Enrichment analysis using mapgen
Kaixuan Luo
2022-04-11
Last updated: 2023-10-20
Checks: 7 0
Knit directory: analysis_pipelines/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 79c858f | kevinlkx | 2023-10-20 | wflow_publish("analysis/mapgen_torus_enrichment_heart_atlas.Rmd") |
| html | 0d76d5c | kevinlkx | 2022-04-22 | Build site. |
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| Rmd | 4bcdf12 | kevinlkx | 2022-04-22 | fixed bugs in run_torus() return values and added torus_input_dir for prepare_torus_input_files() |
| html | db1ff60 | kevinlkx | 2022-04-19 | Build site. |
| Rmd | 39e9a66 | kevinlkx | 2022-04-19 | wflow_rename("analysis/torus_enrichment_heart_atlas.Rmd", "analysis/mapgen_torus_enrichment_heart_atlas.Rmd") |
| html | 39e9a66 | kevinlkx | 2022-04-19 | wflow_rename("analysis/torus_enrichment_heart_atlas.Rmd", "analysis/mapgen_torus_enrichment_heart_atlas.Rmd") |
Here we show an example of performing enrichment analysis on AFib
GWAS data using mapgen.
Univariate enrichment analysis
Here we use scATAC-seq DA peaks as annotations (univariate).
library(mapgen)
library(tidyverse)
suppressMessages(library(liftOver))
suppressMessages(library(ComplexHeatmap))data.dir <- '/project2/xinhe/shared_data/mapgen/example_data'Load GWAS summary statistics of AFib
gwas.sumstats <- readRDS(paste0(data.dir, '/GWAS/ebi-a-GCST006414_aFib.df.rds'))
gwas.sumstats <- gwas.sumstats %>% dplyr::rename(ss_index = og_index)
head(gwas.sumstats)Prepare annotations for TORUS
# load DA peaks (in hg38)
markers <- readRDS(paste0(data.dir, '/ATAC_seq/PeakCalls/DA_MARKERS_FDRP_1_log2FC_1.rds'))
# liftover peaks from hg38 to hg19
path <- system.file(package="liftOver", "extdata", "hg38ToHg19.over.chain")
ch <- import.chain(path)
markers.hg19 <- lapply(markers, function(x){unlist(liftOver(x, ch))})
markers <- as.list(markers)
markers.hg19.l <- vector("list", length = length(markers))
for(i in 1:length(markers.hg19.l)){
markers.hg19.l[[i]] <- unlist(liftOver(markers[[i]], ch))
}
system('mkdir -p Torus/bed_annotations_hg19')
# save to bed format
for(i in 1:length(markers.hg19)){
seqlevelsStyle(markers.hg19[[i]]) <- "NCBI"
}
lapply(names(markers.hg19), function(x){
rtracklayer::export(markers.hg19[[x]],
format = 'bed',
con = paste0(data.dir, '/Torus/bed_annotations_hg19/', x,'_narrowPeaks.bed'))})
annotations <- list.files(path = paste0(data.dir, '/Torus/bed_annotations_hg19'), pattern = '*.bed', full.names = T)Run TORUS for each annotation separately
enrich.res <- vector('list', length(annotations))
names(enrich.res) <- basename(annotations)
for(i in seq_along(annotations)){
annot.name <- gsub('_narrowPeaks*', '', tools::file_path_sans_ext(basename(annotations[i])))
# Prepare TORUS input data
torus.files <- prepare_torus_input_files(gwas.sumstats,
annotations[i],
torus_input_dir = paste0(data.dir, '/Torus/input/', annot.name))
# Estimates enrichment using TORUS
torus.result <- run_torus(torus.files$torus_annot_file,
torus.files$torus_zscore_file,
option = "est",
torus_path = "torus") # set the path to your 'torus' executable
enrich.res[[i]] <- torus.result$enrich
}
saveRDS(enrich.res, paste0(data.dir, '/Torus/Torus_univariate_enrichment_result.rds'))Compare to pre-computed result
enrich.alltraits.res <- readRDS(paste0(data.dir,'/Torus/Torus_CellType_Enrichment_Results_Univariate_MORE.df.rds'))
enrich.res <- lapply(enrich.res, function(x) {tibble::as_tibble(x)})
identical(enrich.res, enrich.alltraits.res$aFib)Plot enrichment for all traits
Load enrichment results
enrich.res <- readRDS(paste0(data.dir, '/Torus/Torus_CellType_Enrichment_Results_Univariate_MORE.df.rds'))
annotations <- list.files(path = paste0(data.dir, '/Torus/bed_annotations_hg19'), pattern = '*.bed', full.names = T)
pval_from_ci <- function(mean, upper, ci){
nsamp <- length(mean)
pval.res <- rep(0, nsamp)
for(i in 1:nsamp){
alph <- (1-ci)/2
zval <- qnorm(p = 1-alph)
se <- (upper[i]-mean[i])/zval
pval.res[i] <- 1 - pnorm(q = mean[i] / se)
}
return(pval.res)
}
res <- lapply(enrich.res, function(x){ Reduce(x = x, f = rbind)})
res <- lapply(res, function(x){x[x$term != "Intercept",]})
for(i in 1:length(res)){
res[[i]]$pvalue <- pval_from_ci(mean = res[[i]]$estimate, upper = res[[i]]$high, ci = 0.95)
}
estimates <- as.data.frame(sapply(res, function(x){x["estimate"]}))
pvalues <- as.data.frame(sapply(res, function(x){x["pvalue"]}))
fdr <- matrix(p.adjust(unlist(pvalues), method = 'BH'), nrow = nrow(pvalues))
rnames <- basename(annotations)
names.order <- c("aFib", "PR_Interval","heart_rate","heart_failure",
"CAD","DiastolicBP","asthma","BMI","Height")
celltype_ideal_order <- c("Cardiomyocyte","Smooth Muscle","Pericyte","Endothelial","Fibroblast","Neuronal", "Lymphoid","Myeloid")
# celltype_ideal_order <- c("Cardiomyocyte","Pericyte","Endothelial","Fibroblast")
row.names(estimates) <- sub('_narrowPeaks.bed','',rnames)
colnames(estimates) <- names(enrich.res)
estimates <- estimates[celltype_ideal_order,names.order]
estimates <- t(estimates)
row.names(fdr) <- sub('_narrowPeaks.bed','',rnames)
colnames(fdr) <- names(enrich.res)
fdr <- fdr[celltype_ideal_order,names.order]
fdr <- t(fdr)
star.mat <- matrix('ns', nrow = nrow(fdr), ncol = ncol(fdr))
star.mat[fdr < 0.05] <- '*'
star.mat[fdr < 0.0001] <- '***'
rownames(star.mat) <- rownames(fdr)
colnames(star.mat) <- colnames(fdr)
mat.to.viz <- estimates/log(2)
mat.to.viz[mat.to.viz < 0] <- 0Plot enrichment
lgd_list <- list()
col_fun <- c("lightblue", "orange", "firebrick")
names(col_fun) <- c("ns", '*', '***')
lgd_list[["fdr"]] <- Legend(title = "fdr (binned)",
labels = c("ns", '*', '***'),
legend_gp = gpar(fill = col_fun))
tic_vec <- c(0, 2, 4)
lgd_list[["log2_enrich"]] <- Legend(title = "log2_enrich",
labels = tic_vec,
# labels_gp = gpar(fontsize = 14),
grid_height = unit(6, "mm"),
grid_width = unit(6, "mm"),
graphics = list(
function(x, y, w, h)
grid.circle(x, y,
r = (tic_vec[1]/10 + 0.2) * unit(2.5, "mm"),
gp = gpar(fill = "black")),
function(x, y, w, h)
grid.circle(x, y,
r = (tic_vec[2]/10 + 0.2) * unit(2.5, "mm"),
gp = gpar(fill = "black")),
function(x, y, w, h)
grid.circle(x, y,
r = (tic_vec[3]/10 + 0.2) * unit(2.5, "mm"),
gp = gpar(fill = "black"))
))
map1 <- Heatmap(star.mat,
name = "Association Effect Size",
col = col_fun,
rect_gp = gpar(type = "none"),
cell_fun = function(j, i, x, y, width, height, fill) {
grid.rect(x = x, y = y, width = width, height = height,
gp = gpar(col = NA, fill = NA))
grid.circle(x = x, y = y,
r = (mat.to.viz[i, j]/10 + 0.2) * unit(2.5, "mm"),
gp = gpar(fill = col_fun[star.mat[i, j]], col = NA))
},
border_gp = gpar(col = "black"),
row_title = "Trait",
column_title = "Cell Type",
cluster_rows = F, cluster_columns = F,
show_heatmap_legend = F,
row_names_gp = gpar(fontsize = 10.5),
column_names_rot = 45,
column_names_side = "top",
use_raster = T)'magick' package is suggested to install to give better rasterization.
Set `ht_opt$message = FALSE` to turn off this message.draw(map1, annotation_legend_list = lgd_list)
| Version | Author | Date |
|---|---|---|
| 39e9a66 | kevinlkx | 2022-04-19 |
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
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 LC_TIME=C
[4] LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=C
[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ComplexHeatmap_2.12.0
[2] liftOver_1.20.0
[3] Homo.sapiens_1.3.1
[4] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[5] org.Hs.eg.db_3.15.0
[6] GO.db_3.15.0
[7] OrganismDbi_1.38.1
[8] GenomicFeatures_1.50.4
[9] AnnotationDbi_1.60.0
[10] Biobase_2.58.0
[11] rtracklayer_1.58.0
[12] GenomicRanges_1.48.0
[13] GenomeInfoDb_1.34.9
[14] IRanges_2.32.0
[15] S4Vectors_0.36.1
[16] BiocGenerics_0.44.0
[17] gwascat_2.28.1
[18] forcats_1.0.0
[19] stringr_1.5.0
[20] dplyr_1.1.0
[21] purrr_1.0.1
[22] readr_2.1.4
[23] tidyr_1.3.0
[24] tibble_3.1.8
[25] ggplot2_3.4.1
[26] tidyverse_1.3.2
[27] mapgen_0.5.6
[28] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] circlize_0.4.15 readxl_1.4.2
[3] backports_1.4.1 BiocFileCache_2.6.0
[5] splines_4.2.0 BiocParallel_1.32.5
[7] digest_0.6.31 foreach_1.5.2
[9] htmltools_0.5.4 fansi_1.0.4
[11] magrittr_2.0.3 memoise_2.0.1
[13] BSgenome_1.66.2 cluster_2.1.3
[15] doParallel_1.0.17 googlesheets4_1.0.1
[17] tzdb_0.3.0 Biostrings_2.66.0
[19] modelr_0.1.10 matrixStats_0.63.0
[21] timechange_0.2.0 prettyunits_1.1.1
[23] colorspace_2.1-0 blob_1.2.3
[25] rvest_1.0.3 rappdirs_0.3.3
[27] haven_2.5.1 xfun_0.37
[29] callr_3.7.3 crayon_1.5.2
[31] RCurl_1.98-1.10 jsonlite_1.8.4
[33] graph_1.74.0 iterators_1.0.14
[35] survival_3.3-1 VariantAnnotation_1.44.1
[37] glue_1.6.2 gtable_0.3.1
[39] gargle_1.3.0 zlibbioc_1.44.0
[41] XVector_0.38.0 GetoptLong_1.0.5
[43] DelayedArray_0.24.0 shape_1.4.6
[45] scales_1.2.1 DBI_1.1.3
[47] Rcpp_1.0.10 progress_1.2.2
[49] clue_0.3-61 bit_4.0.5
[51] httr_1.4.4 RColorBrewer_1.1-3
[53] ellipsis_0.3.2 pkgconfig_2.0.3
[55] XML_3.99-0.13 sass_0.4.5
[57] dbplyr_2.3.0 utf8_1.2.3
[59] tidyselect_1.2.0 rlang_1.0.6
[61] later_1.3.0 munsell_0.5.0
[63] cellranger_1.1.0 tools_4.2.0
[65] cachem_1.0.6 cli_3.6.0
[67] generics_0.1.3 RSQLite_2.2.20
[69] broom_1.0.3 evaluate_0.20
[71] fastmap_1.1.0 yaml_2.3.7
[73] processx_3.8.0 knitr_1.42
[75] bit64_4.0.5 fs_1.6.1
[77] KEGGREST_1.38.0 RBGL_1.72.0
[79] whisker_0.4 xml2_1.3.3
[81] biomaRt_2.54.0 compiler_4.2.0
[83] rstudioapi_0.14 filelock_1.0.2
[85] curl_5.0.0 png_0.1-8
[87] reprex_2.0.2 bslib_0.4.2
[89] stringi_1.7.12 highr_0.10
[91] ps_1.7.2 lattice_0.20-45
[93] Matrix_1.5-3 vctrs_0.5.2
[95] pillar_1.8.1 lifecycle_1.0.3
[97] BiocManager_1.30.18 GlobalOptions_0.1.2
[99] jquerylib_0.1.4 snpStats_1.46.0
[101] bitops_1.0-7 httpuv_1.6.5
[103] R6_2.5.1 BiocIO_1.8.0
[105] promises_1.2.0.1 codetools_0.2-18
[107] assertthat_0.2.1 SummarizedExperiment_1.28.0
[109] rprojroot_2.0.3 rjson_0.2.21
[111] withr_2.5.0 GenomicAlignments_1.34.0
[113] Rsamtools_2.12.0 GenomeInfoDbData_1.2.9
[115] parallel_4.2.0 hms_1.1.2
[117] rmarkdown_2.20 MatrixGenerics_1.10.0
[119] googledrive_2.0.0 Cairo_1.6-0
[121] git2r_0.30.1 getPass_0.2-2
[123] lubridate_1.9.2 restfulr_0.0.15