCPCL000824" & sub_cluster == 2 ~ "EWS-high-proliferative",
+      library_id == "SCPCL000824" & sub_cluster %in% c(3, 4, 5, 7) ~ "EWS-high",
     )
   )
    
 
    Let’s re-plot the heatmap but label by tumor cell state rather than
-cluster.
    
+cluster. In this heatmap the annotation column is
+cell_state.
    
 
    library_ids |> 
   purrr::map(\(id){
     
     filtered_df <- tumor_cells_df |> 
       dplyr::filter(library_id == id)
     
-    full_celltype_heatmap(filtered_df, marker_gene_cols, "cell_state", plot_name = id)
+    full_celltype_heatmap(filtered_df, marker_gene_cols, "cell_state")
     
   })
    
 
    ## [[1]]
    
 
    
 
    ## 
 ## [[2]]
    
-
    
+
    
 
    I think that looks pretty reasonable. I’m not sure we can really make
 more definitive calls given the gene lists that we have, unless we use
 fancier approaches. We could try something like AUCell, but
@@ -1055,7 +1096,7 @@ 
    Combined classifications
    
 
    
 
    ## 
 ## [[2]]
    
-
    
+
    
 
    # export the final classifications in case we want to use them in the future 
 # save all classification columns 
 final_classification_df |> 
@@ -1121,21 +1162,24 @@ 
    Session info
    
 ## [11] matrixStats_1.3.0          
 ## 
 ## loaded via a namespace (and not attached):
-##  [1] rlang_1.1.4               magrittr_2.0.3            clue_0.3-65               GetoptLong_1.0.5          compiler_4.4.2            DelayedMatrixStats_1.26.0
-##  [7] png_0.1-8                 vctrs_0.6.5               stringr_1.5.1             pkgconfig_2.0.3           shape_1.4.6.1             crayon_1.5.3             
-## [13] fastmap_1.2.0             XVector_0.44.0            scuttle_1.14.0            labeling_0.4.3            utf8_1.2.4                rmarkdown_2.27           
-## [19] tzdb_0.4.0                UCSC.utils_1.0.0          purrr_1.0.2               bit_4.0.5                 xfun_0.46                 bluster_1.14.0           
-## [25] cachem_1.1.0              zlibbioc_1.50.0           beachmat_2.20.0           jsonlite_1.8.8            highr_0.11                DelayedArray_0.30.1      
-## [31] BiocParallel_1.38.0       parallel_4.4.2            cluster_2.1.6             R6_2.5.1                  bslib_0.8.0               stringi_1.8.4            
-## [37] RColorBrewer_1.1-3        jquerylib_0.1.4           Rcpp_1.0.13               iterators_1.0.14          knitr_1.48                readr_2.1.5              
-## [43] Matrix_1.7-0              igraph_2.0.3              tidyselect_1.2.1          abind_1.4-5               yaml_2.3.10               doParallel_1.0.17        
-## [49] codetools_0.2-20          lattice_0.22-6            tibble_3.2.1              withr_3.0.0               evaluate_0.24.0           circlize_0.4.16          
-## [55] pillar_1.9.0              BiocManager_1.30.23       renv_1.0.7                foreach_1.5.2             generics_0.1.3            vroom_1.6.5              
-## [61] rprojroot_2.0.4           hms_1.1.3                 sparseMatrixStats_1.16.0  munsell_0.5.1             scales_1.3.0              glue_1.7.0               
-## [67] tools_4.4.2               BiocNeighbors_1.22.0      forcats_1.0.0             fs_1.6.4                  grid_4.4.2                tidyr_1.3.1              
-## [73] colorspace_2.1-1          GenomeInfoDbData_1.2.12   patchwork_1.2.0           cli_3.6.3                 fansi_1.0.6               S4Arrays_1.4.1           
-## [79] viridisLite_0.4.2         ComplexHeatmap_2.20.0     dplyr_1.1.4               gtable_0.3.5              sass_0.4.9                digest_0.6.36            
-## [85] SparseArray_1.4.8         rjson_0.2.21              farver_2.1.2              htmltools_0.5.8.1         lifecycle_1.0.4           httr_1.4.7               
+##  [1] rlang_1.1.4               magrittr_2.0.3            clue_0.3-65               GetoptLong_1.0.5          compiler_4.4.2           
+##  [6] DelayedMatrixStats_1.26.0 png_0.1-8                 vctrs_0.6.5               stringr_1.5.1             pkgconfig_2.0.3          
+## [11] shape_1.4.6.1             crayon_1.5.3              fastmap_1.2.0             XVector_0.44.0            scuttle_1.14.0           
+## [16] labeling_0.4.3            utf8_1.2.4                rmarkdown_2.27            tzdb_0.4.0                UCSC.utils_1.0.0         
+## [21] purrr_1.0.2               bit_4.0.5                 xfun_0.46                 bluster_1.14.0            cachem_1.1.0             
+## [26] zlibbioc_1.50.0           beachmat_2.20.0           jsonlite_1.8.8            highr_0.11                DelayedArray_0.30.1      
+## [31] BiocParallel_1.38.0       parallel_4.4.2            cluster_2.1.6             R6_2.5.1                  bslib_0.8.0              
+## [36] stringi_1.8.4             RColorBrewer_1.1-3        jquerylib_0.1.4           Rcpp_1.0.13               iterators_1.0.14         
+## [41] knitr_1.48                readr_2.1.5               Matrix_1.7-0              igraph_2.0.3              tidyselect_1.2.1         
+## [46] abind_1.4-5               yaml_2.3.10               doParallel_1.0.17         codetools_0.2-20          lattice_0.22-6           
+## [51] tibble_3.2.1              withr_3.0.0               evaluate_0.24.0           circlize_0.4.16           pillar_1.9.0             
+## [56] BiocManager_1.30.23       renv_1.0.7                foreach_1.5.2             generics_0.1.3            vroom_1.6.5              
+## [61] rprojroot_2.0.4           hms_1.1.3                 sparseMatrixStats_1.16.0  munsell_0.5.1             scales_1.3.0             
+## [66] glue_1.7.0                tools_4.4.2               BiocNeighbors_1.22.0      forcats_1.0.0             fs_1.6.4                 
+## [71] grid_4.4.2                tidyr_1.3.1               colorspace_2.1-1          GenomeInfoDbData_1.2.12   patchwork_1.2.0          
+## [76] cli_3.6.3                 fansi_1.0.6               S4Arrays_1.4.1            viridisLite_0.4.2         ComplexHeatmap_2.20.0    
+## [81] dplyr_1.1.4               gtable_0.3.5              sass_0.4.9                digest_0.6.36             SparseArray_1.4.8        
+## [86] rjson_0.2.21              farver_2.1.2              htmltools_0.5.8.1         lifecycle_1.0.4           httr_1.4.7               
 ## [91] GlobalOptions_0.1.2       bit64_4.0.5
    
 
 

From becb6afef228fad42d8c82695e1491976aed59c6 Mon Sep 17 00:00:00 2001
From: Ally Hawkins 
Date: Fri, 10 Jan 2025 16:25:32 -0600
Subject: [PATCH 09/15] use mean

---
 .../06-tumor-cell-state-assignment.Rmd        | 16 ++--
 .../06-tumor-cell-state-assignment.html       | 86 +++++++++----------
 .../scripts/utils/tumor-validation-helpers.R  | 38 +++++++-
 3 files changed, 86 insertions(+), 54 deletions(-)

diff --git a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd
index eb191e063..a58446a5c 100644
--- a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd
+++ b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd
@@ -94,7 +94,7 @@ Below we set up the data for plotting throughout this notebook.
 
 1. The output from `aucell-singler-annotation.sh` is read in and annotations are lumped so that we can easily plot the top cell types. 
 2. Clustering results from `evaluate-clusters.sh` is read in and subset to the desired parameters we would like to use for assigning clusters (leiden, modularity, resolution 0.5, nearest neighbors 20). 
-3. The marker genes for tumor cell states and custom gene signatures are used to calculate the sum of all marker genes in each gene set for each library. 
+3. The marker genes for tumor cell states and custom gene signatures are used to calculate the mean of all marker genes in each gene set for each library. 
 4. UMAP embeddings for each library are pulled out into a data frame and combined with `SingleR` annotations, cluster assignments, and marker gene expression for all marker gene sets.
 
 ```{r, message=FALSE}
@@ -159,12 +159,12 @@ all_markers_df <- dplyr::bind_rows(list(marker_genes_df, aynaud_genes, wrenn_gen
 # get list of all cell states/ gene lists 
 cell_states <- unique(all_markers_df$cell_state)
 
-# get the sum of expression of all genes for each cell state
+# get the mean expression of all genes for each cell state
 gene_exp_df <- sce_list |>
   purrr::map(\(sce) {
     cell_states |>
       purrr::map(\(state){
-        calculate_sum_markers(all_markers_df, sce, state, cell_state)
+        calculate_mean_markers(all_markers_df, sce, state, cell_state)
       }) |>
       purrr::reduce(dplyr::inner_join, by = "barcodes")
   }) |>
@@ -284,8 +284,7 @@ So we can label any cells from clusters that have high tumor marker gene express
 Below we look at the expression of genes for the tumor cell states across all cells. 
 This will show us how the expression of these gene sets varies across cells in each library and help ensure we have the correct population of tumor cells.
 
-The value for each of the marker gene sets corresponds to the sum of the expression of all genes in the given gene list. 
-(Note that I also looked at mean and it had similar output)
+The value for each of the marker gene sets corresponds to the mean of the expression of all genes in the given gene list. 
 
 The gene sets that we use are: 
 
@@ -298,7 +297,7 @@ Note that a lot of the Ewing sarcoma literature argues that `EWS-FLI1` high cell
 
 ```{r}
 # pull out columns that have sum of marker gene expression 
-marker_gene_cols <- colnames(cluster_classification_df)[stringr::str_detect(colnames(cluster_classification_df), "_sum$")]
+marker_gene_cols <- colnames(cluster_classification_df)[stringr::str_detect(colnames(cluster_classification_df), "_mean$")]
 
 # look at the expression of all gene sets across both libraries 
 marker_gene_cols |>
@@ -544,9 +543,10 @@ library_ids |>
   purrr::map(\(id){
     
     filtered_df <- tumor_cells_df |> 
-      dplyr::filter(library_id == id)
+      dplyr::filter(library_id == id)|> 
+      dplyr::arrange(cell_state)
     
-    full_celltype_heatmap(filtered_df, marker_gene_cols, "cell_state")
+    full_celltype_heatmap(filtered_df, marker_gene_cols, "cell_state", cluster_columns = FALSE)
     
   })
 ```
diff --git a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html
index 578802f48..f99c1d026 100644
--- a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html
+++ b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html
@@ -549,7 +549,7 @@ 
    Prepare data
    
 clusters (leiden, modularity, resolution 0.5, nearest neighbors
 20).
 
  • The marker genes for tumor cell states and custom gene signatures
-are used to calculate the sum of all marker genes in each gene set for
+are used to calculate the mean of all marker genes in each gene set for
 each library.
    • 
 
  • UMAP embeddings for each library are pulled out into a data frame
 and combined with SingleR annotations, cluster assignments,
@@ -613,12 +613,12 @@ 
    Prepare data
    
 # get list of all cell states/ gene lists 
 cell_states <- unique(all_markers_df$cell_state)
 
-# get the sum of expression of all genes for each cell state
+# get the mean expression of all genes for each cell state
 gene_exp_df <- sce_list |>
   purrr::map(\(sce) {
     cell_states |>
       purrr::map(\(state){
-        calculate_sum_markers(all_markers_df, sce, state, cell_state)
+        calculate_mean_markers(all_markers_df, sce, state, cell_state)
       }) |>
       purrr::reduce(dplyr::inner_join, by = "barcodes")
   }) |>
@@ -755,9 +755,8 @@ 
    Look at marker gene expression for tumor cell states in all
 across all cells. This will show us how the expression of these gene
 sets varies across cells in each library and help ensure we have the
 correct population of tumor cells.
    
-
    The value for each of the marker gene sets corresponds to the sum of
-the expression of all genes in the given gene list. (Note that I also
-looked at mean and it had similar output)
    
+
    The value for each of the marker gene sets corresponds to the mean of
+the expression of all genes in the given gene list.
    
 
    The gene sets that we use are:
    
 
      
 
    • EWS-low: Marker genes from
@@ -779,7 +778,7 @@ 
      Look at marker gene expression for tumor cell states in all
 genes repressed by EWS-FLI1.
      • 
 
    
 
    # pull out columns that have sum of marker gene expression 
-marker_gene_cols <- colnames(cluster_classification_df)[stringr::str_detect(colnames(cluster_classification_df), "_sum$")]
+marker_gene_cols <- colnames(cluster_classification_df)[stringr::str_detect(colnames(cluster_classification_df), "_mean$")]
 
 # look at the expression of all gene sets across both libraries 
 marker_gene_cols |>
@@ -794,19 +793,19 @@ 
    Look at marker gene expression for tumor cell states in all
       )
   })
    
 
    ## [[1]]
    
-
    
+
    
 
    ## 
 ## [[2]]
    
-
    
+
    
 
    ## 
 ## [[3]]
    
-
    
+
    
 
    ## 
 ## [[4]]
    
-
    
+
    
 
    ## 
 ## [[5]]
    
-
    
+
    
 
    It looks like there’s a cluster of cells in both libraries that have
 expression of the EWS-low and wrenn-ews-low
 signatures. Those cells correspond to the cells that we are labeling as
@@ -859,10 +858,10 @@ 
    Classify tumor cell states
    
     
   })
    • ## [[1]]
    -

    +

    ## 
 ## [[2]]
    -

    +

    A few notes from this:

    • It doesn’t look like very many cells express proliferative markers @@ -967,10 +966,10 @@

      Sub cluster tumor cells

      }) 
      ## [[1]]
      -

      +

      ## 
 ## [[2]]
      -

      +

      In all of the heatmaps below, the annotation column is the sub_cluster.

      library_ids |> 
@@ -983,10 +982,10 @@ 
      Sub cluster tumor cells
      
     
   })
      ## [[1]]
      -

      +

      ## 
 ## [[2]]
      -

      +

      Let’s make the same heatmap, but remove the column clustering.

      library_ids |> 
   purrr::map(\(id){
@@ -999,10 +998,10 @@ 
      Sub cluster tumor cells
      
     
   })
      ## [[1]]
      -

      +

      ## 
 ## [[2]]
      -

      +

      It looks like for both samples we have some clear division between EWS high and EWS low cells. Additionally, there are some cells with really strong expression of the EWS high markers and others that look @@ -1044,16 +1043,17 @@

      Sub cluster tumor cells

      purrr::map(\(id){ filtered_df <- tumor_cells_df |> - dplyr::filter(library_id == id) + dplyr::filter(library_id == id)|> + dplyr::arrange(cell_state) - full_celltype_heatmap(filtered_df, marker_gene_cols, "cell_state") + full_celltype_heatmap(filtered_df, marker_gene_cols, "cell_state", cluster_columns = FALSE) }) 
      ## [[1]]
      -

      +

      ## 
 ## [[2]]
      -

      +

      I think that looks pretty reasonable. I’m not sure we can really make more definitive calls given the gene lists that we have, unless we use fancier approaches. We could try something like AUCell, but @@ -1157,29 +1157,25 @@

      Session info

      ## [1] stats4 stats graphics grDevices datasets utils methods base ## ## other attached packages: -## [1] ggplot2_3.5.1 SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 Biobase_2.64.0 GenomicRanges_1.56.1 -## [6] GenomeInfoDb_1.40.1 IRanges_2.38.1 S4Vectors_0.42.1 BiocGenerics_0.50.0 MatrixGenerics_1.16.0 -## [11] matrixStats_1.3.0 +## [1] ggplot2_3.5.1 SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 Biobase_2.64.0 GenomicRanges_1.56.1 GenomeInfoDb_1.40.1 +## [7] IRanges_2.38.1 S4Vectors_0.42.1 BiocGenerics_0.50.0 MatrixGenerics_1.16.0 matrixStats_1.3.0 ## ## loaded via a namespace (and not attached): -## [1] rlang_1.1.4 magrittr_2.0.3 clue_0.3-65 GetoptLong_1.0.5 compiler_4.4.2 -## [6] DelayedMatrixStats_1.26.0 png_0.1-8 vctrs_0.6.5 stringr_1.5.1 pkgconfig_2.0.3 -## [11] shape_1.4.6.1 crayon_1.5.3 fastmap_1.2.0 XVector_0.44.0 scuttle_1.14.0 -## [16] labeling_0.4.3 utf8_1.2.4 rmarkdown_2.27 tzdb_0.4.0 UCSC.utils_1.0.0 -## [21] purrr_1.0.2 bit_4.0.5 xfun_0.46 bluster_1.14.0 cachem_1.1.0 -## [26] zlibbioc_1.50.0 beachmat_2.20.0 jsonlite_1.8.8 highr_0.11 DelayedArray_0.30.1 -## [31] BiocParallel_1.38.0 parallel_4.4.2 cluster_2.1.6 R6_2.5.1 bslib_0.8.0 -## [36] stringi_1.8.4 RColorBrewer_1.1-3 jquerylib_0.1.4 Rcpp_1.0.13 iterators_1.0.14 -## [41] knitr_1.48 readr_2.1.5 Matrix_1.7-0 igraph_2.0.3 tidyselect_1.2.1 -## [46] abind_1.4-5 yaml_2.3.10 doParallel_1.0.17 codetools_0.2-20 lattice_0.22-6 -## [51] tibble_3.2.1 withr_3.0.0 evaluate_0.24.0 circlize_0.4.16 pillar_1.9.0 -## [56] BiocManager_1.30.23 renv_1.0.7 foreach_1.5.2 generics_0.1.3 vroom_1.6.5 -## [61] rprojroot_2.0.4 hms_1.1.3 sparseMatrixStats_1.16.0 munsell_0.5.1 scales_1.3.0 -## [66] glue_1.7.0 tools_4.4.2 BiocNeighbors_1.22.0 forcats_1.0.0 fs_1.6.4 -## [71] grid_4.4.2 tidyr_1.3.1 colorspace_2.1-1 GenomeInfoDbData_1.2.12 patchwork_1.2.0 -## [76] cli_3.6.3 fansi_1.0.6 S4Arrays_1.4.1 viridisLite_0.4.2 ComplexHeatmap_2.20.0 -## [81] dplyr_1.1.4 gtable_0.3.5 sass_0.4.9 digest_0.6.36 SparseArray_1.4.8 -## [86] rjson_0.2.21 farver_2.1.2 htmltools_0.5.8.1 lifecycle_1.0.4 httr_1.4.7 +## [1] rlang_1.1.4 magrittr_2.0.3 clue_0.3-65 GetoptLong_1.0.5 compiler_4.4.2 DelayedMatrixStats_1.26.0 +## [7] png_0.1-8 vctrs_0.6.5 stringr_1.5.1 pkgconfig_2.0.3 shape_1.4.6.1 crayon_1.5.3 +## [13] fastmap_1.2.0 XVector_0.44.0 scuttle_1.14.0 labeling_0.4.3 utf8_1.2.4 rmarkdown_2.27 +## [19] tzdb_0.4.0 UCSC.utils_1.0.0 purrr_1.0.2 bit_4.0.5 xfun_0.46 bluster_1.14.0 +## [25] cachem_1.1.0 zlibbioc_1.50.0 beachmat_2.20.0 jsonlite_1.8.8 highr_0.11 DelayedArray_0.30.1 +## [31] BiocParallel_1.38.0 parallel_4.4.2 cluster_2.1.6 R6_2.5.1 bslib_0.8.0 stringi_1.8.4 +## [37] RColorBrewer_1.1-3 jquerylib_0.1.4 Rcpp_1.0.13 iterators_1.0.14 knitr_1.48 readr_2.1.5 +## [43] Matrix_1.7-0 igraph_2.0.3 tidyselect_1.2.1 abind_1.4-5 yaml_2.3.10 doParallel_1.0.17 +## [49] codetools_0.2-20 lattice_0.22-6 tibble_3.2.1 withr_3.0.0 evaluate_0.24.0 circlize_0.4.16 +## [55] pillar_1.9.0 BiocManager_1.30.23 renv_1.0.7 foreach_1.5.2 generics_0.1.3 vroom_1.6.5 +## [61] rprojroot_2.0.4 hms_1.1.3 sparseMatrixStats_1.16.0 munsell_0.5.1 scales_1.3.0 glue_1.7.0 +## [67] tools_4.4.2 BiocNeighbors_1.22.0 forcats_1.0.0 fs_1.6.4 grid_4.4.2 tidyr_1.3.1 +## [73] colorspace_2.1-1 GenomeInfoDbData_1.2.12 patchwork_1.2.0 cli_3.6.3 fansi_1.0.6 S4Arrays_1.4.1 +## [79] viridisLite_0.4.2 ComplexHeatmap_2.20.0 dplyr_1.1.4 gtable_0.3.5 sass_0.4.9 digest_0.6.36 +## [85] SparseArray_1.4.8 rjson_0.2.21 farver_2.1.2 htmltools_0.5.8.1 lifecycle_1.0.4 httr_1.4.7 ## [91] GlobalOptions_0.1.2 bit64_4.0.5 diff --git a/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R b/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R index f5ce0a2e2..879bd786e 100644 --- a/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R +++ b/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R @@ -154,6 +154,42 @@ calculate_sum_markers <- function(marker_genes_df, } +# calculate the mean of expression for all markers in a given cell type +# takes as input the marker gene df with `cell_type` and `ensembl_gene_id` as columns +# For any genes that are in the specified `cell_type`, sum of the logcounts is calculated +# output is a data frame with barcodes and `{cell_type}_sum` +calculate_mean_markers <- function(marker_genes_df, + sce, + type, + cell_type_column = cell_type) { + # get list of marker genes to use + marker_genes <- marker_genes_df |> + dplyr::filter({{cell_type_column}} == type) |> + dplyr::pull(ensembl_gene_id) + + # get the gene expression counts for all marker genes + mean_exp <- logcounts(sce[marker_genes, ]) |> + as.matrix() |> + t() |> + rowMeans() + + df <- data.frame( + barcodes = names(mean_exp), + sum_exp = mean_exp + ) + + # get rid of extra " cells" at end of some of the names + type <- stringr::str_remove(type, " cells") + + colnames(df) <- c( + "barcodes", + # add ref name to colnames for easier joining + glue::glue("{type}_mean") + ) + + return(df) +} + # Heatmaps --------------------------------------------------------------------- @@ -264,7 +300,7 @@ full_celltype_heatmap <- function(classification_df, tibble::column_to_rownames("barcodes") |> as.matrix() |> t() - rownames(heatmap_mtx) <- stringr::str_remove(rownames(heatmap_mtx), "_sum|mean-") + rownames(heatmap_mtx) <- stringr::str_remove(rownames(heatmap_mtx), "_sum|mean-|_mean") # plot heatmap of marker genes plot_gene_heatmap(heatmap_mtx, From 51ad6e2914e8924354de3f8866b1c2ebb7c38cb1 Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Mon, 13 Jan 2025 10:55:07 -0600 Subject: [PATCH 10/15] respond to reviews --- .../exploratory_analysis/06-tumor-cell-state-assignment.Rmd | 5 ++++- .../scripts/utils/tumor-validation-helpers.R | 2 +- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd index a58446a5c..20ae8e43c 100644 --- a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd +++ b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd @@ -10,7 +10,7 @@ output: --- In this notebook we look at expression of potential markers for tumor cell states in two libraries from `SCPCP000015`: `SCPCL000822` and `SCPCL000824`. -The expression of specific marker genes are then used to classify tumor cells into three different tumor cell states: `EWS-low`, `EWS-mid`, and `EWS-high`. +The expression of specific marker genes are then used to classify tumor cells into three different tumor cell states: `EWS-low`, `EWS-mid`, `EWS-high`, and `EWS-high-proliferative`. We did this by: @@ -509,11 +509,13 @@ Perhaps we should label those cells separately as `EWS-high-proliferative`. Based on this I would make the following assignments: `SCPCL000822`: + `EWS-FLI1` low - 2 `EWS-FLI1` middle - 1 & 4 `EWS-FLI1` high - 3 `SCPCL000824` : + `EWS-FLI1` low - 1 `EWS-FLI1` middle - 6 `EWS-FLI1` high - 3, 4, 5, 7 @@ -617,6 +619,7 @@ final_classification_df |> ## Conclusions - There is a clear division between cells that are `EWS-FLI` high and `EWS-FLI1` low. + - We also see a set of cells that are both "proliferative" and `EWS-FLI` high. - Most tumor cells in both samples are `EWS-FLI` high, which is consistent with the literature. - There may be a class of cells that live in between high and low, but if we want to define those we probably need to be more stringent with our approach and identify a mechanism to bin cells based on expression of both `EWS-FLI1` activated and repressed genes, which is outside the scope of this notebook. diff --git a/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R b/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R index 879bd786e..ed5124a2b 100644 --- a/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R +++ b/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R @@ -175,7 +175,7 @@ calculate_mean_markers <- function(marker_genes_df, df <- data.frame( barcodes = names(mean_exp), - sum_exp = mean_exp + mean_exp = mean_exp ) # get rid of extra " cells" at end of some of the names From 811b3ad0b4b90a68ce0232106938eaed69c55601 Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Mon, 13 Jan 2025 10:56:57 -0600 Subject: [PATCH 11/15] add top2a, icam1 and vrk1 --- .../cell-type-ewings/references/tumor-cell-state-markers.tsv | 3 +++ 1 file changed, 3 insertions(+) diff --git a/analyses/cell-type-ewings/references/tumor-cell-state-markers.tsv b/analyses/cell-type-ewings/references/tumor-cell-state-markers.tsv index ca0ad997e..31863add0 100644 --- a/analyses/cell-type-ewings/references/tumor-cell-state-markers.tsv +++ b/analyses/cell-type-ewings/references/tumor-cell-state-markers.tsv @@ -1,6 +1,7 @@ cell_state gene_symbol ensembl_gene_id source proliferative MKI67 ENSG00000148773 https://doi.org/10.1101/2024.01.18.576251;https://doi.org/10.1158/1078-0432.CCR-23-1111 proliferative PCNA ENSG00000132646 https://doi.org/10.1101/2024.01.18.576251 +proliferative TOP2A ENSG00000131747 https://doi.org/10.1101/2024.01.18.576251 EWS-low NT5E ENSG00000135318 https://doi.org/10.1158/1078-0432.CCR-23-1111 EWS-low IGFBP3 ENSG00000146674 https://doi.org/10.1016/j.celrep.2020.01.049 EWS-low IL8 ENSG00000169429 https://doi.org/10.1016/j.celrep.2020.01.049 @@ -13,7 +14,9 @@ EWS-low S100A10 ENSG00000197747 https://doi.org/10.1158/1078-0432.CCR-23-1111 EWS-low SPARC ENSG00000113140 https://doi.org/10.1158/1078-0432.CCR-23-1111 EWS-low COL1A2 ENSG00000164692 https://doi.org/10.1158/1078-0432.CCR-23-1111 EWS-low CD44 ENSG00000026508 https://doi.org/10.1101/2024.01.18.576251 +EWS-low ICAM1 ENSG00000090339 https://doi.org/10.1038/onc.2016.498 EWS-high PRKCB ENSG00000166501 https://doi.org/10.1016/j.celrep.2020.01.049 EWS-high LIPI ENSG00000188992 https://doi.org/10.1016/j.celrep.2020.01.049 EWS-high CCND1 ENSG00000110092 https://doi.org/10.1016/j.celrep.2020.01.049 EWS-high NR0B1 ENSG00000169297 https://doi.org/10.1016/j.celrep.2020.01.049 +EWS-high VRK1 ENSG00000100749 https://doi.org/10.1016/j.ccell.2014.10.004 From 85386f6708f320c777a9e5b9100508a5522b85fc Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Mon, 13 Jan 2025 10:57:08 -0600 Subject: [PATCH 12/15] re-render --- .../06-tumor-cell-state-assignment.html | 83 ++++++++++--------- 1 file changed, 44 insertions(+), 39 deletions(-) diff --git a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html index f99c1d026..ef4b2b214 100644 --- a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html +++ b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html @@ -11,7 +11,7 @@ - + Explore expression of marker genes for tumor cell states @@ -441,7 +441,7 @@

      Explore expression of marker genes for tumor cell states

      Ally Hawkins

      -

      2025-01-10

      +

      2025-01-13

      @@ -469,7 +469,7 @@

      2025-01-10

      SCPCL000822 and SCPCL000824. The expression of specific marker genes are then used to classify tumor cells into three different tumor cell states: EWS-low, EWS-mid, -and EWS-high.

      +EWS-high, and EWS-high-proliferative.

      We did this by:

      • Evaluating marker gene expression for different tumor cell states @@ -793,13 +793,13 @@

        Look at marker gene expression for tumor cell states in all ) }) 
        ## [[1]]
        -

        +

        ## 
 ## [[2]]
        -

        +

        ## 
 ## [[3]]
        -

        +

        ## 
 ## [[4]]

        @@ -858,10 +858,10 @@

        Classify tumor cell states

        }) 
        ## [[1]]
        -

        +

        ## 
 ## [[2]]
        -

        +

        A few notes from this:

        • It doesn’t look like very many cells express proliferative markers @@ -966,10 +966,10 @@

          Sub cluster tumor cells

          }) 
          ## [[1]]
          -

          +

          ## 
 ## [[2]]
          -

          +

          In all of the heatmaps below, the annotation column is the sub_cluster.

          library_ids |> 
@@ -982,10 +982,10 @@ 
          Sub cluster tumor cells
          
     
   })
          ## [[1]]
          -

          +

          ## 
 ## [[2]]
          -

          +

          Let’s make the same heatmap, but remove the column clustering.

          library_ids |> 
   purrr::map(\(id){
@@ -998,10 +998,10 @@ 
          Sub cluster tumor cells
          
     
   })
          ## [[1]]
          -

          +

          ## 
 ## [[2]]
          -

          +

          It looks like for both samples we have some clear division between EWS high and EWS low cells. Additionally, there are some cells with really strong expression of the EWS high markers and others that look @@ -1017,12 +1017,13 @@

          Sub cluster tumor cells

          has expression of the proliferative cells. Perhaps we should label those cells separately as EWS-high-proliferative.

          Based on this I would make the following assignments:

          -

          SCPCL000822: EWS-FLI1 low - 2 -EWS-FLI1 middle - 1 & 4 EWS-FLI1 high - -3

          -

          SCPCL000824 : EWS-FLI1 low - 1 -EWS-FLI1 middle - 6 EWS-FLI1 high - 3, 4, 5, 7 -EWS-FLI1 high proliferative - 2

          +

          SCPCL000822:

          +

          EWS-FLI1 low - 2 EWS-FLI1 middle - 1 & +4 EWS-FLI1 high - 3

          +

          SCPCL000824 :

          +

          EWS-FLI1 low - 1 EWS-FLI1 middle - 6 +EWS-FLI1 high - 3, 4, 5, 7 EWS-FLI1 high +proliferative - 2

          # categorize tumor cells based on EWS expression 
 tumor_cells_df <- tumor_cells_df |> 
   dplyr::mutate(
@@ -1050,10 +1051,10 @@ 
          Sub cluster tumor cells
          
     
   })
          ## [[1]]
          -

          +

          ## 
 ## [[2]]
          -

          +

          I think that looks pretty reasonable. I’m not sure we can really make more definitive calls given the gene lists that we have, unless we use fancier approaches. We could try something like AUCell, but @@ -1125,7 +1126,11 @@

          Combined classifications

          Conclusions

          • There is a clear division between cells that are -EWS-FLI high and EWS-FLI1 low.
            • +EWS-FLI high and EWS-FLI1 low. +
              +
            • We also see a set of cells that are both “proliferative” and +EWS-FLI high.
              • +
          • Most tumor cells in both samples are EWS-FLI high, which is consistent with the literature.
          • There may be a class of cells that live in between high and low, but @@ -1161,22 +1166,22 @@

            Session info

            ## [7] IRanges_2.38.1 S4Vectors_0.42.1 BiocGenerics_0.50.0 MatrixGenerics_1.16.0 matrixStats_1.3.0 ## ## loaded via a namespace (and not attached): -## [1] rlang_1.1.4 magrittr_2.0.3 clue_0.3-65 GetoptLong_1.0.5 compiler_4.4.2 DelayedMatrixStats_1.26.0 -## [7] png_0.1-8 vctrs_0.6.5 stringr_1.5.1 pkgconfig_2.0.3 shape_1.4.6.1 crayon_1.5.3 -## [13] fastmap_1.2.0 XVector_0.44.0 scuttle_1.14.0 labeling_0.4.3 utf8_1.2.4 rmarkdown_2.27 -## [19] tzdb_0.4.0 UCSC.utils_1.0.0 purrr_1.0.2 bit_4.0.5 xfun_0.46 bluster_1.14.0 -## [25] cachem_1.1.0 zlibbioc_1.50.0 beachmat_2.20.0 jsonlite_1.8.8 highr_0.11 DelayedArray_0.30.1 -## [31] BiocParallel_1.38.0 parallel_4.4.2 cluster_2.1.6 R6_2.5.1 bslib_0.8.0 stringi_1.8.4 -## [37] RColorBrewer_1.1-3 jquerylib_0.1.4 Rcpp_1.0.13 iterators_1.0.14 knitr_1.48 readr_2.1.5 -## [43] Matrix_1.7-0 igraph_2.0.3 tidyselect_1.2.1 abind_1.4-5 yaml_2.3.10 doParallel_1.0.17 -## [49] codetools_0.2-20 lattice_0.22-6 tibble_3.2.1 withr_3.0.0 evaluate_0.24.0 circlize_0.4.16 -## [55] pillar_1.9.0 BiocManager_1.30.23 renv_1.0.7 foreach_1.5.2 generics_0.1.3 vroom_1.6.5 -## [61] rprojroot_2.0.4 hms_1.1.3 sparseMatrixStats_1.16.0 munsell_0.5.1 scales_1.3.0 glue_1.7.0 -## [67] tools_4.4.2 BiocNeighbors_1.22.0 forcats_1.0.0 fs_1.6.4 grid_4.4.2 tidyr_1.3.1 -## [73] colorspace_2.1-1 GenomeInfoDbData_1.2.12 patchwork_1.2.0 cli_3.6.3 fansi_1.0.6 S4Arrays_1.4.1 -## [79] viridisLite_0.4.2 ComplexHeatmap_2.20.0 dplyr_1.1.4 gtable_0.3.5 sass_0.4.9 digest_0.6.36 -## [85] SparseArray_1.4.8 rjson_0.2.21 farver_2.1.2 htmltools_0.5.8.1 lifecycle_1.0.4 httr_1.4.7 -## [91] GlobalOptions_0.1.2 bit64_4.0.5 +## [1] tidyselect_1.2.1 viridisLite_0.4.2 dplyr_1.1.4 farver_2.1.2 fastmap_1.2.0 bluster_1.14.0 +## [7] digest_0.6.36 lifecycle_1.0.4 cluster_2.1.6 magrittr_2.0.3 compiler_4.4.2 rlang_1.1.4 +## [13] sass_0.4.9 tools_4.4.2 igraph_2.0.3 utf8_1.2.4 yaml_2.3.10 knitr_1.48 +## [19] S4Arrays_1.4.1 labeling_0.4.3 bit_4.0.5 DelayedArray_0.30.1 RColorBrewer_1.1-3 abind_1.4-5 +## [25] BiocParallel_1.38.0 withr_3.0.0 purrr_1.0.2 grid_4.4.2 fansi_1.0.6 beachmat_2.20.0 +## [31] colorspace_2.1-1 iterators_1.0.14 scales_1.3.0 cli_3.6.3 rmarkdown_2.27 crayon_1.5.3 +## [37] generics_0.1.3 rjson_0.2.21 httr_1.4.7 tzdb_0.4.0 DelayedMatrixStats_1.26.0 scuttle_1.14.0 +## [43] cachem_1.1.0 stringr_1.5.1 zlibbioc_1.50.0 parallel_4.4.2 BiocManager_1.30.23 XVector_0.44.0 +## [49] vctrs_0.6.5 Matrix_1.7-0 jsonlite_1.8.8 GetoptLong_1.0.5 hms_1.1.3 patchwork_1.2.0 +## [55] BiocNeighbors_1.22.0 bit64_4.0.5 clue_0.3-65 foreach_1.5.2 jquerylib_0.1.4 tidyr_1.3.1 +## [61] glue_1.7.0 codetools_0.2-20 shape_1.4.6.1 stringi_1.8.4 gtable_0.3.5 UCSC.utils_1.0.0 +## [67] ComplexHeatmap_2.20.0 munsell_0.5.1 tibble_3.2.1 pillar_1.9.0 htmltools_0.5.8.1 circlize_0.4.16 +## [73] GenomeInfoDbData_1.2.12 R6_2.5.1 sparseMatrixStats_1.16.0 doParallel_1.0.17 rprojroot_2.0.4 vroom_1.6.5 +## [79] evaluate_0.24.0 lattice_0.22-6 readr_2.1.5 highr_0.11 png_0.1-8 renv_1.0.7 +## [85] bslib_0.8.0 Rcpp_1.0.13 SparseArray_1.4.8 xfun_0.46 GlobalOptions_0.1.2 fs_1.6.4 +## [91] forcats_1.0.0 pkgconfig_2.0.3 From dc3ec761cfc3eb713bd3a45b6aaface5083e2186 Mon Sep 17 00:00:00 2001 From: Ally Hawkins <54039191+allyhawkins@users.noreply.github.com> Date: Mon, 13 Jan 2025 10:58:05 -0600 Subject: [PATCH 13/15] Use type Co-authored-by: Stephanie Spielman --- .../scripts/utils/tumor-validation-helpers.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R b/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R index ed5124a2b..2b91eb870 100644 --- a/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R +++ b/analyses/cell-type-ewings/scripts/utils/tumor-validation-helpers.R @@ -155,9 +155,9 @@ calculate_sum_markers <- function(marker_genes_df, # calculate the mean of expression for all markers in a given cell type -# takes as input the marker gene df with `cell_type` and `ensembl_gene_id` as columns -# For any genes that are in the specified `cell_type`, sum of the logcounts is calculated -# output is a data frame with barcodes and `{cell_type}_sum` +# takes as input the marker gene df with `type` and `ensembl_gene_id` as columns +# For any genes that are in the specified `type`, sum of the logcounts is calculated +# output is a data frame with barcodes and `{type}_sum` calculate_mean_markers <- function(marker_genes_df, sce, type, From b402db77a451043601c028ee3f123e60ac278923 Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Mon, 13 Jan 2025 12:14:22 -0600 Subject: [PATCH 14/15] add new refs to readme --- analyses/cell-type-ewings/references/README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/analyses/cell-type-ewings/references/README.md b/analyses/cell-type-ewings/references/README.md index d0270e5da..6d58bd2db 100644 --- a/analyses/cell-type-ewings/references/README.md +++ b/analyses/cell-type-ewings/references/README.md @@ -54,7 +54,13 @@ Each library contains a folder with any annotations file used to run `InferCNV` The `tumor-cell-state-markers.tsv` file contains a list of marker genes that can be used to classify tumor cell states in Ewing samples. The marker genes included here are specific to EWS-FLI1 high, EWS-FLI1 low, and proliferative tumor cells. -This list was obtained based on key genes mentioned in [Goodspeed _et al._](https://doi.org/10.1101/2024.01.18.576251), [Aynaud _et al._](https://doi.org/10.1016/j.celrep.2020.01.049), and [Wrenn _et al._](https://doi.org/10.1158/1078-0432.CCR-23-1111). +This list was obtained based on key genes mentioned in the following publications: + +- [Goodspeed _et al._](https://doi.org/10.1101/2024.01.18.576251) +- [Aynaud _et al._](https://doi.org/10.1016/j.celrep.2020.01.049) +- [Wrenn _et al._](https://doi.org/10.1158/1078-0432.CCR-23-1111) +- [Franzetti _et al._](https://doi.org/10.1038/onc.2016.498) +- [Riggi _et al._](https://doi.org/10.1016/j.ccell.2014.10.004) ### Gene signatures From 7888eb94cc70bb8b34bde609b1ce09524f666ecd Mon Sep 17 00:00:00 2001 From: Ally Hawkins Date: Mon, 13 Jan 2025 12:16:35 -0600 Subject: [PATCH 15/15] actually use bullets and re-render --- .../06-tumor-cell-state-assignment.Rmd | 14 +++++++------- .../06-tumor-cell-state-assignment.html | 16 +++++++++++----- 2 files changed, 18 insertions(+), 12 deletions(-) diff --git a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd index 20ae8e43c..4f652eff2 100644 --- a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd +++ b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.Rmd @@ -510,16 +510,16 @@ Based on this I would make the following assignments: `SCPCL000822`: -`EWS-FLI1` low - 2 -`EWS-FLI1` middle - 1 & 4 -`EWS-FLI1` high - 3 +- `EWS-FLI1` low - 2 +- `EWS-FLI1` middle - 1 & 4 +- `EWS-FLI1` high - 3 `SCPCL000824` : -`EWS-FLI1` low - 1 -`EWS-FLI1` middle - 6 -`EWS-FLI1` high - 3, 4, 5, 7 -`EWS-FLI1` high proliferative - 2 +- `EWS-FLI1` low - 1 +- `EWS-FLI1` middle - 6 +- `EWS-FLI1` high - 3, 4, 5, 7 +- `EWS-FLI1` high proliferative - 2 ```{r} # categorize tumor cells based on EWS expression diff --git a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html index ef4b2b214..26232900e 100644 --- a/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html +++ b/analyses/cell-type-ewings/exploratory_analysis/06-tumor-cell-state-assignment.html @@ -1018,12 +1018,18 @@

            Sub cluster tumor cells

            cells separately as EWS-high-proliferative.

            Based on this I would make the following assignments:

            SCPCL000822:

            -

            EWS-FLI1 low - 2 EWS-FLI1 middle - 1 & -4 EWS-FLI1 high - 3

            +
              +
            • EWS-FLI1 low - 2
              • +
            • EWS-FLI1 middle - 1 & 4
              • +
            • EWS-FLI1 high - 3
              • +

            SCPCL000824 :

            -

            EWS-FLI1 low - 1 EWS-FLI1 middle - 6 -EWS-FLI1 high - 3, 4, 5, 7 EWS-FLI1 high -proliferative - 2

            +
              +
            • EWS-FLI1 low - 1
              • +
            • EWS-FLI1 middle - 6
              • +
            • EWS-FLI1 high - 3, 4, 5, 7
              • +
            • EWS-FLI1 high proliferative - 2
              • +
            # categorize tumor cells based on EWS expression 
 tumor_cells_df <- tumor_cells_df |> 
   dplyr::mutate(