Identify clusters in mixture of FACS-purified PBMC data using topic model
Peter Carbonetto
Last updated: 2021-01-03
Checks: 7 0
Knit directory: single-cell-topics/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 113614e | Peter Carbonetto | 2021-01-03 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | edaec3f | Peter Carbonetto | 2021-01-03 | Build site. |
| Rmd | 6e77a95 | Peter Carbonetto | 2021-01-03 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| Rmd | 517d00f | Peter Carbonetto | 2021-01-03 | Re-organized the R Markdown a bit. |
| Rmd | 2d71c87 | Peter Carbonetto | 2020-12-30 | Did some re-organizing. |
| html | 9ecb991 | Peter Carbonetto | 2020-12-29 | Build site. |
| html | 5712896 | Peter Carbonetto | 2020-12-29 | Build site. |
| Rmd | 809d0a1 | Peter Carbonetto | 2020-12-29 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”, verbose = TRUE) |
| html | 761d2c0 | Peter Carbonetto | 2020-11-29 | Fixed loadings plot in clusters_purified_pbmc analysis. |
| Rmd | 674817c | Peter Carbonetto | 2020-11-29 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | aa73ee1 | Peter Carbonetto | 2020-11-29 | Working on adding loadings plot to clusters_purified_pbmc analysis. |
| Rmd | ed4dd7b | Peter Carbonetto | 2020-11-29 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | c8fb5be | Peter Carbonetto | 2020-11-29 | Improved PCA expression plots in clusters_purified_pbmc analysis. |
| Rmd | 95f7750 | Peter Carbonetto | 2020-11-29 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | a42db50 | Peter Carbonetto | 2020-11-28 | Added PCA expression plots to clusters_purified_pbmc analysis. |
| Rmd | 2de8504 | Peter Carbonetto | 2020-11-28 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | 07f6bca | Peter Carbonetto | 2020-11-28 | Made a few minor improvements to the PCA vs. t-SNE + UMAP demo. |
| Rmd | 82b549f | Peter Carbonetto | 2020-11-28 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | 3501298 | Peter Carbonetto | 2020-11-28 | Improved Structure plot for purified PBMC data. |
| Rmd | 1f52a7c | Peter Carbonetto | 2020-11-28 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | f7e773e | Peter Carbonetto | 2020-11-28 | Build site. |
| Rmd | 31103b8 | Peter Carbonetto | 2020-11-28 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | 4781407 | Peter Carbonetto | 2020-11-28 | Made some improvements to the PCA plots in clusters_purified_pbmc. |
| Rmd | 80787eb | Peter Carbonetto | 2020-11-28 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| Rmd | 605ee92 | Peter Carbonetto | 2020-11-28 | Added steps to create PCA plots for paper in clusters_pbmc_purified analysis. |
| Rmd | 39015bd | Peter Carbonetto | 2020-11-28 | Cluster A in PBMC data = dendritic cells. |
| html | 48438b3 | Peter Carbonetto | 2020-11-26 | Added a cluster to the purified PBMC clustering. |
| Rmd | 989e7ac | Peter Carbonetto | 2020-11-26 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | c85de93 | Peter Carbonetto | 2020-11-23 | Build site. |
| Rmd | 9389b77 | Peter Carbonetto | 2020-11-23 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | e7411a0 | Peter Carbonetto | 2020-11-23 | Added plots comparing PCA with t-SNE and UMAP in |
| Rmd | 9167310 | Peter Carbonetto | 2020-11-23 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | 8abec44 | Peter Carbonetto | 2020-11-23 | Added PCA plots to clusters_purified_pbmc analysis. |
| Rmd | a7e6fbc | Peter Carbonetto | 2020-11-23 | Various improvements to the analysis of the PBMC data sets. |
| Rmd | 884b869 | Peter Carbonetto | 2020-11-23 | Working on new plots for clusters_purified_pbmc analysis. |
| Rmd | 4a8b2bd | Peter Carbonetto | 2020-11-23 | A couple additions to clusters_purified_pbmc.Rmd. |
| html | 1845721 | Peter Carbonetto | 2020-11-22 | Added Structure plot to clusters_purified_pbmc. |
| Rmd | 90d201b | Peter Carbonetto | 2020-11-22 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | 015e254 | Peter Carbonetto | 2020-11-22 | Fixed up some of the text and plots in clusters_purified_pbmc analysis. |
| Rmd | b512864 | Peter Carbonetto | 2020-11-22 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
| html | 7cbd5e9 | Peter Carbonetto | 2020-11-22 | First build of clusters_purified_pbmc page. |
| Rmd | 4e32884 | Peter Carbonetto | 2020-11-22 | workflowr::wflow_publish(“clusters_purified_pbmc.Rmd”) |
Here we identify clusters of cells from the mixture proportions estimated in the mixture of FACS-purified PBMC data.
Load the packages used in the analysis below, as well as additional functions that we will use to generate some of the plots.
library(Matrix)
library(fastTopics)
library(ggplot2)
library(cowplot)Load the count data.
load("../data/pbmc_purified.RData")
table(samples$celltype)
#
# CD19+ B CD14+ Monocyte
# 10085 2612
# CD34+ CD4+ T Helper2
# 9232 11213
# CD56+ NK CD8+ Cytotoxic T
# 8385 10209
# CD4+/CD45RO+ Memory CD8+/CD45RA+ Naive Cytotoxic
# 10224 11953
# CD4+/CD45RA+/CD25- Naive T CD4+/CD25 T Reg
# 10479 10263Load the \(K = 6\) Poisson NMF model fit.
fit <- readRDS(file.path("../output/pbmc-purified/rds",
"fit-pbmc-purified-scd-ex-k=6.rds"))$fit
fit <- poisson2multinom(fit)From the PCs of the mixture proportions, we define clusters for B cells, CD14+ cells and CD34+ cells. The remaining cells are assigned to the U cluster (“U” for “unknown”).
pca <- prcomp(fit$L)$x
n <- nrow(pca)
x <- rep("U",n)
pc1 <- pca[,1]
pc2 <- pca[,2]
pc3 <- pca[,3]
pc4 <- pca[,4]
pc5 <- pca[,5]
x[pc2 > 0.25] <- "B"
x[pc3 < -0.2 & pc4 < 0.2] <- "CD34+"
x[(pc4 + 0.1)^2 + (pc5 - 0.8)^2 < 0.07] <- "CD14+"Next, we define clusters for NK cells and dendritic cells from the top 2 PCs of the mixture proportions in the U cluster.
rows <- which(x == "U")
n <- length(rows)
fit2 <- select_loadings(fit,loadings = rows)
pca <- prcomp(fit2$L)$x
y <- rep("U",n)
pc1 <- pca[,1]
pc2 <- pca[,2]
pc3 <- pca[,3]
pc4 <- pca[,4]
y[pc1 < -0.3 & 1.1*pc1 < -pc2 - 0.57] <- "NK"
y[pc3 > 0.4 & pc4 < 0.2] <- "dendritic"
x[rows] <- yAmong the remaining cells, we define a cluster for CD8+ cells, noting that this is much less distinct than the other cells. The rest are labeled as T cells.
rows <- which(x == "U")
n <- length(rows)
fit2 <- select_loadings(fit,loadings = rows)
pca <- prcomp(fit2$L)$x
y <- rep("T",n)
pc1 <- pca[,1]
pc2 <- pca[,2]
y[pc1 < 0.25 & pc2 < -0.15] <- "CD8+"
x[rows] <- yIn summary, we have subdivided the cells into 7 subsets:
samples$cluster <- factor(x)
table(samples$cluster)
#
# B CD14+ CD34+ CD8+ dendritic NK T
# 10439 2956 8237 3757 308 8380 60578This plot shows the clustering of the cells projected onto the top two PCs:
cluster_colors <- c("dodgerblue", # B cells
"forestgreen", # CD14+
"darkmagenta", # CD34+
"red", # CD8+
"skyblue", # dendritic
"gray", # NK
"darkorange") # T cells
p1 <- pca_plot(fit,fill = samples$cluster) +
scale_fill_manual(values = cluster_colors) +
labs(fill = "cluster")
p2 <- pca_hexbin_plot(fit)
plot_grid(p1,p2,rel_widths = c(10,11))
This clustering corresponds well to the Zheng et al (2017) FACS cell populations, although there are some differences.
with(samples,table(celltype,cluster))
# cluster
# celltype B CD14+ CD34+ CD8+ dendritic NK T
# CD19+ B 10073 0 0 3 7 0 2
# CD14+ Monocyte 8 2420 0 3 156 0 25
# CD34+ 352 536 8182 20 121 4 17
# CD4+ T Helper2 0 0 8 45 9 1 11150
# CD56+ NK 0 0 17 82 4 8279 3
# CD8+ Cytotoxic T 0 0 0 3146 0 93 6970
# CD4+/CD45RO+ Memory 0 0 20 355 1 0 9848
# CD8+/CD45RA+ Naive Cytotoxic 3 0 0 52 2 2 11894
# CD4+/CD45RA+/CD25- Naive T 1 0 8 27 5 1 10437
# CD4+/CD25 T Reg 2 0 2 24 3 0 10232Compare the FACS subpopulations projected onto the top two PCs with the clustering in the PCA plot above:
facs_colors <- c("dodgerblue", # B cells
"forestgreen", # CD14+
"darkmagenta", # CD34+
"gray", # NK cells
"tomato", # cytotoxic T cells
"gold") # T cells
x <- as.character(samples$celltype)
x[x == "CD4+ T Helper2" | x == "CD4+/CD45RO+ Memory" |
x == "CD8+/CD45RA+ Naive Cytotoxic" | x == "CD4+/CD45RA+/CD25- Naive T" |
x == "CD4+/CD45RA+/CD25- Naive T" | x == "CD4+/CD25 T Reg"] <- "T cell"
x <- factor(x)
p3 <- pca_plot(fit,fill = x) +
scale_fill_manual(values = facs_colors) +
labs(fill = "FACS subpopulation")
print(p3)
The continuous variation in T cells captured by topics 1 and 6 suggests CD4+/CD8+ lineage differentiation in T cells:
x <- samples$celltype
i <- names(sort(tapply(fit$L[,1],x,mean)))
x <- factor(as.character(x),i)
rows <- which(with(samples,!(celltype == "CD19+ B" |
celltype == "CD14+ Monocyte" |
celltype == "CD34+" |
celltype == "CD56+ NK")))
p4 <- loadings_plot(select_loadings(fit,loadings = rows),
x = x[rows],k = 1) +
scale_y_continuous(limits = c(0,1)) +
labs(y = "topic 1 mixture proportion",title = "")
print(p4)
| Version | Author | Date |
|---|---|---|
| edaec3f | Peter Carbonetto | 2021-01-03 |
The Structure plot summarizes the mixture proportions in each of the 7 clusters:
set.seed(1)
topic_colors <- c("gold","forestgreen","dodgerblue","gray",
"darkmagenta","violet")
topics <- c(5,3,2,4,1,6)
x <- samples$cluster
rows <- sort(c(sample(which(x == "B"),1000),
sample(which(x == "CD14+"),300),
sample(which(x == "CD34+"),500),
sample(which(x == "CD8+"),400),
sample(which(x == "NK"),500),
sample(which(x == "T"),1000),
which(samples$cluster == "dendritic")))
x <- x[rows]
x <- factor(x,c("B","CD14+","CD34+","dendritic","NK","CD8+","T"))
p5 <- structure_plot(select_loadings(fit,loadings = rows),
grouping = x,topics = topics,
colors = topic_colors[topics],
perplexity = c(70,30,30,30,30,30,70),n = Inf,gap = 50,
num_threads = 4,verbose = FALSE)
print(p5)
This Structure plot summarizes the correspondence between the topics and the FACS cell populations. It shows the FACS mislabeling of the CD34+ cells.
set.seed(1)
x <- as.character(samples$celltype)
x[x == "CD4+/CD45RA+/CD25- Naive T"] <- "T cell"
x[x == "CD8+ Cytotoxic T"] <- "T cell"
x[x == "CD4+/CD45RO+ Memory"] <- "T cell"
x[x == "CD8+/CD45RA+ Naive Cytotoxic"] <- "T cell"
x[x == "CD4+ T Helper2"] <- "T cell"
x[x == "CD4+/CD25 T Reg"] <- "T cell"
x <- factor(x,c("CD19+ B","CD14+ Monocyte","CD34+","CD56+ NK","T cell"))
rows <- sort(c(sample(which(x == "CD19+ B"),500),
sample(which(x == "CD14+ Monocyte"),250),
sample(which(x == "CD34+"),500),
sample(which(x == "CD56+ NK"),400),
sample(which(x == "T cell"),1000)))
x <- x[rows]
p6 <- structure_plot(select_loadings(fit,loadings = rows),grouping = x,
topics = topics,colors = topic_colors[topics],
perplexity = c(70,30,30,70,70),n = Inf,gap = 30,
num_threads = 4,verbose = FALSE)
print(p6)
| Version | Author | Date |
|---|---|---|
| edaec3f | Peter Carbonetto | 2021-01-03 |
Save the clustering of the PBMC data to an RDS file.
saveRDS(samples,"clustering-pbmc-purified.rds")
sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.7
#
# 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_0.4-11 Matrix_1.2-18
#
# loaded via a namespace (and not attached):
# [1] ggrepel_0.9.0 Rcpp_1.0.5 lattice_0.20-38
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