Topic modeling analysis of Buenrostro et al (2018) data with k = 10 topics
Peter Carbonetto
Last updated: 2022-07-13
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Knit directory: scATACseq-topics/
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| Name | Class | Size |
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| celltypes | factor | 9 Kb |
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| de | topic_model_de_analysis;list | 174 Mb |
| fit | multinom_topic_model_fit;list | 62.2 Mb |
| homer | list | 1.3 Mb |
| p | gg;ggplot | 9.7 Mb |
| p1 | gg;ggplot | 469.9 Kb |
| pdat | data.frame | 9.7 Mb |
| peaks | data.table;data.frame | 57.3 Mb |
| rows | integer | 177.7 Kb |
| rows1 | integer | 138.6 Kb |
| rows2 | integer | 39.1 Kb |
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Here we summarize and interpret the results from the topic modeling analysis of the Buenrostro et al (2018) data, with \(k = 10\) topics.
Load the packages used in the analysis below.
library(Matrix)
library(fastTopics)
library(ggplot2)
library(cowplot)Load the count data (see here for details on these data and how they were prepared).
load("data/Buenrostro_2018/processed_data/Buenrostro_2018_binarized.RData")Next we load the \(k = 10\) multinomial topic model fit to these data, the results of the GoM DE analysis using this topic model, and the results of the HOMER motif enrichment analysis using the p-values from the GoM DE analysis:
fit <- readRDS(
file.path("output/Buenrostro_2018/binarized/filtered_peaks",
"fit-Buenrostro2018-binarized-filtered-scd-ex-k=10.rds"))$fit
fit <- poisson2multinom(fit)
load(file.path("output/Buenrostro_2018/binarized/filtered_peaks",
"de-buenrostro2018-k=10-noshrink.RData"))
homer <- readRDS(file.path("output/Buenrostro_2018/binarized/filtered_peaks",
"homer-buenrostro2018-k=10-noshrink.rds"))Visualize the structure identified in the FACS cell populations using a Structure plot:
set.seed(1)
celltypes <- factor(samples$label,
c("pDC","CLP","LMPP","HSC","MPP","CMP","MEP","GMP",
"mono","UNK"))
topic_colors <- c("gold","forestgreen","orchid","red","lightgray",
"dimgray","orange","dodgerblue","limegreen","mediumblue")
p <- structure_plot(fit,grouping = celltypes,n = Inf,gap = 20,
perplexity = 70,verbose = FALSE,colors = topic_colors,
topics = c(5,10,8,3,4,1,6,7,2,9))
print(p)
Comparing the topic proportions with the cell labels (provided by FACS), we see that the topics capture chromatin accessibility patterns characteristic of early hematopoietic progenitors (HSC, MPP; topics 2 and 9), and the four different hematopoietic cell lineages: CLPs and LMPPs (topic 8); pDCs (topic 3); monocytes and GMPs (topics 1 and 7); and MEPs (topic 4). Additional topics (topics 5, 6 and 10) may be picking up other factors such as patient-specific batch effects or tissue of origin (bone marrow or blood). Additionally, the split of the HSC/MPP cells into two topics seems to also reflect patient-specific batch effects, which also came up in the PCA analysis in the original paper.
In the GoM DE analysis, we attempt to quantify differential accessibility among topics.
de_analysis function attempts to quantify
differential accessibility in each of the peaks:
pdat <- data.frame(pval = 10^(-as.vector(de$lpval)))
p <- ggplot(pdat,aes(x = pval)) +
geom_histogram(bins = 32,color = "white",fill = "black") +
scale_y_continuous(breaks = seq(0,1e5,2e4)) +
labs(x = "p-value",y = "LFCs") +
theme_cowplot(font_size = 10)
print(p)
The individual results are not strong, but an enrichment analysis of the peaks might point to interesting patterns among these differentially accessible peaks.
sessionInfo()
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