Last updated: 2022-08-31

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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. The dashed blue line shows the “best fit” line between the two quantities. (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()

Version Author Date
ff33f2d Peter Carbonetto 2022-08-29

THaving confirmed the strong relationship between Slc12a1 and topic 8, let’s examine more closely the de_analysis results for the peaks near Slc12a1. Let’s use a window of 400 kb near the transcribed region of Slc12a1 (124.97–125.06 Mb on chromosome 2).

d <- 4e5
seq_gene <- subset(seq_gene,feature_name == "Slc12a1")
rows <- which(with(positions,chr == paste0("chr",seq_gene$chromosome) &
                   start > seq_gene$chr_start - d &
                   end < seq_gene$chr_stop + d))
peaks <- c(cicero$Peak1,cicero$Peak2)

These next two plots show the p-values (top) and the LFC estimates for topic 8 and for the 111 peaks near Slc12a1. The peaks that are identified by Cicero as being relevant to Slc12a1 are highlighted in magenta.

pdat <- data.frame(start      = positions[rows,"start"]/1e5,
                   postmean   = de$postmean[rows,k],
                   z          = de$z[rows,k],
                   lpval      = de$lpval[rows,k],
                   cicero_hit = is.element(positions[rows,"name"],peaks))
p1 <- ggplot(pdat,aes(x = start,y = lpval,color = cicero_hit)) +
  geom_point() +
  scale_color_manual(values = c("royalblue","magenta")) +
  labs(x = "position on chromosome 2 (kb)",
       y = "-log10 p-value") +
  theme_cowplot()
p2 <- ggplot(pdat,aes(x = start,y = postmean,color = cicero_hit)) +
  geom_point() +
  geom_hline(yintercept = 0,color = "black",linetype = "dashed") +
  scale_color_manual(values = c("royalblue","magenta")) +
  labs(x = "position on chromosome 2 (kb)",
       y = "LFC estimate") +
  theme_cowplot()
plot_grid(p1,p2,nrow = 2,ncol = 1)

Version Author Date
69df48d Peter Carbonetto 2022-08-30

It is interesting that most of the Cicero-identified peaks show good evidence for being more accessible in topic 8. But it is also interesting that many other peaks near the gene also show good evidence for being more accessible in topic 8.

To improve the LFC estimates, a simple thing we can do is run ash separately on the subset of peaks that are near Slc12a1. By focussing on this small subset of peaks, ash should recognize that there is a strong signal near the gene, and not shrink the estimates too strongly.

pdat <- transform(pdat,se = postmean/z)
fit <- ash(pdat$postmean,pdat$se,mixcompdist = "normal",method = "shrink")

Indeed, ash preserves the strongest signals, and shrinks the others to zero, or close to zero. It is also interesting that the effects of all the Cicero-identified peaks remain after the adaptive shrinkage step:

pdat$ashmean <- fit$result$PosteriorMean
ggplot(pdat,aes(x = postmean,y = ashmean,color = cicero_hit)) +
  geom_point() +
  geom_abline(intercept = 0,slope = 1,color = "black",linetype = "dashed") +
  scale_color_manual(values = c("royalblue","magenta")) +
  labs(x = "original LFC estimate",y = "ash LFC estimate") +
  theme_cowplot()

Version Author Date
93cb793 Peter Carbonetto 2022-08-30 

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
# 
# loaded via a namespace (and not attached):
#  [1] httr_1.4.2         sass_0.4.0         tidyr_1.1.3        jsonlite_1.7.2    
#  [5] viridisLite_0.3.0  bslib_0.3.1        RcppParallel_5.1.5 assertthat_0.2.1  
#  [9] highr_0.8          mixsqp_0.3-46      yaml_2.2.0         progress_1.2.2    
# [13] ggrepel_0.9.1      pillar_1.6.2       backports_1.1.5    lattice_0.20-38   
# [17] quadprog_1.5-8     quantreg_5.54      glue_1.4.2         digest_0.6.23     
# [21] promises_1.1.0     colorspace_1.4-1   htmltools_0.5.2    httpuv_1.5.2      
# [25] Matrix_1.4-2       pkgconfig_2.0.3    invgamma_1.1       SparseM_1.78      
# [29] purrr_0.3.4        scales_1.1.0       whisker_0.4        later_1.0.0       
# [33] Rtsne_0.15         MatrixModels_0.4-1 git2r_0.29.0       tibble_3.1.3      
# [37] farver_2.0.1       generics_0.0.2     ellipsis_0.3.2     withr_2.5.0       
# [41] pbapply_1.5-1      lazyeval_0.2.2     magrittr_2.0.1     crayon_1.4.1      
# [45] mcmc_0.9-6         evaluate_0.14      fs_1.5.2           fansi_0.4.0       
# [49] MASS_7.3-51.4      truncnorm_1.0-8    prettyunits_1.1.1  tools_3.6.2       
# [53] data.table_1.12.8  hms_1.1.0          lifecycle_1.0.0    stringr_1.4.0     
# [57] MCMCpack_1.4-5     plotly_4.9.2       munsell_0.5.0      irlba_2.3.3       
# [61] compiler_3.6.2     jquerylib_0.1.4    rlang_0.4.11       grid_3.6.2        
# [65] htmlwidgets_1.5.1  labeling_0.3       rmarkdown_2.11     gtable_0.3.0      
# [69] DBI_1.1.0          R6_2.4.1           knitr_1.37         dplyr_1.0.7       
# [73] uwot_0.1.10        fastmap_1.1.0      utf8_1.1.4         workflowr_1.7.0   
# [77] rprojroot_1.3-2    stringi_1.4.3      parallel_3.6.2     SQUAREM_2017.10-1 
# [81] Rcpp_1.0.8         vctrs_0.3.8        tidyselect_1.1.1   xfun_0.29         
# [85] coda_0.19-3