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Knit directory: single-cell-jamboree/analysis/

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Data

This single cell RNA data is available here, as studied in Srivatsan et al, 2021.

The dataset considers 108725 cells and 39198 genes (after QC), measured with spatial locations in the mouse embryo.

library(Seurat)
library(Matrix)
library(data.table)
library(flashier)
library(ggplot2)
library(patchwork)
library(cowplot)
library(RColorBrewer)
library(Biobase)
library(ggpubr)
library(gridExtra)
library(fastTopics)
source('../code/plot_loadings_on_umap.R')
source("../code/Customized_Plots.R")
seurat_object <- readRDS("../data/mouse_embryo/processed_seurat/seurat_object.rds")
Y <- t(seurat_object$RNA$data)

The data contains the UMAP information that we can directly use for visualization, and compare with the “anatomical annotation” and the “cluster label” in Srivatsan et al, 2021.

umap_original_embeddings <- cbind(seurat_object$umap1, seurat_object$umap2)
p1 <- DimPlotSagnik(umap_original_embeddings, group.by = seurat_object$anatomical_annotation, pt.size = 1) +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("UMAP by Anatomical Annotation")

p2 <- DimPlotSagnik(umap_original_embeddings, group.by = seurat_object$final_cluster_label, pt.size = 1) +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("UMAP by Cluster Label")

# Combine plots side-by-side
combined_plot <- p1 + p2 + plot_layout(ncol = 2)
combined_plot

Version Author Date
4a09688 Ziang Zhang 2024-11-15

Trying EBMF

Let’s try to implement the EBMF algorithm with the flashier package, with different constraints and prior distributions.

cols_to_keep <- colSums(Y != 0, na.rm = TRUE) > 0
Y <- Y[, cols_to_keep]
n  <- nrow(Y)
x  <- rpois(1e7, 1/n)
s1 <- sd(log(x + 1))
a <- 1
size_factors <- rowSums(Y)
size_factors <- size_factors / mean(size_factors)
# shifted_log_counts <- log1p(Y / (a * size_factors))
shifted_log_counts <- MatrixExtra::mapSparse(Y/(a*size_factors),log1p)

Non-Negative EBMF

For the first EBMF problem, we consider non-negative EBMF with point_exponential priors:

flashier_fit_nn <- flash(shifted_log_counts,
                      ebnm_fn = ebnm_point_exponential,
                      var_type = 2,
                      greedy_Kmax = 25,
                      S = s1,
                      backfit = T)
plot(flashier_fit_nn, 
     plot_type = "structure",
     pm_which = "loadings", 
     pm_groups = seurat_object$final_cluster_label,
     bins = 20, gap = 70)

Version Author Date
5c65d9b Ziang Zhang 2024-11-27
99a79f6 Ziang Zhang 2024-11-18
4a09688 Ziang Zhang 2024-11-15

Semi Non-Negative EBMF

ebnm_fn_list <- list()
ebnm_fn_list[[1]] <- flash_ebnm(
  prior_family = "point_exponential"
)
ebnm_fn_list[[2]] <- flash_ebnm(
  prior_family = "point_normal",
  mode = "estimate"
)
flashier_fit_semi <- flash(shifted_log_counts,
                      ebnm_fn = ebnm_fn_list,
                      var_type = 2,
                      greedy_Kmax = 25,
                      S = s1,
                      backfit = T)
plot(flashier_fit_semi, 
     plot_type = "structure",
     pm_which = "loadings", 
     pm_groups = seurat_object$final_cluster_label,
     bins = 20, gap = 70)

Version Author Date
5c65d9b Ziang Zhang 2024-11-27
99a79f6 Ziang Zhang 2024-11-18
4a09688 Ziang Zhang 2024-11-15

GB Semi Non-Negative EBMF

ebnm_fn_list <- list()
ebnm_fn_list[[1]] <- flash_ebnm(
  prior_family = "generalized_binary"
)
ebnm_fn_list[[2]] <- flash_ebnm(
  prior_family = "point_laplace",
  mode = "estimate"
)
flashier_fit_gbcd <- flash(shifted_log_counts,
                      ebnm_fn = ebnm_fn_list,
                      var_type = 2,
                      greedy_Kmax = 25,
                      S = s1,
                      backfit = T)
plot(flashier_fit_gbcd, 
     plot_type = "structure",
     pm_which = "loadings", 
     pm_groups = seurat_object$final_cluster_label,
     bins = 20, gap = 70)

Version Author Date
5c65d9b Ziang Zhang 2024-11-27
99a79f6 Ziang Zhang 2024-11-18
4a09688 Ziang Zhang 2024-11-15

GBCD

res.gbcd <- fit_gbcd(Y = Y, Kmax = 25, maxiter1 = 100,
                     maxiter2 = 50, maxiter3 = 50, 
                     prior = flash_ebnm(prior_family = "generalized_binary",
                                        scale = 0.04))
saveRDS(res.gbcd, "../output/mouse_embryo/res.gbcd.rds")

Trying Topic Models

Then, let’s try fitting a topic model to this dataset using FastTopics.

fasttopics_fit <- fit_topic_model(Y, k = 25)

Version Author Date
99a79f6 Ziang Zhang 2024-11-18

Summarize Structures from Loadings

There are some interesting structures of the loading based on the result of EBMF and FastTopics, that add new insights to the original UMAP visualization.

Non-negative EBMF

First, based on the non-negative EBMF result, there are some factors that are unique to certain cell types. For example:

  • The factor k9 (mostly) is unique to Endothelial Cells.

  • The factor k13 is unique to Choroid Plexus.

  • The factor k15 is unique to White Blood Cells.

Version Author Date
62d5728 Ziang Zhang 2024-11-27
5c65d9b Ziang Zhang 2024-11-27
99a79f6 Ziang Zhang 2024-11-18

Shared factors include:

  • The factor k2 is shared across Erythroid Progenitors and Hepatocytes.

  • The factors k3 and k10 are shared across multiple types of neurons and glial cells.

  • The factor k4 is shared across Cardiac Muscle lineages and Myocytes.

  • The factor k7 is shared across Erythroid Progenitors and White Blood Cells.

Version Author Date
62d5728 Ziang Zhang 2024-11-27
5c65d9b Ziang Zhang 2024-11-27
99a79f6 Ziang Zhang 2024-11-18

Semi-negative EBMF

The loadings from the semi-negative EBMF are more diverse, where each cell is composed of a larger number of factors. However, there are still both “unique” and “shared” factors across different cell types. For examples:

Specific factors include k3, k12, k14, k16, k19, k23:

  • Factor k3 is (mostly) unique to Myocytes.

  • Factor k12 is (mostly) unique to Chondrocytes (also appears in Connective Tissue Progenitors).

  • Factor k14 is unique to Hepatocytes.

  • Factor k16 is unique to Cardiac Muscle lineages.

  • Factor k19 is unique to Choroid Plexus.

  • Factor k23 is unique to White Blood Cells.

Version Author Date
5c65d9b Ziang Zhang 2024-11-27
99a79f6 Ziang Zhang 2024-11-18

Shared factors include k2, k4, k9, k11:

  • Factor k2 is shared across Erythroid Progenitors and Hepatocytes.

  • Factor k4 (as well as k11) is shared across multiple types of neurons and glial cells.

  • Factor k9 is shared across some clusters of the white blood cells and Erythroid cells.

Version Author Date
5c65d9b Ziang Zhang 2024-11-27

FastTopics

The result from FastTopics looks quite similar to the semi-negative EBMF, where each cell is composed of a larger number of factors.

For examples, some shared factors include:

  • Factor k3 is shared between Erythroid Lineage and Hepatocytes.

  • Factor k7 is shared between Peripheral Neuron and Schwann Cells.

  • Factors k10 is shared between Developing Guts and Epithelial Cells.

  • Factor k13 is shared between Erythroid Progenitors, Hepatocytes and White blood cells.

  • Factor k15 is shared between Chondrocytes and Connective Tissue Progenitors.

  • Factor k16 is shared between Glial cells and Peripheral Neuron.

Version Author Date
5c65d9b Ziang Zhang 2024-11-27

For unique factors, we can see:

  • Factor k4 is (mostly) unique to Myocytes.

  • Factor k5 is (mostly) unique to Neuron.

  • Factor k8 is unique to White Blood Cells.

  • Factor k9 is unique to Endothelial Cells.

  • Factor k14 is unique to Neuron.

  • Factor k18 is unique to Cardiac Muscle lineages.

Version Author Date
5c65d9b Ziang Zhang 2024-11-27

Compare these methods

At the same time, non-negative EBMF also has smaller number of “interpretable” factors compared to the semi-negative EBMF and FastTopics. For example, if we count the number of factors that appear (with loading value greater than say 1e-10) at least in 10 cells, we have:

nn_factors <- colSums(abs(flashier_fit_nn$L_pm) > 1e-10) > 10
table(nn_factors)
nn_factors
FALSE  TRUE 
    6    19 
semi_factors <- colSums(abs(flashier_fit_semi$L_pm) > 1e-10) > 10
table(semi_factors)
semi_factors
TRUE 
  25 
ft_factors <- colSums(abs(fasttopics_fit$L) > 1e-10) > 10
table(ft_factors)
ft_factors
TRUE 
  25 

This is a good thing if we want factors and loadings with very clear interpretation. However, it might also lead to a loss of information for some more subtle structures in the data.

More detailed exploration of NN-EBMF

gene_ids <- rownames(flashier_fit_nn$F_pm)
res <- ldf(flashier_fit_nn, type = "i")
F <- with(res, F %*% diag(D))
# let's make the gene names more readable
# gene_ids <- gsub("\\..*", "", gene_ids)
rownames(flashier_fit_nn$F_pm) <- gene_ids

For now, let’s focus on the result from the non-negative EBMF. First, let’s take a look at the structure plot of each factor from nn-EBMF. To make the visualization more clear, we only show the top 4 genes that contribute the most to each factor.

factor_of_interest <- c(2, 3, 9, 13, 15)
top_genes_mat <- apply(F, 2, order, decreasing = TRUE)[1:4, factor_of_interest]
top_genes <- unique(rownames(flashier_fit_nn$F_pm)[top_genes_mat])
plot(flashier_fit_nn,
     plot_type = "heatmap",
     pm_which = "factors",
     pm_subset = top_genes,
     pm_groups = factor(top_genes, levels = rev(top_genes)),
     kset = factor_of_interest,
     gap = 0.2)

Version Author Date
62d5728 Ziang Zhang 2024-11-27

For the factor k2 that is shared across Erythroid Progenitors and Hepatocytes, the leading gene is ENSMUSG00000052305, which corresponds to the Hbb-bs gene in Mus musculus (mouse), encoding the hemoglobin, beta adult s chain. This gene is a part of the beta-globin cluster and plays a crucial role in oxygen transport from the lungs to peripheral tissues. It is predominantly expressed in tissues involved in hematopoiesis. It is reasonable that this gene is highly expressed in Erythroid Progenitors, but it is kind of surprising that it is also highly expressed in Hepatocytes. Maybe this implies there exists some annotation error in the cell types of the original data.

For the factor k3 that is shared across multiple types of neurons and glial cells, the leading gene is ENSMUSG00000072235. ENSMUSG00000072235, corresponds to Tuba1a, encoding the alpha tubulin protein in Mus musculus (mouse). This gene is known for its ubiquitous expression, with particularly high levels in brain and lungs.

For the factor k9 that is unique to Endothelial Cells, the leading gene is ENSMUSG00000031502, which corresponds to the Col4a1 gene in Mus musculus (mouse), encoding the collagen, type IV, alpha 1 protein. This protein is a crucial component of the basement membrane.

For the factor k13 that is unique to Choroid Plexus, the leading gene is ENSMUSG00000061808. It corresponds to the Ttr gene in Mus musculus (mouse), encoding the protein transthyretin, a transport protein primarily involved in the distribution of the thyroid hormone thyroxine and retinol (vitamin A). It is predominantly synthesized in the liver and the choroid plexus of the brain.

For the factor k15 that is unique to White Blood Cells, the leading gene is ENSMUSG00000049744, which corresponds to the ArhGAP15 gene in Mus musculus (mouse), encoding the Rho GTPase-activating protein 15. Existing literature has reported that “Knock-out of Arhgap15 function demonstrates that this gene is required to regulate multiple functions in macrophages and neutrophils.”


sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.7.4

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] fastTopics_0.6-192  gridExtra_2.3       ggpubr_0.6.0       
 [4] Biobase_2.62.0      BiocGenerics_0.48.1 RColorBrewer_1.1-3 
 [7] cowplot_1.1.3       patchwork_1.3.0     ggplot2_3.5.1      
[10] flashier_1.0.54     ebnm_1.1-34         data.table_1.16.2  
[13] Matrix_1.6-4        Seurat_5.1.0        SeuratObject_5.0.2 
[16] sp_2.1-4           

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22       splines_4.3.1          later_1.3.2           
  [4] tibble_3.2.1           polyclip_1.10-7        fastDummies_1.7.4     
  [7] lifecycle_1.0.4        mixsqp_0.3-54          rstatix_0.7.2         
 [10] rprojroot_2.0.4        globals_0.16.3         lattice_0.22-6        
 [13] MASS_7.3-60            backports_1.5.0        magrittr_2.0.3        
 [16] plotly_4.10.4          sass_0.4.9             rmarkdown_2.28        
 [19] jquerylib_0.1.4        yaml_2.3.10            httpuv_1.6.15         
 [22] sctransform_0.4.1      spam_2.11-0            spatstat.sparse_3.1-0 
 [25] reticulate_1.39.0      pbapply_1.7-2          abind_1.4-8           
 [28] Rtsne_0.17             quadprog_1.5-8         purrr_1.0.2           
 [31] git2r_0.33.0           ggrepel_0.9.6          irlba_2.3.5.1         
 [34] listenv_0.9.1          spatstat.utils_3.1-0   goftest_1.2-3         
 [37] RSpectra_0.16-2        spatstat.random_3.3-2  fitdistrplus_1.2-1    
 [40] parallelly_1.38.0      leiden_0.4.3.1         codetools_0.2-20      
 [43] tidyselect_1.2.1       farver_2.1.2           matrixStats_1.4.1     
 [46] spatstat.explore_3.3-3 jsonlite_1.8.9         progressr_0.14.0      
 [49] Formula_1.2-5          ggridges_0.5.6         survival_3.7-0        
 [52] tools_4.3.1            progress_1.2.3         ica_1.0-3             
 [55] Rcpp_1.0.13-1          glue_1.8.0             xfun_0.48             
 [58] dplyr_1.1.4            withr_3.0.2            fastmap_1.2.0         
 [61] fansi_1.0.6            digest_0.6.37          truncnorm_1.0-9       
 [64] R6_2.5.1               mime_0.12              colorspace_2.1-1      
 [67] scattermore_1.2        gtools_3.9.5           tensor_1.5            
 [70] spatstat.data_3.1-2    RhpcBLASctl_0.23-42    utf8_1.2.4            
 [73] tidyr_1.3.1            generics_0.1.3         prettyunits_1.2.0     
 [76] httr_1.4.7             htmlwidgets_1.6.4      scatterplot3d_0.3-44  
 [79] deconvolveR_1.2-1      whisker_0.4.1          uwot_0.1.16           
 [82] pkgconfig_2.0.3        gtable_0.3.6           workflowr_1.7.1       
 [85] lmtest_0.9-40          htmltools_0.5.8.1      carData_3.0-5         
 [88] dotCall64_1.2          horseshoe_0.2.0        scales_1.3.0          
 [91] png_0.1-8              spatstat.univar_3.0-1  ashr_2.2-66           
 [94] knitr_1.48             rstudioapi_0.16.0      reshape2_1.4.4        
 [97] nlme_3.1-166           cachem_1.1.0           zoo_1.8-12            
[100] Polychrome_1.5.1       stringr_1.5.1          KernSmooth_2.23-24    
[103] parallel_4.3.1         miniUI_0.1.1.1         softImpute_1.4-1      
[106] pillar_1.9.0           grid_4.3.1             vctrs_0.6.5           
[109] RANN_2.6.2             promises_1.3.0         car_3.1-3             
[112] xtable_1.8-4           cluster_2.1.6          evaluate_1.0.1        
[115] invgamma_1.1           cli_3.6.3              compiler_4.3.1        
[118] rlang_1.1.4            crayon_1.5.3           SQUAREM_2021.1        
[121] future.apply_1.11.2    ggsignif_0.6.4         labeling_0.4.3        
[124] plyr_1.8.9             fs_1.6.4               stringi_1.8.4         
[127] viridisLite_0.4.2      deldir_2.0-4           munsell_0.5.1         
[130] lazyeval_0.2.2         spatstat.geom_3.3-3    RcppHNSW_0.6.0        
[133] hms_1.1.3              future_1.34.0          shiny_1.9.1           
[136] highr_0.11             trust_0.1-8            ROCR_1.0-11           
[139] igraph_2.1.1           broom_1.0.7            RcppParallel_5.1.9    
[142] bslib_0.8.0