Last updated: 2022-08-29

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Knit directory: scATACseq-topics/

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The aim of this short analysis is to get a better understanding of the Cicero co-accessibility data, and how (and whether) these data can be used to connect chromatin accessibility peaks to genes in order to identify “driving genes” for topics estimated from single-cell ATAC-seq data. As an illustration, here we focus on the Cicero data for a single gene, Slc12a1, that was highlighted in Cusanovich et al, 2018 in connection with the “loop of henle” cell type (see Fig. 5 of that paper, and see also Park et al, 2018).

Load the packages used in the analysis below.

library(fastTopics)
library(ggplot2)
library(cowplot)
library(ashr)

Load the base-pair positions of the genes for the mm9 Mouse Genome Assembly.

load("data/mm9_seq_gene.RData")

Load the Cicero co-accessibility data, including the “gene activity scores”, for gene Slc12a1. (These data were downloaded from the Mouse sci-ATAC-seq Atlas website then prepared using the extract_slc12a1_data.R script.)

load("data/Cusanovich_2018/processed_data/slc12a1_data.RData")
cicero <- transform(cicero,
                    Peak1 = as.character(Peak1),
                    Peak2 = as.character(Peak2))

Load the \(K = 10\) topic model fit, and the results of the DE analysis using this topic model (without the adaptive shrinkage step).

fit <- readRDS(file.path("output/Cusanovich_2018/tissues",
                         "fit-Cusanovich2018-Kidney-scd-ex-k=10.rds"))$fit
fit <- poisson2multinom(fit)
load(file.path("output/Cusanovich_2018/tissues",
           "de-cusanovich2018-kidney-k=10-noshrink.RData"))

From the Structure plots here, topic 8 appears to capture Loop of Henle (LoH) cells, so in the remainder we focus on topic 8.

k <- 8

Get the base-pair positions of the peaks.

feature_names <- rownames(de$postmean)
out           <- strsplit(feature_names,"_")
positions     <- data.frame(chr   = sapply(out,"[[",1),
                            start = sapply(out,"[[",2),
                            end   = sapply(out,"[[",3),
                            name  = feature_names,
                            stringsAsFactors = FALSE)
positions <- transform(positions,
                       start = as.numeric(start),
                       end   = as.numeric(end))

Before examining the co-accessibility data in detail, this first plot confirms that Slc12a1 is highly relevant to topic 8, the LoH topic. It is a simple scatterplot showing the Slc12a1 gene activity score and topic proportion for each cell. (Note that the gene activity scores are shown on the log-scale.)

pdat <- data.frame(loading = fit$L[,k],score = log10(1 + scores))
b <- coef(lm(score ~ loading,data = pdat))
ggplot(pdat,aes(x = loading,y = score)) +
  geom_point() +
  geom_abline(intercept = b["(Intercept)"],slope = b["loading"],
              color = "dodgerblue",size = 1,linetype = "dashed") +
  labs(x = "topic proportion",y = "gene activity score") +
  theme_cowplot()


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] ashr_2.2-54        cowplot_1.1.1      ggplot2_3.3.6      fastTopics_0.6-131
# [5] workflowr_1.7.0   
# 
# loaded via a namespace (and not attached):
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# [65] crayon_1.4.1       lattice_0.20-38    hms_1.1.0          knitr_1.37        
# [69] ps_1.6.0           pillar_1.6.2       glue_1.4.2         evaluate_0.14     
# [73] getPass_0.2-2      data.table_1.12.8  RcppParallel_5.1.5 vctrs_0.3.8       
# [77] httpuv_1.5.2       MatrixModels_0.4-1 gtable_0.3.0       purrr_0.3.4       
# [81] tidyr_1.1.3        assertthat_0.2.1   xfun_0.29          coda_0.19-3       
# [85] later_1.0.0        viridisLite_0.3.0  truncnorm_1.0-8    tibble_3.1.3      
# [89] ellipsis_0.3.2