Comparing single-group & multi-group for LDL
XSun
2024-08-25
Last updated: 2024-08-26
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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library(ctwas)Warning: replacing previous import 'utils::download.file' by
'restfulr::download.file' when loading 'rtracklayer'library(EnsDb.Hsapiens.v86)Loading required package: ensembldbLoading required package: BiocGenerics
Attaching package: 'BiocGenerics'The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colnames, dirname, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
tapply, union, unique, unsplit, which.max, which.minLoading required package: GenomicRangesLoading required package: stats4Loading required package: S4Vectors
Attaching package: 'S4Vectors'The following objects are masked from 'package:base':
I, expand.grid, unnameLoading required package: IRangesLoading required package: GenomeInfoDbLoading required package: GenomicFeaturesLoading required package: AnnotationDbiLoading required package: BiobaseWelcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.Loading required package: AnnotationFilter
Attaching package: 'ensembldb'The following object is masked from 'package:stats':
filterlibrary(ggplot2)
library(RColorBrewer)
load("/project2/xinhe/shared_data/multigroup_ctwas/weights/E_S_A_mapping_updated.RData")
ens_db <- EnsDb.Hsapiens.v86
trait <- "LDL-ukb-d-30780_irnt"
gwas_n <- 343621
sum_pve_across_types <- function(ctwas_parameters) {
# Round the group_pve values
pve <- round(ctwas_parameters$group_pve, 4)
pve <- as.data.frame(pve)
# Extract SNP PVE for later use
SNP_pve <- pve["SNP", ]
# Add type and context columns
pve$type <- sapply(rownames(pve), function(x) { unlist(strsplit(x, "[|]"))[1] })
pve$context <- sapply(rownames(pve), function(x) { unlist(strsplit(x, "[|]"))[2] })
# Remove rows with NA values and sort
pve <- na.omit(pve)
pve <- pve[order(rownames(pve)), ]
# Aggregate PVE by type
df_pve <- aggregate(pve$pve, by = list(pve$type), FUN = sum)
colnames(df_pve) <- c("type", "total_pve")
df_pve$total_pve <- round(df_pve$total_pve, 4)
# Add context-specific columns
for (context in unique(pve$context)) {
context_pve <- aggregate(pve$pve, by = list(pve$type, pve$context), FUN = sum)
context_pve <- context_pve[context_pve$Group.2 == context, ]
colnames(context_pve)[3] <- context
df_pve <- merge(df_pve, context_pve[, c("Group.1", context)], by.x = "type", by.y = "Group.1", all.x = TRUE)
}
# Insert SNP PVE
SNP_row <- c("SNP", SNP_pve, rep(0, ncol(df_pve) - 2))
df_pve <- rbind(df_pve, SNP_row)
# Convert to numeric except for the type column
df_pve[, -1] <- lapply(df_pve[, -1], as.numeric)
# Sum all rows and add a sum_pve row
sum_row <- colSums(df_pve[, -1], na.rm = TRUE)
sum_row <- c("total_pve", sum_row)
df_pve <- rbind(df_pve, sum_row)
# Clean up row names and return
row.names(df_pve) <- NULL
return(df_pve)
}
palette <- c(brewer.pal(12, "Paired"), brewer.pal(12, "Set3"), brewer.pal(6, "Dark2"))
pve_pie_chart <- function(pve_vector, palette=NULL, title) {
# Create data frame for plotting
data <- data.frame(
Group = names(pve_vector),
Value = pve_vector
)
# Calculate percentages
data$Percentage <- round(100 * data$Value / sum(data$Value), 1)
# Set palette if not specified
if (is.null(palette)) {
palette <- brewer.pal(min(8, length(data$Group)), "Set3")
}
# Create pie chart
ggplot(data, aes(x = "", y = Value, fill = Group)) +
geom_bar(stat = "identity", width = 1, color = "white") +
coord_polar(theta = "y") + # This transforms the bar chart into a pie chart
scale_fill_manual(values = palette, name = "Group") +
labs(title = title, x = NULL, y = NULL) +
theme_void() +
theme(plot.title = element_text(hjust = 0.5),
legend.position = "right",
legend.title = element_text(face = "bold"),
legend.text = element_text(size = 12)) +
geom_text(aes(label = paste(Percentage, "%", sep="")), position = position_stack(vjust = 0.5))
}Settings
Weights:
single group: Liver, eQTL
multi group: Liver, Spleen, Adipose_Subcutaneous, Adrenal_Gland, Esophagus_Mucosa; eQTL + sQTL
- drop_strand_ambig = TRUE,
- scale_by_ld_variance = TRUE,
- load_predictdb_LD = F,
Main function
- niter_prefit = default,
- niter = default,
- pre-estimate L
Results
Single group
Parameters
results_dir_single <- paste0("/project/xinhe/xsun/multi_group_ctwas/xxxintalk/results_predictdb_main_single/",trait,"/")
finemap.res.single <- readRDS(paste0(results_dir_single,trait,".ctwas.res.RDS"))
snp_map.single <- readRDS(paste0(results_dir_single,trait,".snp_map.RDS"))
res.single <- finemap.res.single$finemap_res
param.single <- finemap.res.single$param
make_convergence_plots(param.single, gwas_n)
ctwas_parameters.single <- summarize_param(param.single, gwas_n)
group_size.single <- data.frame(group = names(ctwas_parameters.single$group_size),
group_size = as.vector(ctwas_parameters.single$group_size))
group_size.single <- t(group_size.single)
DT::datatable(group_size.single,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Group size'),options = list(pageLength = 5) )ctwas results
annotated_finemap_res.single <- anno_finemap_res(finemap_res = res.single,
snp_map = snp_map.single,
gene_annot = E_S_A_mapping,
use_gene_pos = "mid",
filter_protein_coding_genes = T,
drop_unannotated_genes = T,
filter_cs = T)2024-08-26 03:07:04 INFO::Annotating ctwas finemapping result ...
2024-08-26 03:07:30 INFO::keep only protein coding genes
2024-08-26 03:07:30 INFO::keep only results in credible sets
2024-08-26 03:07:30 INFO::add gene_name and gene_type
2024-08-26 03:07:30 INFO::use gene mid positions
2024-08-26 03:07:30 INFO::add SNP positionsres_gene.single <- annotated_finemap_res.single[annotated_finemap_res.single$type != "SNP",]
combined_pip_by_context.single <- combine_gene_pips(finemap_res = annotated_finemap_res.single,
by = "type", digits = 4)
highpip.single <- combined_pip_by_context.single[combined_pip_by_context.single$combined_pip > 0.8,]
DT::datatable(highpip.single,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes with PIP > 0.8'),options = list(pageLength = 5) )Multi group
Parameters
results_dir_multi <- paste0("/project/xinhe/xsun/multi_group_ctwas/xxxintalk/results_predictdb_main_multi/",trait,"/")
finemap.res.multi <- readRDS(paste0(results_dir_multi,trait,".ctwas.res.RDS"))
snp_map.multi <- readRDS(paste0(results_dir_multi,trait,".snp_map.RDS"))
res.multi <- finemap.res.multi$finemap_res
param.multi <- finemap.res.multi$param
make_convergence_plots(param.multi, gwas_n,colors = palette)
ctwas_parameters.multi <- summarize_param(param.multi, gwas_n)
group_size.multi <- data.frame(group = names(ctwas_parameters.multi$group_size),
group_size = as.vector(ctwas_parameters.multi$group_size))
group_size.multi <- t(group_size.multi)
DT::datatable(group_size.multi,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Group size'),options = list(pageLength = 5) )para.multi <- sum_pve_across_types(ctwas_parameters.multi)
DT::datatable(para.multi,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Heritability contribution by contexts'),options = list(pageLength = 5) )pve_vector.multi <- as.numeric(para.multi$total_pve[1:3])
names(pve_vector.multi) <- para.multi$type[1:3]
pve_pie_chart(pve_vector.multi, title = "Pie Chart of PVE across Types", palette)
ctwas results
annotated_finemap_res.multi <- anno_finemap_res(finemap_res = res.multi,
snp_map = snp_map.multi,
gene_annot = E_S_A_mapping,
use_gene_pos = "mid",
filter_protein_coding_genes = T,
drop_unannotated_genes = T,
filter_cs = T)2024-08-26 03:08:01 INFO::Annotating ctwas finemapping result ...
2024-08-26 03:08:13 INFO::keep only protein coding genes
2024-08-26 03:08:13 INFO::keep only results in credible sets
2024-08-26 03:08:13 INFO::add gene_name and gene_type
2024-08-26 03:08:13 INFO::split PIPs for traits mapped to multiple genes
2024-08-26 03:08:13 INFO::use gene mid positions
2024-08-26 03:08:13 INFO::add SNP positionsres_gene.multi <- annotated_finemap_res.multi[annotated_finemap_res.multi$type != "SNP",]
combined_pip_by_context.multi <- combine_gene_pips(finemap_res = annotated_finemap_res.multi,
by = "type", digits = 4)
highpip.multi <- combined_pip_by_context.multi[combined_pip_by_context.multi$combined_pip > 0.8,]
DT::datatable(highpip.multi,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes with PIP > 0.8'),options = list(pageLength = 5) )Comparing the results
overlap <- highpip.multi[highpip.multi$gene_name %in% highpip.single$gene_name,]
sprintf("the number of genes reported by single group analysis: %s", nrow(highpip.single))[1] "the number of genes reported by single group analysis: 33"sprintf("the number of genes reported by multi group analysis: %s", nrow(highpip.multi))[1] "the number of genes reported by multi group analysis: 37"sprintf("the number of overlapped gene: %s", nrow(overlap))[1] "the number of overlapped gene: 16"DT::datatable(overlap,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','overlapped genes'),options = list(pageLength = 5) )multi_unique <- combined_pip_by_context.multi[!combined_pip_by_context.multi$gene_name %in% overlap$gene_name,]
multi_unique <- multi_unique[multi_unique$combined_pip > 0.8,]
DT::datatable(multi_unique,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Unique genes -- multi group'),options = list(pageLength = 5) )unique_detail <- res_gene.multi[!res_gene.multi$gene_name %in%overlap$gene_name,]
unique_detail <- unique_detail[unique_detail$gene_name %in% highpip.multi$gene_name,]
save(unique_detail, file = "/project/xinhe/xsun/multi_group_ctwas/xxxintalk/unique_detail.rdata")
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] C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] RColorBrewer_1.1-3 ggplot2_3.5.1
[3] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.20.2
[5] AnnotationFilter_1.20.0 GenomicFeatures_1.48.3
[7] AnnotationDbi_1.58.0 Biobase_2.56.0
[9] GenomicRanges_1.48.0 GenomeInfoDb_1.39.9
[11] IRanges_2.30.0 S4Vectors_0.34.0
[13] BiocGenerics_0.42.0 ctwas_0.4.7
loaded via a namespace (and not attached):
[1] colorspace_2.0-3 rjson_0.2.21
[3] ellipsis_0.3.2 rprojroot_2.0.3
[5] XVector_0.36.0 locuszoomr_0.2.1
[7] fs_1.5.2 rstudioapi_0.13
[9] farver_2.1.0 DT_0.22
[11] ggrepel_0.9.1 bit64_4.0.5
[13] fansi_1.0.3 xml2_1.3.3
[15] codetools_0.2-18 logging_0.10-108
[17] cachem_1.0.6 knitr_1.39
[19] jsonlite_1.8.0 workflowr_1.7.0
[21] Rsamtools_2.12.0 dbplyr_2.1.1
[23] png_0.1-7 readr_2.1.2
[25] compiler_4.2.0 httr_1.4.3
[27] assertthat_0.2.1 Matrix_1.5-3
[29] fastmap_1.1.0 lazyeval_0.2.2
[31] cli_3.6.1 later_1.3.0
[33] htmltools_0.5.2 prettyunits_1.1.1
[35] tools_4.2.0 gtable_0.3.0
[37] glue_1.6.2 GenomeInfoDbData_1.2.8
[39] dplyr_1.1.4 rappdirs_0.3.3
[41] Rcpp_1.0.12 jquerylib_0.1.4
[43] vctrs_0.6.5 Biostrings_2.64.0
[45] rtracklayer_1.56.0 crosstalk_1.2.0
[47] xfun_0.41 stringr_1.5.1
[49] lifecycle_1.0.4 irlba_2.3.5
[51] restfulr_0.0.14 XML_3.99-0.14
[53] zlibbioc_1.42.0 zoo_1.8-10
[55] scales_1.3.0 gggrid_0.2-0
[57] hms_1.1.1 promises_1.2.0.1
[59] MatrixGenerics_1.8.0 ProtGenerics_1.28.0
[61] parallel_4.2.0 SummarizedExperiment_1.26.1
[63] LDlinkR_1.2.3 yaml_2.3.5
[65] curl_4.3.2 memoise_2.0.1
[67] sass_0.4.1 biomaRt_2.54.1
[69] stringi_1.7.6 RSQLite_2.3.1
[71] highr_0.9 BiocIO_1.6.0
[73] filelock_1.0.2 BiocParallel_1.30.3
[75] rlang_1.1.2 pkgconfig_2.0.3
[77] matrixStats_0.62.0 bitops_1.0-7
[79] evaluate_0.15 lattice_0.20-45
[81] purrr_1.0.2 labeling_0.4.2
[83] GenomicAlignments_1.32.0 htmlwidgets_1.5.4
[85] cowplot_1.1.1 bit_4.0.4
[87] tidyselect_1.2.0 magrittr_2.0.3
[89] R6_2.5.1 generics_0.1.2
[91] DelayedArray_0.22.0 DBI_1.2.2
[93] withr_2.5.0 pgenlibr_0.3.3
[95] pillar_1.9.0 KEGGREST_1.36.3
[97] RCurl_1.98-1.7 mixsqp_0.3-43
[99] tibble_3.2.1 crayon_1.5.1
[101] utf8_1.2.2 BiocFileCache_2.4.0
[103] plotly_4.10.0 tzdb_0.4.0
[105] rmarkdown_2.25 progress_1.2.2
[107] grid_4.2.0 data.table_1.14.2
[109] blob_1.2.3 git2r_0.30.1
[111] digest_0.6.29 tidyr_1.3.0
[113] httpuv_1.6.5 munsell_0.5.0
[115] viridisLite_0.4.0 bslib_0.3.1