Visualize and interpret topics in PBMC data
Peter Carbonetto
Last updated: 2020-08-24
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Knit directory: single-cell-topics/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 5b2fb5c | Peter Carbonetto | 2020-08-24 | workflowr::wflow_publish(“plots_pbmc.Rmd”) |
| html | b6489db | Peter Carbonetto | 2020-08-23 | Re-built plots_pbmc with new PCA plots from 68k fit. |
| Rmd | 89d98ed | Peter Carbonetto | 2020-08-23 | Removed basic_pca_plot in plots.R; working on PCA plots for 68k fit. |
| html | 13ee038 | Peter Carbonetto | 2020-08-23 | Revised notes in plots_pbmc.Rmd. |
| Rmd | e766da5 | Peter Carbonetto | 2020-08-23 | workflowr::wflow_publish(“plots_pbmc.Rmd”) |
| Rmd | dbd5882 | Peter Carbonetto | 2020-08-23 | Added some explanatory text to plots_pbmc.Rmd. |
| html | 97d7e86 | Peter Carbonetto | 2020-08-23 | Fixed subclustering of cluster A in purified PBMC data. |
| Rmd | 005b217 | Peter Carbonetto | 2020-08-23 | workflowr::wflow_publish(“plots_pbmc.Rmd”) |
| html | 59777e7 | Peter Carbonetto | 2020-08-22 | Added table comparing Zheng et al (2017) labeling with clusters in |
| Rmd | 9cd4a08 | Peter Carbonetto | 2020-08-22 | workflowr::wflow_publish(“plots_pbmc.Rmd”) |
| html | c87ddf8 | Peter Carbonetto | 2020-08-22 | Added PBMC purified structure plot with the new clustering (A1, A2, B, |
| Rmd | c6dd5f2 | Peter Carbonetto | 2020-08-22 | workflowr::wflow_publish(“plots_pbmc.Rmd”) |
| html | ce421ae | Peter Carbonetto | 2020-08-22 | A few small revisions to plots_pbmc analysis. |
| Rmd | ddc6de4 | Peter Carbonetto | 2020-08-22 | workflowr::wflow_publish(“plots_pbmc.Rmd”) |
| html | a406a2f | Peter Carbonetto | 2020-08-22 | Added back first PCA plot for 68k pbmc data. |
| Rmd | 8daa131 | Peter Carbonetto | 2020-08-22 | workflowr::wflow_publish(“plots_pbmc.Rmd”) |
| html | 7900d17 | Peter Carbonetto | 2020-08-22 | Revamping the process of defining clusters in plots_pbmc using PCA. |
| Rmd | 14dd774 | Peter Carbonetto | 2020-08-22 | workflowr::wflow_publish(“plots_pbmc.Rmd”) |
| Rmd | 944ce0c | Peter Carbonetto | 2020-08-22 | Working on new PCA plots for purified PBMC c3 subclusters. |
| html | b05232d | Peter Carbonetto | 2020-08-22 | Build site. |
| Rmd | d0e9f6e | Peter Carbonetto | 2020-08-22 | workflowr::wflow_publish(“plots_pbmc.Rmd”) |
| html | fbb0697 | Peter Carbonetto | 2020-08-21 | Build site. |
| Rmd | cdca13e | Peter Carbonetto | 2020-08-21 | workflowr::wflow_publish(“plots_pbmc.Rmd”) |
| html | 216027a | Peter Carbonetto | 2020-08-21 | Re-built plots_pbmc webpage with structure plots. |
| Rmd | 98888b2 | Peter Carbonetto | 2020-08-21 | Added structure plots to plots_pbmc.R. |
| html | 9310993 | Peter Carbonetto | 2020-08-21 | Revised the PCA plots in pbmc_plots. |
| Rmd | 3a746ff | Peter Carbonetto | 2020-08-21 | workflowr::wflow_publish(“plots_pbmc.Rmd”) |
| Rmd | 1091dd0 | Peter Carbonetto | 2020-08-21 | Added code for Ternary plots to plots_pbmc. |
| html | 6d3d7e4 | Peter Carbonetto | 2020-08-20 | Added 68k PCA plots to plots_pbmc. |
| 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)
library(Ternary)
source("../code/plots.R")Load the sample annotations. (The count data are no longer needed at this stage of the analysis, so I have removed them.)
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. In the descriptions below, I refer to these Poisson NMF model fits as the “purified” and “68k” fits.
To aid presentation of the results, topics in the 68k fit are reordered to better align with the topics in purified Poisson NMF 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(4,1,5,3,6,2)
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)We begin by exploring structure in the data as inferred by the topic model. We will visualize this structure by plotting principal components (PCs) of the topic proportions. Although PCA is simple, we will see that it quite effective, and avoids the complications of the popular t-SNE and UMAP nonlinear dimensionality reduction methods.
We begin with the mixture of FACS-purified PBMC data.
fit <- poisson2multinom(fit_purified)
pca <- prcomp(fit$L)$xThree large clusters are clearly evident from first two PCs (there is also finer-scale structure which we will examine below). We label these clusters simply as “A”, “B” and “C”.
n <- nrow(pca)
x <- rep("C",n)
pc1 <- pca[,"PC1"]
pc2 <- pca[,"PC2"]
x[pc1 + 0.2 > pc2] <- "A"
x[pc2 > 0.25] <- "B"
x[(pc1 + 0.4)^2 + (pc2 + 0.1)^2 < 0.07] <- "C"
samples_purified$cluster <- x
p1 <- pca_plot_with_labels(fit_purified,c("PC1","PC2"),
samples_purified$cluster) +
labs(fill = "cluster")
print(p1)
Most of the samples are in cluster A:
table(x)
# x
# A B C
# 72614 10439 11602In cluster C, there are two well-defined subclusters, which we label “C1” and “C2”. There are perhaps other subclusters that are less defined, but we here we ignore this more subtle structure, and focus on the most obvious clusters:
rows <- which(samples_purified$cluster == "C")
fit <- select(poisson2multinom(fit_purified),loadings = rows)
pca <- prcomp(fit$L)$x
n <- nrow(pca)
x <- rep("C3",n)
pc1 <- pca[,1]
pc2 <- pca[,2]
x[pc1 < 0 & pc2 < 0.4] <- "C1"
x[pc1 > 0.5 & pc2 < 0.3] <- "C2"
samples_purified[rows,"cluster"] <- x
p5 <- pca_plot_with_labels(fit,c("PC1","PC2"),x) +
labs(fill = "cluster")
print(p5)
| Version | Author | Date |
|---|---|---|
| 7900d17 | Peter Carbonetto | 2020-08-22 |
The two subclusters, C1 and C2, account for most of the samples in cluster C:
table(x)
# x
# C1 C2 C3
# 7822 2990 790Let’s now look more closely at cluster A. There is a large, much less distinct subcluster, which we label as “A1”. Otherwise, there are no other clearly distinct clusters.
rows <- which(samples_purified$cluster == "A")
fit <- select(poisson2multinom(fit_purified),loadings = rows)
pca <- prcomp(fit$L)$x
n <- nrow(fit$L)
x <- rep("A2",n)
pc1 <- pca[,1]
pc2 <- pca[,2]
x[pc1 > 0.58 - pc2 | pc1 > 0.7] <- "A1"
samples_purified[rows,"cluster"] <- x
p6 <- pca_plot_with_labels(fit,c("PC1","PC2"),x) +
labs(fill = "cluster")
print(p6)
In summary, we have subdivided these data into 6 clusters:
samples_purified$cluster <- factor(samples_purified$cluster)
table(samples_purified$cluster)
#
# A1 A2 B C1 C2 C3
# 8271 64343 10439 7822 2990 790The Structure plot provides a visual summary of topic proportions in each of these six clusters:
set.seed(1)
pbmc_purified_topic_colors <- c("gold","forestgreen","dodgerblue",
"gray","greenyellow","magenta")
pbmc_purified_topics <- c(2,5,1,3,4,6)
rows <- sort(c(sample(which(samples_purified$cluster == "A1"),250),
sample(which(samples_purified$cluster == "A2"),1200),
sample(which(samples_purified$cluster == "B"),250),
sample(which(samples_purified$cluster == "C1"),250),
sample(which(samples_purified$cluster == "C2"),250),
sample(which(samples_purified$cluster == "C3"),200)))
p7 <- structure_plot(select(poisson2multinom(fit_purified),loadings = rows),
grouping = samples_purified[rows,"cluster"],
topics = pbmc_purified_topics,
colors = pbmc_purified_topic_colors[pbmc_purified_topics],
gap = 40,num_threads = 4,verbose = FALSE)
print(p7)
Out of the 6 topics, 4 of them (\(k = 2, 3, 4, 5\)) align closely with the clusters (labeled A1, B, C1, C2). Indeed, they also align closely with the FACS-purified data sets:
with(samples_purified,table(celltype,cluster))
# cluster
# celltype A1 A2 B C1 C2 C3
# CD19+ B 0 3 10073 0 1 8
# CD14+ Monocyte 0 30 8 1 2443 130
# CD34+ 4 43 352 7740 545 548
# CD4+ T Helper2 1 11183 0 16 0 13
# CD56+ NK 8243 120 0 17 1 4
# CD8+ Cytotoxic T 21 10135 0 0 0 53
# CD4+/CD45RO+ Memory 0 10201 0 19 0 4
# CD8+/CD45RA+ Naive Cytotoxic 1 11945 3 0 0 4
# CD4+/CD45RA+/CD25- Naive T 1 10440 1 25 0 12
# CD4+/CD25 T Reg 0 10243 2 4 0 14For example, cluster B corresponds almost perfectly to the B-cell data set, and the largest cluster—cluster A2—is comprised of the T-cell data sets. It is also interesting that many of the samples labeled as “CD34+” are not assigned to the CD34+ cluster (C1), which probably reflects the fact that the this population was much less pure (45%) than the others, and so probably contained other cell types.
Cluster C3 is a heterogeneous cluster that we do not investigate further. Cluster A2—see also the PCA plot above—is a clear example where topic modeling is more appropriate than clustering because any additional clustering of the data will be arbitrary.
Next, we turn to the 68k fit.
fit <- poisson2multinom(fit_68k)
pca <- prcomp(fit$L)$xIn this case, we find least three distinct clusters in the projection onto PCs 3 and 4. We label these clusters “A”, “B” and “C”, as above, but this labeling does not imply a connection between the two sets of clusters.
n <- nrow(pca)
x <- rep("A",n)
pc3 <- pca[,"PC3"]
pc4 <- pca[,"PC4"]
x[pc4 > 0.13 | 0.17 - pc3/1.9 < pc4] <- "B"
x[pc4 > 0.75] <- "C"
samples_68k$cluster <- x
p7 <- pca_plot_with_labels(fit_68k,c("PC3","PC4"),x) +
labs(fill = "cluster")
print(p7)
The vast majority of the cells are in cluster A:
table(samples_68k$cluster)
#
# A B C
# 63408 5006 165Looking more closely at cluster B:
rows <- which(samples_68k$cluster == "B")
fit <- select(poisson2multinom(fit_68k),loadings = rows)
pca <- prcomp(fit$L)$x
n <- nrow(pca)
x <- rep("B3",n)
pc1 <- pca[,"PC1"]
x[pc1 < 0.05] <- "B1"
x[pc1 > 0.3] <- "B2"
samples_68k[rows,"cluster"] <- x
p8 <- pca_plot_with_labels(fit,c("PC1","PC2"),x) +
labs(fill = "cluster")
print(p8)
| Version | Author | Date |
|---|---|---|
| b6489db | Peter Carbonetto | 2020-08-23 |
Looking more closely at cluster A:
rows <- which(samples_68k$cluster == "A")
fit <- select(poisson2multinom(fit_68k),loadings = rows)
pca <- prcomp(fit$L)$x
n <- nrow(pca)
x <- rep("A3",n)
pc2 <- pca[,"PC2"]
pc3 <- pca[,"PC3"]
x[2.5*pc3 < pc2 + 0.3] <- "A1"
x[pc3 > pc2 + 0.8] <- "A2"
samples_68k[rows,"cluster"] <- x
p9 <- pca_plot_with_labels(fit,c("PC2","PC3"),x) +
labs(fill = "cluster")
print(p9)
| Version | Author | Date |
|---|---|---|
| b6489db | Peter Carbonetto | 2020-08-23 |
Within cluster A, the vast majority of the samples are assigned to the A1 subcluster:
table(x)
# x
# A1 A2 A3
# 59260 3555 593In summary, we have subdivided these data into 7 clusters:
samples_68k$cluster <- factor(samples_68k$cluster)
table(samples_68k$cluster)
#
# A1 A2 A3 B1 B2 B3 C
# 59260 3555 593 3869 819 318 165TO DO: Add text here.
set.seed(1)
pbmc_68k_topic_colors <- c("magenta","lightskyblue","dodgerblue",
"gray","forestgreen","gold")
pbmc_68k_topics <- c(2,5,1,3,4,6)
rows <- sort(c(sample(which(samples_68k$cluster == "A1"),1200),
sample(which(samples_68k$cluster == "A2"),500),
sample(which(samples_68k$cluster == "A3"),300),
sample(which(samples_68k$cluster == "B1"),500),
sample(which(samples_68k$cluster == "B2"),300),
which(samples_68k$cluster == "B3"),
which(samples_68k$cluster == "C")))
p10 <- structure_plot(select(poisson2multinom(fit_68k),loadings = rows),
grouping = samples_68k[rows,"cluster"],
topics = pbmc_68k_topics,
colors = pbmc_68k_topic_colors[pbmc_68k_topics],
gap = 40,num_threads = 4,verbose = FALSE)
print(p10)
Comparison to Zheng et al (2017) cell-type labelin:
with(samples_68k,table(celltype,cluster))
# cluster
# celltype A1 A2 A3 B1 B2 B3 C
# CD14+ Monocyte 7 0 2 2804 1 46 2
# CD19+ B 1981 3547 342 0 37 1 0
# CD34+ 11 3 49 21 21 9 163
# CD4+ T Helper2 66 0 17 6 8 0 0
# CD4+/CD25 T Reg 6157 0 28 2 0 0 0
# CD4+/CD45RA+/CD25- Naive T 1863 1 4 0 3 2 0
# CD4+/CD45RO+ Memory 3058 0 1 2 0 0 0
# CD56+ NK 8733 0 25 6 1 11 0
# CD8+ Cytotoxic T 20658 1 88 17 0 9 0
# CD8+/CD45RA+ Naive Cytotoxic 16648 0 10 0 5 3 0
# Dendritic 78 3 27 1011 743 237 0In fit_68k, cluster 2 mostly consists of CD14+ monocyte and dendritic cells, whereas cluster 3 is a small population of CD34+ cells.
pbmc_purified_celltype_colors <-
c("dodgerblue", # CD19+ B
"forestgreen", # CD14+ Monocyte
"palegreen", # CD34+
"plum", # CD4+ T Helper2
"gray", # 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 RegLoadings 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)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)
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] Ternary_1.1.4 cowplot_1.0.0 ggplot2_3.3.0 fastTopics_0.3-163
# [5] 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