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In this analysis, we aim to generate an improved visualization of
pancreas data from the 2022 benchmarking study by [Luecken et
al.][luecken-2022] using the Seurat package in R. This analysis uses the
pre-processed dataset, pancreas.RData
.
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
# 'SeuratObject' was built with package 'Matrix' 1.7.0 but the current
# version is 1.7.1; it is recomended that you reinstall 'SeuratObject' as
# the ABI for 'Matrix' may have changed
#
# Attaching package: 'SeuratObject'
# The following objects are masked from 'package:base':
#
# intersect, t
library(tidyverse)
# ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
# ✔ dplyr 1.1.4 ✔ readr 2.1.5
# ✔ forcats 1.0.0 ✔ stringr 1.5.1
# ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
# ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
# ✔ purrr 1.0.2
# ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
# ✖ dplyr::filter() masks stats::filter()
# ✖ dplyr::lag() masks stats::lag()
# ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(patchwork)
library(cowplot)
#
# Attaching package: 'cowplot'
#
# The following object is masked from 'package:patchwork':
#
# align_plots
#
# The following object is masked from 'package:lubridate':
#
# stamp
library(RColorBrewer)
library(Biobase)
# Loading required package: BiocGenerics
#
# Attaching package: 'BiocGenerics'
#
# The following objects are masked from 'package:lubridate':
#
# intersect, setdiff, union
#
# The following objects are masked from 'package:dplyr':
#
# combine, intersect, setdiff, union
#
# The following object is masked from 'package:SeuratObject':
#
# intersect
#
# The following objects are masked from 'package:stats':
#
# IQR, mad, sd, var, xtabs
#
# The following objects are masked from 'package:base':
#
# anyDuplicated, aperm, append, as.data.frame, basename, cbind,
# colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
# get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
# match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
# Position, rank, rbind, Reduce, rownames, sapply, setdiff, table,
# tapply, union, unique, unsplit, which.max, which.min
#
# Welcome to Bioconductor
#
# Vignettes contain introductory material; view with
# 'browseVignettes()'. To cite Bioconductor, see
# 'citation("Biobase")', and for packages 'citation("pkgname")'.
library(clusterSim)
# Loading required package: cluster
# Loading required package: MASS
#
# Attaching package: 'MASS'
#
# The following object is masked from 'package:patchwork':
#
# area
#
# The following object is masked from 'package:dplyr':
#
# select
library(fpc)
library(ggpubr)
#
# Attaching package: 'ggpubr'
#
# The following object is masked from 'package:cowplot':
#
# get_legend
library(gridExtra)
#
# Attaching package: 'gridExtra'
#
# The following object is masked from 'package:Biobase':
#
# combine
#
# The following object is masked from 'package:BiocGenerics':
#
# combine
#
# The following object is masked from 'package:dplyr':
#
# combine
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()
}
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")
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)
Version | Author | Date |
---|---|---|
7667043 | Sagnik | 2024-11-04 |
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)
Version | Author | Date |
---|---|---|
7667043 | Sagnik | 2024-11-04 |
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
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.
Version | Author | Date |
---|---|---|
7667043 | Sagnik | 2024-11-04 |
Generate an elbow plot to analyze variance across the top variable features.
create_elbow_plot(object = pancreas, assay = "RNA", k = 5000)
Version | Author | Date |
---|---|---|
7667043 | Sagnik | 2024-11-04 |
Select the top 1,000 variable genes for further analysis.
desired_genes <- head(VariableFeatures(pancreas), 1000)
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)
Version | Author | Date |
---|---|---|
7667043 | Sagnik | 2024-11-04 |
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")
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"
)
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
Version | Author | Date |
---|---|---|
7667043 | Sagnik | 2024-11-04 |
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)
)
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