Enhanced Visualization and Exploration of Pancreas Data using Seurat

1. Load Libraries

Load the necessary libraries for data analysis, plotting, and Seurat analysis.

library(Seurat)
# Loading required package: SeuratObject
# Loading required package: sp
# 'SeuratObject' was built under R 4.4.0 but the current version is
# 4.4.1; it is recomended that you reinstall 'SeuratObject' as the ABI
# for R may have changed
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# version is 1.7.1; it is recomended that you reinstall 'SeuratObject' as
# the ABI for 'Matrix' may have changed
# 
# Attaching package: 'SeuratObject'
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library(tidyverse)
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library(patchwork)
library(cowplot)
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library(RColorBrewer)
library(Biobase)
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library(clusterSim)
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library(fpc)
library(ggpubr)
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2. Define Custom Functions

Define any custom functions for the analysis. This includes a customized plotting function for dimensional reduction (e.g., UMAP or PCA) and a function to create an elbow plot of the top variable features in a Seurat object.

# Customized dimensional reduction plot function (e.g., UMAP or PCA)
DimPlot <- function(
    data,
    dims = c(1, 2),         # Dimensions to plot (e.g., UMAP1 and UMAP2)
    cells = NULL,           # Subset of cells to plot
    cols = NULL,            # Colors for different groups
    pt.size = NULL,         # Point size for plotting
    reduction = NULL,       # Dimensional reduction technique (e.g., "umap" or "pca")
    group.by = NULL,        # Metadata column to color by
    split.by = NULL,        # Metadata column to facet by
    shape.by = NULL,        # Metadata column to shape by
    order = NULL,           # Custom order for plotting
    shuffle = FALSE,        # Option to shuffle data points to randomize plotting order
    seed = 1,               # Random seed for shuffling
    label = FALSE,          # Option to add labels to clusters
    label.size = 4,         # Size of cluster labels
    label.color = 'black',  # Color of the labels
    label.box = FALSE,      # Option to put labels in a box
    repel = FALSE,          # Use repelling labels to avoid overlap
    cells.highlight = NULL, # Subset of cells to highlight
    cols.highlight = '#DE2D26', # Color for highlighted cells
    sizes.highlight = 1,    # Size of highlighted cells
    na.value = 'grey50',    # Color for NA values
    ncol = NULL,            # Number of columns for faceting
    combine = TRUE,         # Combine plots into one
    raster = NULL,          # Option to use raster graphics
    raster.dpi = c(512, 512) # Resolution for raster graphics
) {
   if (length(x = dims) != 2) {
    stop("'dims' must be a two-length vector")
  }
  colnames(data) <- paste0("UMAP", dims)
  data <- as.data.frame(x = data)
  dims <- paste0("UMAP", dims)
  data <- cbind(data, group.by)
  orig.groups <- group.by
  group.by <- colnames(x = data)[3:ncol(x = data)]
  for (group in group.by) {
    if (!is.factor(x = data[, group])) {
      data[, group] <- factor(x = data[, group])
    }
  }
  if (!is.null(x = shape.by)) {
    data[, shape.by] <- object[[shape.by, drop = TRUE]]
  }
  if (!is.null(x = split.by)) {
    data[, split.by] <- object[[split.by, drop = TRUE]]
  }
  if (isTRUE(x = shuffle)) {
    set.seed(seed = seed)
    data <- data[sample(x = 1:nrow(x = data)), ]
  }
  plots <- lapply(
    X = group.by,
    FUN = function(x) {
      plot <- SingleDimPlot(
        data = data[, c(dims, x, split.by, shape.by)],
        dims = dims,
        col.by = x,
        cols = cols,
        pt.size = pt.size,
        shape.by = shape.by,
        order = order,
        label = FALSE,
        cells.highlight = cells.highlight,
        cols.highlight = cols.highlight,
        sizes.highlight = sizes.highlight,
        na.value = na.value,
        raster = raster,
        raster.dpi = raster.dpi
      )
      if (label) {
        plot <- LabelClusters(
          plot = plot,
          id = x,
          repel = repel,
          size = label.size,
          split.by = split.by,
          box = label.box,
          color = label.color
        )
      }
      if (!is.null(x = split.by)) {
        plot <- plot + FacetTheme() +
          facet_wrap(
            facets = vars(!!sym(x = split.by)),
            ncol = if (length(x = group.by) > 1 || is.null(x = ncol)) {
              length(x = unique(x = data[, split.by]))
            } else {
              ncol
            }
          )
      }
      plot <- if (is.null(x = orig.groups)) {
        plot + labs(title = NULL)
      } else {
        plot + labs(title = NULL)
      }
    }
  )
  if (!is.null(x = split.by)) {
    ncol <- 1
  }
  if (combine) {
    plots <- wrap_plots(plots, ncol = orig.groups %iff% ncol)
  }
  return(plots)
}


create_elbow_plot <- function(object, assay = "RNA", k = 5000) {
  # Get the high variable feature information
  hvf.info <- HVFInfo(object = object, assay = assay)
  
  # Sort the features by standardized variance in descending order
  sorted_standardized_variances <- hvf.info[order(hvf.info$variance.standardized, decreasing = TRUE), ]
  
  # Convert row names to a column named "Gene"
  sorted_standardized_variances <- sorted_standardized_variances %>%
    tibble::rownames_to_column("Gene") %>%
    mutate(Gene_Index = row_number())
  
  # Select the top k genes based on variance
  top_interest <- head(sorted_standardized_variances, k)
  
  # Create the elbow plot
  ggplot(top_interest, aes(x = Gene_Index, y = variance.standardized)) +
    geom_line() +
    labs(title = paste("Elbow Plot of Top", k, "Genes by Standardized Variance"),
         x = paste("Top", k, "Genes (ordered by variance)"),
         y = "Standardized Variance") +
    theme_minimal()
}

# Function to create an elbow plot for Seurat objects
create_elbow_plot <- function(object, assay = "RNA", k = 5000) {
  hvf.info <- HVFInfo(object = object, assay = assay)
  sorted_standardized_variances <- hvf.info[order(hvf.info$variance.standardized, decreasing = TRUE), ]
  sorted_standardized_variances <- sorted_standardized_variances %>%
    tibble::rownames_to_column("Gene") %>%
    mutate(Gene_Index = row_number())
  top_interest <- head(sorted_standardized_variances, k)
  ggplot(top_interest, aes(x = Gene_Index, y = variance.standardized)) +
    geom_line() +
    labs(
      title = paste("Elbow Plot of Top", k, "Genes by Standardized Variance"),
      x = paste("Top", k, "Genes (ordered by variance)"),
      y = "Standardized Variance"
    ) +
    theme_minimal()
}

3. Set Working Directory and Load Data

Set the working directory to the location of the pancreas.RData file, and load the dataset, which contains “counts” and “sample_info”.

#setwd("/path/to/directory") # Replace with your directory path
load("/Users/sagnik/Dropbox/Research Projects/Mathew Stephens/EBNMF/Pancreas/pancreas.RData")

4. Study Size Factors

Visualize the distribution of total counts (size factors) per cell in log scale.

s <- rowSums(counts)
pdat <- data.frame(log_size_factor = log10(s))
ggplot(pdat, aes(log_size_factor)) +
  geom_histogram(bins = 64, col = "black", fill = "black") +
  labs(x = "Log(Size Factor)") +
  theme_cowplot(font_size = 10)

Past versions of size-factors-1.png

5. Study Gene Expression Levels

Examine the distribution of gene expression levels in the dataset.

p <- counts[counts > 0]  # Filter non-zero counts
pdat <- data.frame(log_rel_expression_level = log10(p))
ggplot(pdat, aes(log_rel_expression_level)) +
  geom_histogram(bins = 64, col = "black", fill = "black") +
  labs(x = "Log-Expression Level (Relative)") +
  theme_cowplot(font_size = 10)

Past versions of gene-expression-1.png

6. Create Seurat Object

Create a Seurat object from the counts data, using sample_info as metadata. Normalize the data with a custom scale factor based on the mean size factor.

pancreas <- CreateSeuratObject(counts = t(counts), project = "pancreas", meta.data = sample_info)
scale_factor <- mean(s)
pancreas <- NormalizeData(pancreas, normalization.method = "LogNormalize", scale.factor = scale_factor)
# Normalizing layer: counts

7. Identify Variable Features

Identify the top 5,000 variable genes.

pancreas <- FindVariableFeatures(pancreas, selection.method = "vst", nfeatures = 5000)
# Finding variable features for layer counts
VariableFeaturePlot(pancreas)
# Warning in scale_x_log10(): log-10 transformation introduced infinite values.

Past versions of find-variable-features-1.png

8. Elbow Plot of Variable Features

Generate an elbow plot to analyze variance across the top variable features.

create_elbow_plot(object = pancreas, assay = "RNA", k = 5000)

Past versions of elbow-plot-1.png

9. Select Top 1,000 Variable Features

Select the top 1,000 variable genes for further analysis.

desired_genes <- head(VariableFeatures(pancreas), 1000)

10. Scale Data and Run PCA

Scale the data for the selected features and run PCA.

pancreas <- ScaleData(pancreas, features = desired_genes)
# Centering and scaling data matrix
pancreas <- RunPCA(pancreas, assay = "RNA", features = desired_genes)
# PC_ 1 
# Positive:  CHGA, VGF, PCSK1, CRYBA2, IAPP, ADCYAP1, PAPPA2, PRUNE2, EDN3, ELMO1 
#      SORL1, BMP5, DLK1, C10orf10, PLCB4, RBP4, LOXL4, KLHL41, NPY, PFKFB2 
#      PLCE1, PDK4, SLC25A6, WSCD2, TMED8, RGS16, PTP4A3, GPM6A, SLC25A34, WNK3 
# Negative:  ZFP36L1, PMEPA1, SERPING1, SOX4, MSN, TACSTD2, C1S, NFIB, LGALS1, FSTL1 
#      CFB, CLIC4, SERPINH1, ENC1, KRT7, LTBP1, CLDN1, UACA, PHLDA1, ITGA5 
#      COL4A1, SERPINA3, COL4A2, IL32, CD44, SDC4, C1R, FN1, PDZK1IP1, LCN2 
# PC_ 2 
# Positive:  SPARC, PDGFRB, BGN, COL15A1, COL6A2, COL1A2, COL5A1, COL3A1, AEBP1, SFRP2 
#      MRC2, LUM, LAMA4, COL5A2, THBS2, MMP2, THY1, TIMP3, IGFBP4, HTRA3 
#      CDH11, LRRC32, PXDN, COL4A1, NID1, LAMC3, DCN, COL6A3, VCAN, PRRX1 
# Negative:  SERPINA3, PDZK1IP1, TACSTD2, GATM, MUC1, LCN2, SDC4, KRT7, ANPEP, CFB 
#      KRT8, SPINK1, KRT18, PRSS1, CPA2, CLDN4, AKR1C3, PRSS8, TC2N, PNLIP 
#      REG1A, CLDN1, IL32, KLK1, CTRB1, CPA1, CTRC, TM4SF1, PLA2G1B, PRSS3 
# PC_ 3 
# Positive:  CFTR, VTCN1, TSPAN8, MMP7, AQP1, SPP1, SLC3A1, SLC4A4, HSD17B2, ALDH1A3 
#      ANXA3, CEACAM7, LGALS4, SERPINA5, SORL1, KRT23, TINAGL1, APCDD1, ANXA4, CTSH 
#      CLDN10, CD74, APCS, VCAM1, SLPI, PIGR, PRKCA, KRT19, CRP, PDLIM3 
# Negative:  CELA3B, CELA3A, CTRB1, SYCN, KLK1, PNLIPRP1, CLPS, CELA2A, CPA2, CTRC 
#      PLA2G1B, CPA1, CELA2B, CTRB2, CTRL, CUZD1, PNLIPRP2, PRSS1, GP2, CPB1 
#      PNLIP, PRSS3, REG3G, PDIA2, REG1B, RNASE1, AMY2A, MGST1, REG1A, SPINK1 
# PC_ 4 
# Positive:  PLVAP, CD93, KDR, PECAM1, ROBO4, ESM1, ECSCR, ACVRL1, VWF, FLT1 
#      ESAM, RGCC, S1PR1, ERG, CDH5, CXCR4, BCL6B, CLEC14A, PODXL, PTPRB 
#      PASK, ABI3, IFI27, CALCRL, ANGPT2, PCDH12, MYCT1, MMRN2, STC1, ACKR3 
# Negative:  LUM, SFRP2, COL1A2, THBS2, COL6A3, COL5A1, COL3A1, COL5A2, FMOD, VCAN 
#      DCN, LTBP2, COL1A1, CDH11, FN1, HEYL, TNFAIP6, ANTXR1, PRRX1, MXRA8 
#      HSD11B1, COL8A1, PDGFRB, ADAMTS12, PDGFRA, LAMC3, ISLR, WISP1, SPON2, WNT5A 
# PC_ 5 
# Positive:  EXPH5, MEG3, PEAK1, PLCB4, PFKFB2, PRUNE2, ANO5, ZNF33B, DGKB, INSR 
#      REG3A, CEL, TCF4, ZNF721, PNLIP, LYZ, SORL1, ENTPD3, ZNF124, BCAT1 
#      PTCHD4, USP54, COX15, GSTA2, ALB, NEAT1, NRG4, CHL1, CPB1, FLT1 
# Negative:  KRT19, SERPINA1, SERPING1, TFPI2, TINAGL1, MMP7, CCL2, PMEPA1, ALDH1A3, KRT23 
#      SLC25A6, VCAM1, CFTR, LGALS4, MYL9, HSPB1, SERPINA5, FSTL3, LAMB3, TGM2 
#      CXCL6, DDIT4, CTGF, KRT17, PLAUR, SERINC2, PRSS8, CXCL1, IGFBP7, VTCN1
ElbowPlot(pancreas, ndims = 50)

Past versions of scale-data-pca-1.png

11. Run UMAP

Perform UMAP using the top 25 principal components.

pancreas <- RunUMAP(pancreas, dims = 1:25)
# Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
# To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
# This message will be shown once per session
# 18:26:34 UMAP embedding parameters a = 0.9922 b = 1.112
# 18:26:34 Read 16382 rows and found 25 numeric columns
# 18:26:34 Using Annoy for neighbor search, n_neighbors = 30
# 18:26:34 Building Annoy index with metric = cosine, n_trees = 50
# 0%   10   20   30   40   50   60   70   80   90   100%
# [----|----|----|----|----|----|----|----|----|----|
# **************************************************|
# 18:26:35 Writing NN index file to temp file /var/folders/wc/v_nfs9816z5gz9p6y3_q53pm0000gn/T//RtmpAk3hOz/fileae544d87419c
# 18:26:35 Searching Annoy index using 1 thread, search_k = 3000
# 18:26:38 Annoy recall = 100%
# 18:26:38 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
# 18:26:38 Initializing from normalized Laplacian + noise (using RSpectra)
# 18:26:39 Commencing optimization for 200 epochs, with 716942 positive edges
# 18:26:43 Optimization finished
umap_embeddings <- Embeddings(object = pancreas, reduction = "umap")

12. Define Colors for Cell Types

Define a color vector for distinguishing 14 different cell types.

col_vector <- c(
  "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",
  "#FFFF33", "#A65628", "#999999", "#66C2A5", "#FC8D62",
  "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F"
)

13. Create UMAP Plots by Cell Type and Technology

Generate UMAP plots where cell types are represented by colors and technology by shapes.

# Create UMAP Plots by Cell Type and Technology
p1 <- DimPlot(umap_embeddings, group.by = sample_info$celltype, pt.size = 0.04, cols = col_vector) +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("UMAP of Pancreas Cells by Cell Type")
p2 <- DimPlot(umap_embeddings, group.by = sample_info$tech, pt.size = 0.04, cols = col_vector) +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("UMAP of Pancreas Cells by Technology")

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

Past versions of umap-plot-1.png

ggsave(
  filename = "combined_umap_plot.png",    # File name and format
  plot = combined_plot,                   # The plot object to save
  width = 15,                             # Width in inches
  height = 6,                             # Height in inches
  dpi = 300                               # Resolution in dots per inch (for high-quality output)
)

Session information

sessionInfo()
# R version 4.4.1 (2024-06-14)
# Platform: aarch64-apple-darwin20
# Running under: macOS 15.0.1
# 
# Matrix products: default
# BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
# LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
# 
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# 
# time zone: America/Chicago
# tzcode source: internal
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
#  [1] gridExtra_2.3       ggpubr_0.6.0        fpc_2.2-13         
#  [4] clusterSim_0.51-5   MASS_7.3-61         cluster_2.1.6      
#  [7] Biobase_2.64.0      BiocGenerics_0.50.0 RColorBrewer_1.1-3 
# [10] cowplot_1.1.3       patchwork_1.3.0     lubridate_1.9.3    
# [13] forcats_1.0.0       stringr_1.5.1       dplyr_1.1.4        
# [16] purrr_1.0.2         readr_2.1.5         tidyr_1.3.1        
# [19] tibble_3.2.1        ggplot2_3.5.1       tidyverse_2.0.0    
# [22] Seurat_5.1.0        SeuratObject_5.0.2  sp_2.1-4           
# 
# loaded via a namespace (and not attached):
#   [1] RcppAnnoy_0.0.22       splines_4.4.1          later_1.3.2           
#   [4] polyclip_1.10-7        fastDummies_1.7.4      lifecycle_1.0.4       
#   [7] rstatix_0.7.2          rprojroot_2.0.4        globals_0.16.3        
#  [10] lattice_0.22-6         prabclus_2.3-4         backports_1.5.0       
#  [13] magrittr_2.0.3         plotly_4.10.4          sass_0.4.9            
#  [16] rmarkdown_2.28         jquerylib_0.1.4        yaml_2.3.10           
#  [19] httpuv_1.6.15          sctransform_0.4.1      spam_2.11-0           
#  [22] flexmix_2.3-19         spatstat.sparse_3.1-0  reticulate_1.39.0     
#  [25] pbapply_1.7-2          ade4_1.7-22            abind_1.4-8           
#  [28] Rtsne_0.17             nnet_7.3-19            git2r_0.35.0          
#  [31] ggrepel_0.9.6          irlba_2.3.5.1          listenv_0.9.1         
#  [34] spatstat.utils_3.1-0   goftest_1.2-3          RSpectra_0.16-2       
#  [37] spatstat.random_3.3-2  fitdistrplus_1.2-1     parallelly_1.38.0     
#  [40] leiden_0.4.3.1         codetools_0.2-20       tidyselect_1.2.1      
#  [43] farver_2.1.2           matrixStats_1.4.1      stats4_4.4.1          
#  [46] spatstat.explore_3.3-3 jsonlite_1.8.9         e1071_1.7-16          
#  [49] progressr_0.15.0       Formula_1.2-5          ggridges_0.5.6        
#  [52] survival_3.7-0         systemfonts_1.1.0      tools_4.4.1           
#  [55] ragg_1.3.3             ica_1.0-3              Rcpp_1.0.13           
#  [58] glue_1.8.0             xfun_0.48              withr_3.0.2           
#  [61] fastmap_1.2.0          fansi_1.0.6            digest_0.6.37         
#  [64] timechange_0.3.0       R6_2.5.1               mime_0.12             
#  [67] textshaping_0.4.0      colorspace_2.1-1       scattermore_1.2       
#  [70] tensor_1.5             spatstat.data_3.1-2    diptest_0.77-1        
#  [73] utf8_1.2.4             generics_0.1.3         data.table_1.16.2     
#  [76] robustbase_0.99-4-1    class_7.3-22           httr_1.4.7            
#  [79] htmlwidgets_1.6.4      whisker_0.4.1          uwot_0.2.2            
#  [82] pkgconfig_2.0.3        gtable_0.3.6           modeltools_0.2-23     
#  [85] workflowr_1.7.1        lmtest_0.9-40          htmltools_0.5.8.1     
#  [88] carData_3.0-5          dotCall64_1.2          scales_1.3.0          
#  [91] png_0.1-8              spatstat.univar_3.0-1  knitr_1.48            
#  [94] rstudioapi_0.17.1      tzdb_0.4.0             reshape2_1.4.4        
#  [97] nlme_3.1-166           proxy_0.4-27           cachem_1.1.0          
# [100] zoo_1.8-12             KernSmooth_2.23-24     parallel_4.4.1        
# [103] miniUI_0.1.1.1         pillar_1.9.0           grid_4.4.1            
# [106] vctrs_0.6.5            RANN_2.6.2             promises_1.3.0        
# [109] car_3.1-3              xtable_1.8-4           evaluate_1.0.1        
# [112] cli_3.6.3              compiler_4.4.1         rlang_1.1.4           
# [115] future.apply_1.11.3    ggsignif_0.6.4         labeling_0.4.3        
# [118] mclust_6.1.1           plyr_1.8.9             fs_1.6.5              
# [121] stringi_1.8.4          viridisLite_0.4.2      deldir_2.0-4          
# [124] munsell_0.5.1          lazyeval_0.2.2         spatstat.geom_3.3-3   
# [127] Matrix_1.7-1           RcppHNSW_0.6.0         hms_1.1.3             
# [130] future_1.34.0          shiny_1.9.1            highr_0.11            
# [133] kernlab_0.9-33         ROCR_1.0-11            igraph_2.1.1          
# [136] broom_1.0.7            bslib_0.8.0            DEoptimR_1.1-3