Enrichment analysis using mapgen with TORUS
Kaixuan Luo
2022-04-11
Last updated: 2022-04-22
Checks: 7 0
Knit directory: analysis_pipelines/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | cb7333c | kevinlkx | 2022-04-22 | fixed bugs in run_torus() |
| html | ce62d73 | kevinlkx | 2022-04-22 | Build site. |
| 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 with TORUS.
Univariate enrichment analysis
Here we use scATAC-seq DA peaks for each cell type as a separate annotation (univariate).
This example is based on the R script from Alan
suppressMessages(library(liftOver))
suppressMessages(library(ComplexHeatmap))
library(mapgen)data.dir <- '/project2/gca/Heart_Atlas/reorganized_data/example_data'Load GWAS summary statistics of AFib
gwas.sumstats <- readRDS(paste0(data.dir, '/GWAS/ebi-a-GCST006414_aFib.df.rds'))
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))})
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$enrich
}
saveRDS(enrich.res, paste0(data.dir, '/Torus/Torus_univariate_enrichment_result.rds'))Compare to pre-computed result
enrich.alltraits.res <- readRDS(paste0(data.dir,'/Torus/Torus_CellType_Enrichment_Results_Univariate_MORE.df.rds'))
identical(enrich.res, enrich.alltraits.res$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 4.0.4 (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 parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] mapgen_0.3.7
[2] ComplexHeatmap_2.6.2
[3] liftOver_1.14.0
[4] Homo.sapiens_1.3.1
[5] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[6] org.Hs.eg.db_3.12.0
[7] GO.db_3.12.1
[8] OrganismDbi_1.32.0
[9] GenomicFeatures_1.42.3
[10] AnnotationDbi_1.52.0
[11] Biobase_2.50.0
[12] rtracklayer_1.50.0
[13] GenomicRanges_1.42.0
[14] GenomeInfoDb_1.26.7
[15] IRanges_2.24.1
[16] S4Vectors_0.28.1
[17] BiocGenerics_0.36.1
[18] gwascat_2.22.0
[19] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.0-3 rjson_0.2.21
[3] ellipsis_0.3.2 rprojroot_2.0.2
[5] circlize_0.4.14 XVector_0.30.0
[7] GlobalOptions_0.1.2 fs_1.5.2
[9] clue_0.3-60 rstudioapi_0.13
[11] bit64_4.0.5 fansi_1.0.3
[13] xml2_1.3.3 splines_4.0.4
[15] snpStats_1.40.0 cachem_1.0.6
[17] knitr_1.38 jsonlite_1.8.0
[19] Rsamtools_2.6.0 Cairo_1.5-15
[21] cluster_2.1.2 dbplyr_2.1.1
[23] png_0.1-7 graph_1.68.0
[25] BiocManager_1.30.16 readr_2.1.2
[27] compiler_4.0.4 httr_1.4.2
[29] assertthat_0.2.1 Matrix_1.4-1
[31] fastmap_1.1.0 cli_3.2.0
[33] later_1.3.0 htmltools_0.5.2
[35] prettyunits_1.1.1 tools_4.0.4
[37] gtable_0.3.0 glue_1.6.2
[39] GenomeInfoDbData_1.2.4 dplyr_1.0.8
[41] rappdirs_0.3.3 Rcpp_1.0.8.3
[43] jquerylib_0.1.4 vctrs_0.4.1
[45] Biostrings_2.58.0 xfun_0.30
[47] stringr_1.4.0 ps_1.6.0
[49] lifecycle_1.0.1 XML_3.99-0.9
[51] scales_1.2.0 getPass_0.2-2
[53] zlibbioc_1.36.0 BSgenome_1.58.0
[55] VariantAnnotation_1.36.0 hms_1.1.1
[57] promises_1.2.0.1 MatrixGenerics_1.2.1
[59] SummarizedExperiment_1.20.0 RBGL_1.66.0
[61] RColorBrewer_1.1-3 yaml_2.3.5
[63] curl_4.3.2 memoise_2.0.1
[65] ggplot2_3.3.5 sass_0.4.1
[67] biomaRt_2.46.3 stringi_1.7.6
[69] RSQLite_2.2.11 highr_0.9
[71] BiocParallel_1.24.1 shape_1.4.6
[73] rlang_1.0.2 pkgconfig_2.0.3
[75] matrixStats_0.61.0 bitops_1.0-7
[77] evaluate_0.15 lattice_0.20-45
[79] purrr_0.3.4 GenomicAlignments_1.26.0
[81] bit_4.0.4 processx_3.5.3
[83] tidyselect_1.1.2 magrittr_2.0.3
[85] R6_2.5.1 generics_0.1.2
[87] DelayedArray_0.16.3 DBI_1.1.2
[89] pillar_1.7.0 whisker_0.4
[91] survival_3.3-1 RCurl_1.98-1.6
[93] tibble_3.1.6 crayon_1.5.1
[95] utf8_1.2.2 BiocFileCache_1.14.0
[97] tzdb_0.3.0 rmarkdown_2.13
[99] GetoptLong_1.0.5 progress_1.2.2
[101] blob_1.2.3 callr_3.7.0
[103] git2r_0.30.1 digest_0.6.29
[105] httpuv_1.6.5 munsell_0.5.0
[107] openssl_2.0.0 bslib_0.3.1
[109] askpass_1.1