6 modality weights from Munro et al.
XSun
2024-06-05
Last updated: 2024-06-14
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Knit directory: multigroup_ctwas_analysis/
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library(ggplot2)
library(tidyverse)Introduction
The data is from Multimodal analysis of RNA sequencing data powers discovery of complex trait genetics. It provides FUSION weights for gene expression, isoform ratios, splice junction usage, alternative TSS/polyA usage, and RNA stability.
dat_sum <- readxl::read_excel("/project2/xinhe/shared_data/multigroup_ctwas/weights/files_Munro/Munro_data_summary.xlsx")
DT::datatable(dat_sum,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Table S1 from the paper: Counts of xGenes and xQTLs per GTEx tissue, modalities mapped separately'),options = list(pageLength = 10) )genes_expression_only: Number of genes with at least one cis-QTL for expression (cis-eQTL) and none for any other modality
genes_expression_and_others: Number of genes with at least one cis-QTL for expression and at least one for any other modality
genes_others_only: Number of genes with no cis-QTLs for expression and at least one for any other modality
genes_total: Number of genes with any cis-QTL, i.e. the sum of the previous three columns
genes_expression/isoforms/splicing/alt_TSS/alt_polyA/stability: Number of genes with at least one cis-QTL for expression/isoforms/splicing/alt_TSS/alt_polyA/stability
qtls_expression/isoforms/splicing/alt_TSS/alt_polyA/stability Number of conditionally independent cis-QTLs for expression/isoforms/splicing/alt_TSS/alt_polyA/stability
Expression models
Comparing with predictdb exression models
load("/project2/xinhe/shared_data/multigroup_ctwas/weights/summary/compare_expr_predictdb_munro.rdata")
DT::datatable(df_summary,caption = htmltools::tags$caption(style = 'caption-side: left; text-align: left; color:black; font-size:150%; ', ''),options = list(pageLength = 5))An example: LDL-Liver as an example (from the single group analysis)
z-scores for the overlapped genes
We compared the imputed gene-zscores for the overlapped genes between these 2 datasets.
Taking LDL-Liver as an example (from the single group analysis),
z_gene_earlier <- readRDS("/project/xinhe/xsun/multi_group_ctwas/1.single_tissue/results/LDL-ukb-d-30780_irnt/Liver/LDL-ukb-d-30780_irnt_Liver.z_gene.RDS")
z_gene_earlier$ensg_id <- sub("\\..*", "", z_gene_earlier$id)
z_gene_current <- readRDS("/project/xinhe/xsun/multi_group_ctwas/6.multi_group_munro/results_ldl_liver/LDL-ukb-d-30780_irnt/LDL-ukb-d-30780_irnt.z_gene.RDS")
z_gene_current$ensg_id <- sub("\\|.*", "", z_gene_current$id)
overlap <- merge(z_gene_current,z_gene_earlier, by="ensg_id")
ggplot(data = overlap) + aes(x = z.x, y=z.y) +
geom_point() +
geom_abline(slope = 1, intercept = 0, col="red") +
labs(x="Munro zscores (LDL-liver, single group)",y="Predictdb zscores (LDL-liver, single group)")+
theme_minimal()
| Version | Author | Date |
|---|---|---|
| b4a73d9 | XSun | 2024-06-05 |
Comparing results with the published ctwas paper
mapping_data <- readRDS("/project2/xinhe/shared_data/multigroup_ctwas/weights/Munro_mapping.RDS")
finemap_res <- readRDS("/project/xinhe/xsun/multi_group_ctwas/6.multi_group_munro/results_ldl_liver/LDL-ukb-d-30780_irnt/LDL-ukb-d-30780_irnt.ctwas.res.RDS")
finemap_gene_res <- finemap_res[finemap_res$type!="SNP",]
finemap_gene_res$gene <- sapply(finemap_gene_res$id, function(x){unlist(strsplit(x,"[|]"))[1]})
finemap_gene_res <- finemap_gene_res %>% plyr::join(mapping_data, by = "gene") %>% dplyr::select(-gene) %>% na.omit()
finemap_gene_res <- finemap_gene_res %>% group_by(id) %>% mutate(susie_pip_adjusted = ifelse(n() > 1, susie_pip / n(), susie_pip)) %>% ungroup() %>% dplyr::select(-susie_pip) %>% dplyr::rename(susie_pip = susie_pip_adjusted)
finemap_gene_res <- finemap_gene_res[,c("genename","susie_pip","type","context","gene_type")]
DT::datatable(finemap_gene_res,caption = htmltools::tags$caption(style = 'caption-side: left; text-align: left; color:black; font-size:150%;','Gene PIPs for the LDL-liver single group analysis'),options = list(pageLength = 5))APA models
Some stats
load("/project2/xinhe/shared_data/multigroup_ctwas/weights/files_Munro/apa.stat.rdata")
colnames(sum) <- c("tissue","#of genes","average # of non-zero weights_lasso","average # of non-zero weights_enet")
DT::datatable(sum,caption = htmltools::tags$caption(style = 'caption-side: left; text-align: left; color:black; font-size:150%;'),options = list(pageLength = 5))Comparing the APA model from Chen et al
Chen et al: https://sq-96.github.io/multigroup_ctwas_analysis/apa_weights_analysis_chenetal.html
dat_chen <- get(load("/project2/xinhe/shared_data/multigroup_ctwas/weights/apa.stat.rdata"))
dat_chen$tissue <- rownames(dat_chen)
load("/project2/xinhe/shared_data/multigroup_ctwas/weights/files_Munro/apa.stat.rdata")
overlap <- merge(sum,dat_chen, by="tissue")
ggplot(data = overlap, aes(x=ngene_alltissue.x, y=ngene_alltissue.y)) +
geom_point()+
geom_abline(slope = 1, intercept = 0, col="red") +
labs(x="#of apa target in Munro et al (current study)", y="#of apa target in Chen et al", title = "Comparing apa targets reported by 2 studies") +
theme_minimal()
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.1 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
[5] readr_2.1.2 tidyr_1.3.0 tibble_3.2.1 tidyverse_1.3.1
[9] ggplot2_3.5.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 lubridate_1.8.0 assertthat_0.2.1 rprojroot_2.0.3
[5] digest_0.6.29 utf8_1.2.2 plyr_1.8.7 R6_2.5.1
[9] cellranger_1.1.0 backports_1.4.1 reprex_2.0.1 evaluate_0.15
[13] highr_0.9 httr_1.4.3 pillar_1.9.0 rlang_1.1.2
[17] readxl_1.4.0 rstudioapi_0.13 whisker_0.4 jquerylib_0.1.4
[21] DT_0.22 rmarkdown_2.25 labeling_0.4.2 htmlwidgets_1.5.4
[25] munsell_0.5.0 broom_0.8.0 compiler_4.2.0 httpuv_1.6.5
[29] modelr_0.1.8 xfun_0.41 pkgconfig_2.0.3 htmltools_0.5.2
[33] tidyselect_1.2.0 workflowr_1.7.0 fansi_1.0.3 crayon_1.5.1
[37] tzdb_0.4.0 dbplyr_2.1.1 withr_2.5.0 later_1.3.0
[41] grid_4.2.0 jsonlite_1.8.0 gtable_0.3.0 lifecycle_1.0.4
[45] DBI_1.2.2 git2r_0.30.1 magrittr_2.0.3 scales_1.3.0
[49] cli_3.6.1 stringi_1.7.6 farver_2.1.0 fs_1.5.2
[53] promises_1.2.0.1 xml2_1.3.3 bslib_0.3.1 ellipsis_0.3.2
[57] generics_0.1.2 vctrs_0.6.5 tools_4.2.0 glue_1.6.2
[61] crosstalk_1.2.0 hms_1.1.1 fastmap_1.1.0 yaml_2.3.5
[65] colorspace_2.0-3 rvest_1.0.2 knitr_1.39 haven_2.5.0
[69] sass_0.4.1