Last updated: 2020-08-20

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Knit directory: single-cell-topics/analysis/

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Rmd 6ec82ca Peter Carbonetto 2020-08-20 workflowr::wflow_publish(“plots_pbmc.Rmd”)
html 38f07a2 Peter Carbonetto 2020-08-20 A few small revisions to the plots_pbmc analysis.
Rmd fd0316f Peter Carbonetto 2020-08-20 workflowr::wflow_publish(“plots_pbmc.Rmd”)
html 606cd97 Peter Carbonetto 2020-08-20 Added basic PCA plots to plots_pbmc.
Rmd 1b41a60 Peter Carbonetto 2020-08-20 workflowr::wflow_publish(“plots_pbmc.Rmd”)
html d6e5d39 Peter Carbonetto 2020-08-20 Added PCA plot with purified PBMC clustering to plots_pbmc analysis.
Rmd 99301a7 Peter Carbonetto 2020-08-20 workflowr::wflow_publish(“plots_pbmc.Rmd”)
Rmd bf23ca0 Peter Carbonetto 2020-08-20 Added manual labeling of purified PBMC data to plots_pbmc analysis.
html 0c1b570 Peter Carbonetto 2020-08-20 First build on plots_pbmc page.
Rmd eb7f776 Peter Carbonetto 2020-08-20 workflowr::wflow_publish(“plots_pbmc.Rmd”)

TO DO: Add introductory text here.

Load the packages used in the analysis below.

library(dplyr)
library(fastTopics)
library(ggplot2)
library(cowplot)
source("../code/plots.R")

Load the sample annotations. (The count data are no longer needed at this stage of the analysis.)

load("../data/pbmc_purified.RData")
samples_purified <- samples
load("../data/pbmc_68k.RData")
samples_68k <- samples
rm(genes,counts)

Load the \(k = 6\) Poisson NMF model fits for both PBMC data sets. To reduce confusion, topics in the fit_68k Poisson NMF model fit are reordered to better align with the topics in fit_purified Poisson NMF model fit.

fit_purified <-
  readRDS("../output/pbmc-purified/rds/fit-pbmc-purified-scd-ex-k=6.rds")$fit
fit_68k <- readRDS("../output/pbmc-68k/rds/fit-pbmc-68k-scd-ex-k=6.rds")$fit
cols      <- c(1,6,5,3,4,2)
fit_68k$F <- fit_68k$F[,cols]
fit_68k$L <- fit_68k$L[,cols]
colnames(fit_68k$F) <- paste0("k",1:6)
colnames(fit_68k$L) <- paste0("k",1:6)

PCs 1 and 2 in mixture of FACS-purified PBMC data:

p1 <- basic_pca_plot(fit_purified,c("PC1","PC2"))
print(p1)

Version Author Date
606cd97 Peter Carbonetto 2020-08-20
d6e5d39 Peter Carbonetto 2020-08-20

TO DO: Add text here.

pca_purified <- prcomp(poisson2multinom(fit_purified)$L)$x
n   <- nrow(pca_purified)
x   <- rep("c3",n)
pc1 <- pca_purified[,"PC1"]
pc2 <- pca_purified[,"PC2"]
x[pc1 + 0.2 > pc2] <- "c1"
x[pc2 > 0.25] <- "c2"
x[(pc1 + 0.4)^2 + (pc2 + 0.1)^2 < 0.07] <- "c3"
samples_purified$cluster <- factor(x)

PCs 1 and 2 in mixture of FACS-purified PBMC data:

purified_cluster_colors <- c("tomato","dodgerblue","lightskyblue")
p2 <- pca_plot_with_labels(fit_purified,c("PC1","PC2"),
                           samples_purified$cluster,
                           purified_cluster_colors) +
      labs(fill = "cluster")
print(p2)

Version Author Date
38f07a2 Peter Carbonetto 2020-08-20

Most of the samples are in the first (red) cluster:

print(table(samples_purified$cluster))
# 
#    c1    c2    c3 
# 72614 10439 11602

PCs 3 and 4 in 68k PBMC data:

p3 <- basic_pca_plot(fit_68k,c("PC3","PC4"))
print(p3)

TO DO: Add text here.

pca_68k <- prcomp(poisson2multinom(fit_68k)$L)$x
n   <- nrow(pca_68k)
x   <- rep("c1",n)
pc3 <- pca_68k[,"PC3"]
pc4 <- pca_68k[,"PC4"]
x[pc4 < -0.13 | pc3/2 - 0.17 > pc4] <- "c2"
x[pc4 < -0.75] <- "c3"
samples_68k$cluster <- factor(x)

PCs 3 and 4 with the clustering layered on:

pbmc_68k_cluster_colors <- c("cornflowerblue","darkorange","firebrick")
p4 <- pca_plot_with_labels(fit_68k,c("PC3","PC4"),factor(x),
                           pbmc_68k_cluster_colors) +
      labs(fill = "cluster")
print(p4)

The vast majority of the cells are in the first cluster:

table(samples_68k$cluster)
# 
#    c1    c2    c3 
# 63432  4982   165

Comparison to Zheng et al (2017) cell-type labeling of the FACS-purified PBMC data:

purified_celltype_colors <-
  c("dodgerblue",  # CD19+ B
    "forestgreen", # CD14+ Monocyte
    "lightskyblue",# CD34+
    "plum",        # CD4+ T Helper2
    "slategray",   # CD56+ NK
    "tomato",      # CD8+ Cytotoxic T
    "gold",        # CD4+/CD45RO+ Memory
    "magenta",     # CD8+/CD45RA+ Naive Cytotoxic
    "darkorange",  # CD4+/CD45RA+/CD25- Naive T
    "yellowgreen") # CD4+/CD25 T Reg
p4 <- pca_plot_with_labels(fit_purified,c("PC1","PC2"),
                           samples_purified$celltype,
                           purified_celltype_colors) + labs(fill = "celltype")
p5 <- pca_plot_with_labels(fit_purified,c("PC4","PC5"),
                           samples_purified$celltype,
                           purified_celltype_colors) + labs(fill = "celltype")

Loadings plot:

loadings_plot(poisson2multinom(fit_purified),samples_purified$celltype)
loadings_plot(poisson2multinom(fit_68k),samples_68k$celltype)

PCA plot:

clrs <- c("forestgreen",  # CD14+ Monocyte
          "dodgerblue",   # CD19+ B
          "darkmagenta",  # CD34+"
          "yellowgreen",  # CD4+ T Helper2
          "gold",         # CD4+/CD25 T Reg
          "limegreen",    # CD4+/CD45RA+/CD25- Naive T
          "orange",       # CD4+/CD45RO+ Memory"
          "gray",         # CD56+ NK
          "tomato",       # CD8+ Cytotoxic T
          "magenta",      # CD8+/CD45RA+ Naive Cytotoxic"
          "darkblue")     # Dendritic"
fit2 <- poisson2multinom(fit)
pca  <- prcomp(fit2$L)
pdat <- cbind(samples,pca$x)
ggplot(pdat,aes(x = PC3,y = PC4,fill = celltype)) +
  geom_point(shape = 21,color = "white",size = 1.5) +
  scale_fill_manual(values = clrs) +
  theme_cowplot(font_size = 10)

t-SNE plot:

set.seed(1)
p2 <- tsne_plot(fit,n = 8000,num_threads = 4)

Differential count analysis:

diff_count_res <- diff_count_analysis(fit,counts)

Volcano plots:

p3 <- volcano_plot(diff_count_res,labels = genes$symbol,
                   label_above_quantile = 0.995)

Structure plots:

set.seed(1)
fit2 <- select(poisson2multinom(fit),
               loadings = which(samples$celltype == "CD19+ B"))
p4   <- structure_plot(fit2,n = 2000,num_threads = 4) # B-cells.
set.seed(1)
fit2 <- select(poisson2multinom(fit),
               loadings = which(samples$celltype == "CD56+ NK"))
p5   <- structure_plot(fit2,n = 2000,num_threads = 4) # NK cells.
set.seed(1)
fit2 <- select(poisson2multinom(fit),
               loadings = which(samples$celltype == "CD34+"))
p6   <- structure_plot(fit2,num_threads = 4,perplexity = 50) # CD34+ cells
set.seed(1)
fit2 <- select(poisson2multinom(fit),
               loadings = which(samples$celltype == "CD14+ Monocyte"))
p7   <- structure_plot(fit2,num_threads = 4) # CD14+ monocytes
set.seed(1)
fit2 <- select(poisson2multinom(fit),
               loadings = which(samples$celltype == "Dendritic"))
p8   <- structure_plot(fit2,num_threads = 4) # dendritic cells
plot_grid(p7,p8,nrow = 2)
set.seed(1)
fit2 <- select(poisson2multinom(fit),
               loadings = which(samples$celltype == "CD4+ T Helper2"))
p9   <- structure_plot(fit2,num_threads = 4,perplexity = 30) +
          ggtitle("CD4+ T Helper2") +
set.seed(1)
fit2 <- select(poisson2multinom(fit),
          loadings = which(samples$celltype == "CD4+/CD45RA+/CD25- Naive T"))
p10  <- structure_plot(fit2,num_threads = 4) +
          ggtitle("CD4+/CD45RA+/CD25- Naive T")
set.seed(1)
fit2 <- select(poisson2multinom(fit),
               loadings = which(samples$celltype == "CD4+/CD45RO+ Memory"))
p11  <- structure_plot(fit2,num_threads = 4) +
          ggtitle("CD4+/CD45RO+ Memory")
set.seed(1)
fit2 <- select(poisson2multinom(fit),
               loadings = which(samples$celltype == "CD4+/CD25 T Reg"))
p12  <- structure_plot(fit2,num_threads = 4) +
          ggtitle("CD4+/CD25 T Reg")
fit2 <- select(poisson2multinom(fit),
          loadings = which(samples$celltype == "CD8+/CD45RA+ Naive Cytotoxic"))
p13  <- structure_plot(fit2,num_threads = 4) +
          ggtitle("CD8+/CD45RA+ Naive Cytotoxic")
fit2 <- select(poisson2multinom(fit),
          loadings = which(samples$celltype == "CD8+ Cytotoxic T"))
p14  <- structure_plot(fit2,num_threads = 4) +
          ggtitle("CD8+ Cytotoxic T")
plot_grid(p9,p10,p11,p12,p13,p14,nrow = 6)

Another structure plot:

p15 <- structure_plot(fit,num_threads = 4)

sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.5
# 
# Matrix products: default
# BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
# 
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] cowplot_1.0.0      ggplot2_3.3.0      fastTopics_0.3-163 dplyr_0.8.3       
# 
# loaded via a namespace (and not attached):
#  [1] ggrepel_0.9.0        Rcpp_1.0.3           lattice_0.20-38     
#  [4] tidyr_1.0.0          prettyunits_1.1.1    assertthat_0.2.1    
#  [7] zeallot_0.1.0        rprojroot_1.3-2      digest_0.6.23       
# [10] R6_2.4.1             backports_1.1.5      MatrixModels_0.4-1  
# [13] evaluate_0.14        coda_0.19-3          httr_1.4.1          
# [16] pillar_1.4.3         rlang_0.4.5          progress_1.2.2      
# [19] lazyeval_0.2.2       data.table_1.12.8    irlba_2.3.3         
# [22] SparseM_1.78         whisker_0.4          Matrix_1.2-18       
# [25] rmarkdown_2.3        labeling_0.3         Rtsne_0.15          
# [28] stringr_1.4.0        htmlwidgets_1.5.1    munsell_0.5.0       
# [31] compiler_3.6.2       httpuv_1.5.2         xfun_0.11           
# [34] pkgconfig_2.0.3      mcmc_0.9-6           htmltools_0.4.0     
# [37] tidyselect_0.2.5     tibble_2.1.3         workflowr_1.6.2.9000
# [40] quadprog_1.5-8       viridisLite_0.3.0    crayon_1.3.4        
# [43] withr_2.1.2          later_1.0.0          MASS_7.3-51.4       
# [46] grid_3.6.2           jsonlite_1.6         gtable_0.3.0        
# [49] lifecycle_0.1.0      git2r_0.26.1         magrittr_1.5        
# [52] scales_1.1.0         RcppParallel_4.4.4   stringi_1.4.3       
# [55] farver_2.0.1         fs_1.3.1             promises_1.1.0      
# [58] vctrs_0.2.1          tools_3.6.2          glue_1.3.1          
# [61] purrr_0.3.3          hms_0.5.2            yaml_2.2.0          
# [64] colorspace_1.4-1     plotly_4.9.2         knitr_1.26          
# [67] quantreg_5.54        MCMCpack_1.4-5