Differential expression analysis using a topic model: illustration in mixture of FACS-purified PBMC data

Last updated: 2021-11-29

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Knit directory: single-cell-topics/analysis/

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Rmd	ffdd29d	Peter Carbonetto	2021-11-29	workflowr::wflow_publish(“de_analysis_purified_pbmc.Rmd”, verbose = TRUE)
html	1580f79	Peter Carbonetto	2021-11-24	Fixed volcano plot in de_analysis_purified_pbmc analysis.
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html	29aada6	Peter Carbonetto	2021-11-24	Added B-cells volcano plot to de_analysis_purified_pbmc analysis.
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Rmd	7b34362	Peter Carbonetto	2021-11-24	Working on comparison of deseq2 and fasttopics in B cells in de_analysis_purified_pbmc analysis.
Rmd	6fbc554	Peter Carbonetto	2021-11-24	Working on deseq2 vs fasttopics comparison in de_analysis_purified_pbmc analysis.
Rmd	c1e0931	Peter Carbonetto	2021-11-23	Revised explanatory test in de_analysis_purified_pbmc analysis.
Rmd	fb81385	Peter Carbonetto	2021-11-23	Made a few improvements to the volcano plot in de_analysis_purified_pbmc analysis.
Rmd	a11826f	Peter Carbonetto	2021-11-23	Added scatterplot to de_analysis_purified_pbmc analysis.
Rmd	161d0e9	Peter Carbonetto	2021-11-23	Made a few improvements the b-cells scatterplot in the de_analysis_purified_pbmc analysis.
Rmd	760f6ad	Peter Carbonetto	2021-11-23	Added a z-score q-q plot to de_analysis_purified_pbmc analysis.
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html	0d0c720	Peter Carbonetto	2021-11-21	Added scatterplots assessing accuracy of Monte Carlo estimates to
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The aim of this analysis is to understand by way of illustration the differences between a “classical” differential expresion analysis comparing expression among cell types (here we implement this “classical” DE analysis using DESEq2), and a differential expression analysis using the topic model, which allows for grades of membership to cell types (or more generally cellular expression factors).

Begin by loading the packages and some function definitions used in the analysis.

library(Matrix)
library(DESeq2)
library(fastTopics)
library(ggplot2)
library(ggrepel)
library(cowplot)
source("../code/de_analysis_functions.R")

Load count data and topic model fit

Load the UMI count data for 94,655 cells and 21,952 genes.

load("../data/pbmc_purified.RData")
dim(counts)
# [1] 94655 21952

Load the $K = 6$ Poisson NMF model fit to these data, and convert the Poisson NMF model fit to a multinomial topic model fit.

fit <- readRDS(file.path("../output/pbmc-purified/rds",
                         "fit-pbmc-purified-scd-ex-k=6.rds"))$fit
fit <- poisson2multinom(fit)

The cells are subdivided, based on FACS sorting, into 10 “cell types”. Several of the cell types are virtually indistinguishable based on their gene expression profiles alone, so we combine these indistinguishable cell types into a single “T cell” cell type. This results in 5 predefined cell types, the majority of which are T cells:

set.seed(1)
celltype <- as.character(samples$celltype)
celltype[celltype == "CD4+ T Helper2" |
         celltype == "CD4+/CD45RO+ Memory" |
         celltype == "CD8+/CD45RA+ Naive Cytotoxic" |
         celltype == "CD4+/CD45RA+/CD25- Naive T" |
         celltype == "CD8+ Cytotoxic T" |
         celltype == "CD4+/CD25 T Reg"] <- "T cell"
celltype <- factor(celltype,
                   c("CD19+ B","CD14+ Monocyte","CD34+","CD56+ NK","T cell"))
table(celltype)
# celltype
#        CD19+ B CD14+ Monocyte          CD34+       CD56+ NK         T cell 
#          10085           2612           9232           8385          64341

Structure plot

Next we visualize the structure inferred by the $K = $ topic model using a “structure plot”. The cells in this plot are arranged horizontally according to their predefined cell type to relate the topics to these predefinend cell types:

topic_colors <- c("gold","forestgreen","dodgerblue","gray",
                  "darkmagenta","violet")
topics <- c(5,3,2,4,1,6)
rows <- sort(c(sample(which(celltype == "CD19+ B"),500),
               sample(which(celltype == "CD14+ Monocyte"),250),
               sample(which(celltype == "CD34+"),500),
               sample(which(celltype == "CD56+ NK"),400),
               sample(which(celltype == "T cell"),1000)))
p1 <- structure_plot(select_loadings(fit,loadings = rows),
                     grouping = celltype[rows],topics = topics,
                     colors = topic_colors,perplexity = 70,n = Inf,gap = 30,
                     num_threads = 4,verbose = FALSE)
# Running tsne on 500 x 6 matrix.
# Running tsne on 250 x 6 matrix.
# Running tsne on 500 x 6 matrix.
# Running tsne on 400 x 6 matrix.
# Running tsne on 1000 x 6 matrix.
print(p1)

Version	Author	Date
1917832	Peter Carbonetto	2021-11-21

Some of the topics correspond very closely to the predefined cell types. In particular, topics 1 through 4 closely correspond, respectively, to T cells, CD14+ monocytes (myeloid cells), B cells and natural killer (NK) cells.

Topic 5 (violet) closely corresponds to the “CD34+” FACS cell type, but from the structure plot we observe many cells labeled as “CD34+” with little to no contribution from topic 5, which suggests mislabeling of the CD34+ cells.

Topic 6 (magenta) does not correspond to any FACS cell type and as we will see it captures a different characteristic of the cells—specifically, abundance of ribosomal protein genes. Therefore, the DE results for topics 1–4 are most comparable to a classical DE analysis, and we begin with these comparisons. But before doing this we first assess accuracy of the MCMC computations used in the topic-model-based DE analysis.

Assessing accuracy of the Monte Carlo estimates

The topic-model-based DE analysis was performed previously by simulating the posterior distribution of the LFC statistics via MCMC. Here we assess accuracy of the MCMC calculations. We performed the DE analysis twice (using different seeds, each with 100,000 Monte Carlo samples), so we can compare the posterior mean estimates and z-scores returned by the two de_analysis runs.

load("../output/pbmc-purified/de-pbmc-purified-seed=1.RData")
de1 <- de
load("../output/pbmc-purified/de-pbmc-purified-seed=2.RData")
de2 <- de
rm(de)

The MCMC estimates of the posterior mean log-fold change (LFC) estimates are largely consistent:

pdat <- data.frame(postmean1 = as.vector(de1$postmean),
                   postmean2 = as.vector(de2$postmean))
ggplot(pdat,aes(x = postmean1,y = postmean2)) +
  geom_point(shape = 21,color = "white",fill = "black",size = 2) +
  geom_abline(intercept = 0,slope = 1,color = "magenta",linetype = "dashed") +
  labs(x = "first posterior mean",y = "second posterior mean") +
  theme_cowplot()

Version	Author	Date
29aada6	Peter Carbonetto	2021-11-24
5fa98f8	Peter Carbonetto	2021-11-23
0d0c720	Peter Carbonetto	2021-11-21

The $z$-scores on the other hand are estimated less consistently, presumably because accurately estimating uncertainty is harder. Still, the $z$-scores are still are consistent enough in that it is rare for an LFC to have an lfsr less than 0.05 in one MCMC simulation and not the other (these are the red points in the scatterplot). Note for better visualization $z$-scores larger than 100 (or smaller than -100) are shown as 100 (or -100) in this plot.

pdat <- data.frame(z1 = clamp(as.vector(de1$z),-100,+100),
                   z2 = clamp(as.vector(de2$z),-100,+100),
                   lfsr = factor((de1$lfsr < 0.05) + (de2$lfsr < 0.05)))
ggplot(pdat,aes(x = z1,y = z2,fill = lfsr)) +
  geom_point(shape = 21,color = "white",size = 2) +
  geom_abline(intercept = 0,slope = 1,color = "magenta",linetype = "dotted") +
  scale_fill_manual(values = c("darkblue","tomato","dodgerblue"),
                    na.value = "white") +
  labs(x = "first z-score",y = "second z-score",fill = "lfsr < 0.05") +
  theme_cowplot()

Version	Author	Date
29aada6	Peter Carbonetto	2021-11-24
5fa98f8	Peter Carbonetto	2021-11-23
0d0c720	Peter Carbonetto	2021-11-21

Moving forward, when the two z-scores disagree, we use the one that is nearer to zero.

de             <- de1[c("f0","postmean","z","lfsr")]
class(de)      <- c("topic_model_de_analysis","list")
i              <- which(abs(de2$z) < abs(de1$z))
de$postmean[i] <- de2$postmean[i]
de$z[i]        <- de2$z[i]
de$lfsr[i]     <- de$lfsr[i]

Load DESeq2 results for all cell types

We load the results of the DESeq2 analyes, and combine them into two data frames: a data frame for the posterior mean LFC estimates, and a data frame for the z-scores.

load("../output/pbmc-purified/deseq2-pbmc-purified.RData")
celltypes <- names(deseq)
n <- length(celltypes)
p <- nrow(genes)
deseq2 <- list(postmean = matrix(0,p,n),
               z        = matrix(0,p,n))
rownames(deseq2$postmean) <- genes$ensembl
rownames(deseq2$z)        <- genes$ensembl
colnames(deseq2$postmean) <- celltypes
colnames(deseq2$z)        <- celltypes
for (i in 1:n) {
  deseq2$postmean[,i] <- deseq[[i]]$log2FoldChange
  deseq2$z[,i]        <- with(deseq[[i]],log2FoldChange/lfcSE)
}
deseq <- deseq2
rm(deseq2)

Since we filtered out a few lowly expressed genes before running DESeq2, we subset the fastTopics results to match up with DESeq2.

rows        <- match(rownames(deseq$z),rownames(de$z))
de$f0       <- de$f0[rows]
de$postmean <- de$postmean[rows,]
de$z        <- de$z[rows,]
de$lfsr     <- de$lfsr[rows,]

DESeq2 vs. fastTopics

Comparing the distributions of all $z$-scores, we see that the DE analysis allowing for grades of membership has many more $z$-scores near zero, yet still has (Because there are a few extremely large and extremely small $z$-scores, for better visualization z-scores larger than 100 in magnitude are shown as 100 or -100.)

pdat <-
  data.frame(deseq     =quantile(deseq$z,seq(0,1,length.out=1000),na.rm=TRUE),
             fasttopics=quantile(de$z,seq(0,1,length.out=1000),na.rm=TRUE))
pdat <- transform(pdat,
                  deseq      = clamp(deseq,-100,+100),
                  fasttopics = clamp(fasttopics,-100,+100))
ggplot(pdat,aes(x = deseq,y = fasttopics)) +
  geom_point() +
  geom_abline(intercept = 0,slope = 1,color = "magenta",linetype = "dotted") +
  labs(x = "DESeq2",y = "fastTopics",title = "z-score quantiles") +
  theme_cowplot()

Version	Author	Date
29aada6	Peter Carbonetto	2021-11-24

Now let’s look closely at the DESeq2 and fastTopics results for B cells, one of the cell types that closely corresponds to a topic (topic 3). Here we focus on genes for which the $z$-score is greater than 2 in at least one of the analyses. Genes are colored according to the LFSR estimated in the fastTopics analysis ().

i <- "CD19+ B"
k <- "k3"
pdat <- data.frame(gene                = genes$symbol,
                   postmean.deseq      = deseq$postmean[,i],
                   postmean.fasttopics = de$postmean[,k],
                   z.deseq             = deseq$z[,i],
                   z.fasttopics        = de$z[,k],
                   lfsr = cut(de$lfsr[,k],c(-1,0.001,0.01,0.05,Inf)),
                   stringsAsFactors = FALSE)
j <- which(pdat$postmean.fasttopics < 8)
pdat[j,"gene"] <- ""
pdat[genes$symbol == "HOPX","gene"] <- "HOPX"
pdat <- subset(pdat,abs(z.deseq) > 2 | abs(z.fasttopics) > 2)
ggplot(pdat,aes(x = postmean.deseq,y = postmean.fasttopics,
                fill = lfsr,label = gene)) +
  geom_point(shape = 21,color = "white") +
  geom_abline(intercept = 0,slope = 1,color = "black",linetype = "dotted") +
  geom_text_repel(color = "darkgray",size = 2.25,fontface = "italic",
                  segment.color = "darkgray",segment.size = 0.25,
                  min.segment.length = 0,max.overlaps = Inf,na.rm = TRUE) +
  scale_fill_manual(values = c("deepskyblue","gold","orange","coral"),
                    na.value = "gainsboro") +
  xlim(-12.3,13.1) +
  ylim(-10,13.1) +
  labs(x = "DESeq2",y = "fastTopics",
       title = "LFC in B cells") +
  theme_cowplot()

Version	Author	Date
1580f79	Peter Carbonetto	2021-11-24
29aada6	Peter Carbonetto	2021-11-24

A few interesting themes emerge from this plot:

Many genes with large LFC in the DESeq2, particularly genes with very negative LFCs (genes with large decreases in expression in B cells) have zero LFC in the fastTopics analysis.
Most of the LFC estimates in the fastTopics DE analysis agree with, but are lower than, the LFC estimates in the DESeq2 analysis. The one exception is the genes with the largest expression increases in B cells: both DESeq2 and fastTopics largely identifies the same genes with the largest increases in expression in B cells, but the LFC is higher for most of these genes in the fastTopics analysis.
I highlighted here the HOPX gene because this is a gene that was identified in the fastTopics analysis, but not DESeq2 analysis, presumably because this gene is more strongly expressed in cells that are not actually labeled as “B cell” by FACS.

The fastTopics DE analysis for the “B cells” topic is summarized in the following volcano plot:

volcano_plot(de,k = "k3",ymax = 150,labels = genes$symbol)

Version	Author	Date
1580f79	Peter Carbonetto	2021-11-24
29aada6	Peter Carbonetto	2021-11-24

These same results can also be explored in an interactive volcano plot (you can view the plot here:

volcano_plotly(de,k = "k3",ymax = 150,labels = genes$symbol,
               file = "de_analysis_purified_pbmc_bcells.html")
# Warning: `arrange_()` was deprecated in dplyr 0.7.0.
# Please use `arrange()` instead.
# See vignette('programming') for more help
# Warning: Ignoring 323 observations