Last updated: 2022-02-24

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Knit directory: scATACseq-topics/

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    Modified:   scripts/fit_all_models_Cusanovich2018.sh
    Modified:   scripts/postfit_Buenrostro2018_Chen2019pipeline.sh
    Modified:   scripts/postfit_Cusanovich2018.sh
    Modified:   scripts/postfit_gene_analysis.sbatch

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/motif_analysis_Cusanovich2018_v2.Rmd) and HTML (docs/motif_analysis_Cusanovich2018_v2.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 2810d52 kevinlkx 2022-02-24 wflow_publish("analysis/motif_analysis_Cusanovich2018_v2.Rmd")
html 0083668 kevinlkx 2022-02-16 Build site.
Rmd 33840df kevinlkx 2022-02-16 updated with v2 of the DA analysis

Here we perform TF motif analysis for the Cusanovich et al (2018) scATAC-seq result inferred from the multinomial topic model with \(k = 13\).

Load packages and some functions used in this analysis

library(Matrix)
library(fastTopics)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggrepel)
library(cowplot)
library(plotly)
library(htmlwidgets)
library(DT)
library(reshape2)
library(Logolas)
library(grid)
source("code/motif_analysis.R")
source("code/plots.R")

Load data and topic model results

Load the data and the \(k = 13\) Poisson NMF fit results.

data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Cusanovich_2018/processed_data/"
load(file.path(data.dir, "Cusanovich_2018.RData"))
cat(sprintf("%d x %d counts matrix.\n",nrow(counts),ncol(counts)))
rm(counts)
# 81173 x 436206 counts matrix.
fit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
fit <- readRDS(file.path(fit.dir, "/fit-Cusanovich2018-scd-ex-k=13.rds"))$fit
fit <- poisson2multinom(fit)

Visualize by Structure plot grouped by tissues

set.seed(10)
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
                   "#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
                   "gray")
rows <- sample(nrow(fit$L),4000)
samples$tissue <- as.factor(samples$tissue)

p.structure <- structure_plot(select(fit,loadings = rows),
                     grouping = samples[rows, "tissue"], n = Inf,gap = 40,
                     perplexity = 50,colors = colors_topics,
                     num_threads = 4,verbose = FALSE)

print(p.structure)

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Differential accessbility analysis of the ATAC-seq regions for the topics

Load results from differential accessbility analysis for the topics

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2"
cat(sprintf("Load results from %s \n", out.dir))
DA_res <- readRDS(file.path(out.dir, paste0("DAanalysis-Cusanovich2018-k=13/DA_regions_topics_10000iters.rds")))
# Load results from /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2

Volcano plot of the regions

Topic 1 and topic 4 examples

p.volcano.1 <- volcano_plot(DA_res,k = 1, labels = rep("",nrow(DA_res$z)))

p.volcano.4 <- volcano_plot(DA_res,k = 4, labels = rep("",nrow(DA_res$z)))

plot_grid(p.volcano.1, p.volcano.4)

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Motif enrichment analysis using HOMER

Compile Homer results across topics

homer.dir <- paste0(out.dir, "/motifanalysis-Cusanovich2018-k=13-quantile/HOMER")
cat(sprintf("Directory of motif analysis result: %s \n", homer.dir))
homer_res_topics <- readRDS(file.path(homer.dir, "/homer_knownResults.rds"))
selected_regions <- readRDS(file.path(homer.dir, "/selected_regions.rds"))

# Compile Homer results (pvalue and ranking) across topics
motif_res <- compile_homer_motif_res(homer_res_topics)
saveRDS(motif_res, paste0(homer.dir, "/homer_motif_enrichment_results.rds"))
cat("compiled homer motif results are saved in", paste0(homer.dir, "/homer_motif_enrichment_results.rds"))

motif_table <- data.frame(motif = gsub("/.*", "", rownames(motif_res$mlog10P)),
                          round(motif_res$mlog10P,2))
DT::datatable(motif_table, rownames = F, caption = "Motif enrichment (-log10P)")
# Directory of motif analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2/motifanalysis-Cusanovich2018-k=13-quantile/HOMER 
# compiled homer motif results are saved in /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2/motifanalysis-Cusanovich2018-k=13-quantile/HOMER/homer_motif_enrichment_results.rds

Top 10 motifs in each topic

cat("Number of regions selected for each topic: \n")
print(mapply(nrow, selected_regions[1:(length(selected_regions)-1)]))

colnames_homer <- c("motif_name", "consensus", "P", "log10P", "Padj",  "num_target", "percent_target", "num_bg", "percent_bg")

top_motifs <- data.frame(matrix(nrow=10, ncol = length(homer_res_topics)))
colnames(top_motifs) <- names(homer_res_topics)
for (k in 1:length(homer_res_topics)){
  homer_res <- homer_res_topics[[k]]
  colnames(homer_res) <- colnames_homer
  homer_res <- homer_res %>% separate(motif_name, c("motif", "origin", "database"), "/")
  top_motifs[,k] <- head(homer_res$motif, 10)
}

DT::datatable(data.frame(rank = 1:10, top_motifs), rownames = F, caption = "Top 10 motifs enriched in each topic.")
# Number of regions selected for each topic: 
#   k1   k2   k3   k4   k5   k6   k7   k8   k9  k10  k11  k12  k13 
# 4137 4137 4137 4137 4137 4137 4137 4137 4137 4137 4137 4137 4137

Heatmap of motif enrichment across topics

Heatmap of motif enrichment -log10(p-value).

create_motif_enrichment_heatmap(motif_res, enrichment = "-log10(p-value)", 
                                cluster_motifs = TRUE, cluster_topics = TRUE, motif_filter = 10, horizontal = FALSE,
                                enrichment_range = c(0,100), method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)

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# 250 out of 439 motifs included the heatmap

Scatterplots of motif enrichment

Plot motif enrichment (-log10 p-value) and the ranking

# Plot enrichment (-log10 p-value) and ranking of the motifs

plots <- vector("list", ncol(motif_res$mlog10P))
names(plots) <- colnames(motif_res$mlog10P)

for( i in 1:length(plots)){
  plots[[i]] <- create_motif_enrichment_ranking_plot(motif_res, k = i, 
                                                     max.overlaps = 20, subsample = FALSE)
}

do.call(plot_grid,plots)

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Topic 1 example

print(plots[[1]])

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Motif enrichment vs gene score

Load pre-computed gene scores

gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-absZ-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res <- readRDS(file.path(gene.dir, "genescore_result.rds"))
# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2/geneanalysis-Cusanovich2018-k=13-TSS-absZ-l2

Get TF genes

motif_names <- motif_res$motifs$motif
gene_names <- genescore_res$genes$SYMBOL
common_genes <- intersect(toupper(motif_names), toupper(gene_names))
cat(sprintf("%s TF genes mapped between motif names and gene symbol. \n", length(common_genes)))

motif_gene_table <- data.frame(motif = motif_names[match(common_genes, toupper(motif_names))], 
                                      gene = gene_names[match(common_genes, toupper(gene_names))])
# 247 TF genes mapped between motif names and gene symbol.

Compute correlation between motif enrichment z-score and gene score:

Topic 1 example

  • Compute motif enrichment z-scores from the motif enrichment p-values
  • Plot motif enrichment (-log10 p-value) and correlation between motif enrichment z-scores and gene scores
  • Rank motifs by motif enrichment (-log10 p-value) and correlation between motif enrichment z-score and gene scores
motif_gene_mapping <- create_motif_gene_cor_scatterplot(motif_res, genescore_res, motif_gene_table, 
                                                        k = 1, cor.motif = "z-score")

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motif_gene_mapping <- motif_gene_mapping[with(motif_gene_mapping, order(motif_mlog10P*cor_zscore, decreasing = T)),]
rownames(motif_gene_mapping) <- 1:nrow(motif_gene_mapping)

cat("Top 10 motifs by motif enrichment (-log10 p-value) and correlation to gene scores: \n")
print(head(motif_gene_mapping[,c("motif","motif_mlog10P", "gene_score", "cor_zscore")], 10))
# Top 10 motifs by motif enrichment (-log10 p-value) and correlation to gene scores: 
#          motif motif_mlog10P gene_score cor_zscore
# 1    Gata2(Zf)     405.97848  2.6620405  0.6191297
# 2    Gata4(Zf)     402.37384  0.1782368  0.1705583
# 3    Gata1(Zf)     405.76133 13.0433214  0.1630853
# 4    Gata6(Zf)     415.88040  0.3492756  0.1087562
# 5      Sp2(Zf)      45.34034  6.2994971  0.4073351
# 6     KLF3(Zf)      43.77688  6.4292462  0.4064971
# 7     KLF1(Zf)      53.93937 20.9038040  0.3298559
# 8     KLF5(Zf)      51.33361  0.1760143  0.3047540
# 9     KLF6(Zf)      43.41642  0.2608836  0.3209959
# 10 Bach1(bZIP)      24.38998  9.9906945  0.4985966

GATA family

motif_names <- motif_res$motifs$motif
gene_names <- genescore_res$genes$SYMBOL
TF_motifs <- sort(unique(grep("^GATA\\d*$", motif_names, ignore.case=T, value=T)))
TF_genes <- sort(unique(grep("^GATA\\d*$", gene_names, ignore.case=T, value=T)))
common_genes <- intersect(toupper(TF_motifs), toupper(TF_genes))

motif_gene_table <- data.frame(motif = TF_motifs[match(common_genes, toupper(TF_motifs))], 
                                      gene = TF_genes[match(common_genes, toupper(TF_genes))])
print(motif_gene_table)
#   motif  gene
# 1 Gata1 Gata1
# 2 Gata2 Gata2
# 3 GATA3 Gata3
# 4 Gata4 Gata4
# 5 Gata6 Gata6

Plot GATA motifs in topic 1

# Plot GATA motifs in topic 1
k = 1
motif_order <- order(motif_res$mlog10P[,k], decreasing = T)
motifs <- rownames(motif_res$motifs[motif_order,])
motif_names <- motif_res$motifs[motif_order, "motif"]
selected_motifs <- unique(motifs[match(toupper(motif_gene_table$motif), toupper(motif_names))])
motif.dir <- paste0(homer.dir, "/homer_result_topic_", k, "/knownResults/")

for (i in 1:length(selected_motifs)){
  plot_motif_logo(homer_res_topics, selected_motifs[i], k, motif.dir, type = "both")
}
  • Plot motif enrichment (-log10 p-value) and gene scores
plots <- create_motif_gene_scatterplot(motif_res, genescore_res, 
                                       motif_gene_table,
                                       k = 1, 
                                       y = "-log10(p-value)", 
                                       colors = colors_topics,
                                       max.overlaps = 10)

do.call(plot_grid,plots)

Version Author Date
0083668 kevinlkx 2022-02-16
  • Plot motif enrichment (zscore) and gene scores
plots <- create_motif_gene_scatterplot(motif_res, genescore_res, 
                                       motif_gene_table,
                                       k = 1, 
                                       y = "z-score", 
                                       colors = colors_topics,
                                       max.overlaps = 10)

do.call(plot_grid,plots)

Version Author Date
0083668 kevinlkx 2022-02-16

sessionInfo()
# R version 4.0.4 (2021-02-15)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Scientific Linux 7.4 (Nitrogen)
# 
# Matrix products: default
# BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
# 
# locale:
#  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
# [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
# 
# attached base packages:
# [1] grid      stats     graphics  grDevices utils     datasets  methods  
# [8] base     
# 
# other attached packages:
#  [1] Logolas_1.3.1     reshape2_1.4.4    DT_0.20           htmlwidgets_1.5.4
#  [5] plotly_4.10.0     cowplot_1.1.1     ggrepel_0.9.1     ggplot2_3.3.5    
#  [9] tidyr_1.1.4       dplyr_1.0.8       fastTopics_0.6-97 Matrix_1.4-0     
# [13] workflowr_1.7.0  
# 
# loaded via a namespace (and not attached):
#   [1] Rtsne_0.15           colorspace_2.0-3     seqinr_4.2-8        
#   [4] ellipsis_0.3.2       class_7.3-20         rprojroot_2.0.2     
#   [7] fs_1.5.2             rstudioapi_0.13      farver_2.1.0        
#  [10] listenv_0.8.0        MatrixModels_0.5-0   bit64_4.0.5         
#  [13] prodlim_2019.11.13   fansi_1.0.2          lubridate_1.8.0     
#  [16] codetools_0.2-18     splines_4.0.4        knitr_1.37          
#  [19] ade4_1.7-18          jsonlite_1.7.3       pROC_1.18.0         
#  [22] mcmc_0.9-7           caret_6.0-90         gridBase_0.4-7      
#  [25] Rmpfr_0.8-7          ashr_2.2-47          uwot_0.1.11         
#  [28] compiler_4.0.4       httr_1.4.2           assertthat_0.2.1    
#  [31] fastmap_1.1.0        lazyeval_0.2.2       cli_3.2.0           
#  [34] later_1.3.0          prettyunits_1.1.1    htmltools_0.5.2     
#  [37] quantreg_5.86        tools_4.0.4          gmp_0.6-2.1         
#  [40] coda_0.19-4          gtable_0.3.0         glue_1.6.2          
#  [43] Rcpp_1.0.8           jquerylib_0.1.4      vctrs_0.3.8         
#  [46] ape_5.6-1            nlme_3.1-155         conquer_1.2.1       
#  [49] crosstalk_1.2.0      iterators_1.0.13     timeDate_3043.102   
#  [52] CVXR_1.0-10          gower_0.2.2          xfun_0.29           
#  [55] stringr_1.4.0        globals_0.14.0       ps_1.6.0            
#  [58] lifecycle_1.0.1      irlba_2.3.5          future_1.23.0       
#  [61] getPass_0.2-2        MASS_7.3-55          scales_1.1.1        
#  [64] ipred_0.9-12         hms_1.1.1            promises_1.2.0.1    
#  [67] parallel_4.0.4       SparseM_1.81         yaml_2.2.2          
#  [70] pbapply_1.5-0        sass_0.4.0           rpart_4.1-15        
#  [73] stringi_1.7.6        SQUAREM_2021.1       highr_0.9           
#  [76] foreach_1.5.1        lava_1.6.10          truncnorm_1.0-8     
#  [79] rlang_1.0.1          pkgconfig_2.0.3      matrixStats_0.61.0  
#  [82] evaluate_0.14        lattice_0.20-45      invgamma_1.1        
#  [85] purrr_0.3.4          labeling_0.4.2       recipes_0.1.17      
#  [88] bit_4.0.4            processx_3.5.2       tidyselect_1.1.2    
#  [91] parallelly_1.30.0    plyr_1.8.6           magrittr_2.0.2      
#  [94] R6_2.5.1             generics_0.1.2       DBI_1.1.2           
#  [97] pillar_1.7.0         whisker_0.4          withr_2.4.3         
# [100] survival_3.2-13      mixsqp_0.3-43        nnet_7.3-17         
# [103] tibble_3.1.6         future.apply_1.8.1   crayon_1.5.0        
# [106] utf8_1.2.2           rmarkdown_2.11       progress_1.2.2      
# [109] data.table_1.14.2    callr_3.7.0          git2r_0.29.0        
# [112] ModelMetrics_1.2.2.2 digest_0.6.29        httpuv_1.6.5        
# [115] MCMCpack_1.6-0       RcppParallel_5.1.5   stats4_4.0.4        
# [118] munsell_0.5.0        viridisLite_0.4.0    bslib_0.3.1         
# [121] quadprog_1.5-8