AF_HF_SNPs
Renee Matthews
2025-05-09
Last updated: 2025-05-09
Checks: 7 0
Knit directory: ATAC_learning/
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Modified: ATAC_learning.Rproj
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Modified: analysis/Jaspar_motif.Rmd
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Modified: analysis/final_four_analysis.Rmd
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | dbb1557 | reneeisnowhere | 2025-05-09 | update reanalysis |
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(ggfortify)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(ggVennDiagram)
library(BiocParallel)
library(ggpubr)
library(edgeR)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(ComplexHeatmap)
library(cowplot)
library(smplot2)
library(readxl)
library(devtools)
library(vargen)Loading Atrial Fibrillation and Heart Failure SNPs
gwas_HF <- readRDS("data/other_papers/HF_gwas_association_downloaded_2025_01_23_EFO_0003144_withChildTraits.RDS")
gwas_ARR <- readRDS("data/other_papers/AF_gwas_association_downloaded_2025_01_23_EFO_0000275.RDS")
Short_gwas_gr <-
gwas_ARR %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="AF") %>%
rbind(gwas_HF %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="HF")) %>%
na.omit() %>%
mutate(seqnames=paste0("chr",CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(start=CHR_POS, end=CHR_POS, width=1) %>%
GRanges()
Short_gwas_10k_gr <-
gwas_ARR %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="AF") %>%
rbind(gwas_HF %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="HF")) %>%
na.omit() %>%
mutate(seqnames=paste0("chr",CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(start=CHR_POS-5000, end=CHR_POS+4999) %>%
GRanges()
Short_gwas_20k_gr <-
gwas_ARR %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="AF") %>%
rbind(gwas_HF %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="HF")) %>%
na.omit() %>%
mutate(seqnames=paste0("chr",CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(start=(CHR_POS-10000),end=(CHR_POS+9999), width=20000) %>%
distinct() %>%
GRanges()Loading ATAC-seq regions
Collapsed_peaks <- read_delim("data/Final_four_data/collapsed_new_peaks.txt", delim = "\t", col_names = TRUE)
Collapsed_peaks_gr <- Collapsed_peaks %>% dplyr::select(chr:Peakid) %>% GRanges()
Motif_list_gr <- readRDS("data/Final_four_data/re_analysis/Motif_list_granges.RDS")
##order specific
df_list <- plyr::llply(Motif_list_gr, as.data.frame)
### no change motif_list_gr names so they do not overwrite the dataframes
names(Motif_list_gr) <- paste0(names(Motif_list_gr), "_gr")
list2env(Motif_list_gr,envir= .GlobalEnv)<environment: R_GlobalEnv>list2env(df_list,envir= .GlobalEnv)<environment: R_GlobalEnv>final_peaks_gr <- Collapsed_peaks %>%
dplyr::filter(Peakid %in% mcols(all_regions_gr)$Peakid) %>%
GRanges()
final_peaks <- Collapsed_peaks %>%
dplyr::filter(Peakid %in% mcols(all_regions_gr)$Peakid)
mrc_lookup <- bind_rows(
(EAR_open %>% dplyr::select(Peakid) %>% mutate(mrc = "EAR_open")),
(EAR_close %>% dplyr::select(Peakid) %>%mutate(mrc = "EAR_close")),
(ESR_open %>% dplyr::select(Peakid) %>%mutate(mrc = "ESR_open")),
(ESR_close %>% dplyr::select(Peakid) %>%mutate(mrc = "ESR_close")),
(ESR_opcl %>% dplyr::select(Peakid) %>%mutate(mrc = "ESR_opcl")),
(ESR_clop %>% dplyr::select(Peakid) %>%mutate(mrc = "ESR_clop")),
(LR_open %>% dplyr::select(Peakid) %>%mutate(mrc = "LR_open")),
(LR_close %>% dplyr::select(Peakid) %>%mutate(mrc = "LR_close")),
(NR %>% dplyr::select(Peakid) %>%mutate(mrc = "NR"))
) %>%
distinct(Peakid, mrc)
ATAC_med_3 <- read_csv("data/Final_four_data/re_analysis/median_3_lfc_norm.csv")%>%
dplyr::select(peak,med_3h_lfc)
ATAC_med_24 <- read_csv("data/Final_four_data/re_analysis/median_24_lfc_norm.csv")%>%
dplyr::select(peak,med_24h_lfc)
ATAC_med_LFC <- ATAC_med_3 %>%
left_join(.,ATAC_med_24)Overlappig ATAC regions and SNPs
gwas_peak_check <- join_overlap_intersect(final_peaks_gr,Short_gwas_gr) %>%
as.data.frame()
gwas_peak_check_10k <- join_overlap_intersect(final_peaks_gr,Short_gwas_10k_gr) %>%
as.data.frame()
gwas_peak_check_20k <- join_overlap_intersect(final_peaks_gr,Short_gwas_20k_gr) %>%
as.data.frame()
count_df <- join_overlap_intersect(Collapsed_peaks_gr, Short_gwas_gr)
gwas_peak_check %>%
dplyr::filter(gwas=="HF;AF") %>%
# dplyr::filter(mrc != "NR")
distinct(SNPS)[1] SNPS
<0 rows> (or 0-length row.names)drawing the heatmap
new_gwas_df <-
gwas_peak_check %>%
as.data.frame() %>%
left_join(., mrc_lookup, by = c("Peakid"="Peakid")) %>%
dplyr::filter(mrc != "NR") %>%
dplyr::filter(!is.na(mrc)) %>%
left_join(ATAC_med_LFC, by = c("Peakid"="peak")) %>%
mutate(dist_to_SNP=0) %>%
group_by(Peakid, SNPS) %>%
summarize(med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
GWAS=paste(unique(gwas),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) %>%
tidyr::unite(name,Peakid,SNPS,sep ="_",remove=FALSE) %>%
arrange(., Peakid)
new_gwas_mat <- new_gwas_df%>%
ungroup() %>%
dplyr::select(name,med_3h_lfc, med_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
new_gwas_name_mat <- new_gwas_df %>%
ungroup() %>%
dplyr::select(name,mrc,GWAS,dist_to_SNP)
row_anno_gwas <-
rowAnnotation(
gwas_status=new_gwas_name_mat$GWAS,
MRC=new_gwas_name_mat$mrc,
direct_overlap=new_gwas_name_mat$dist_to_SNP,
col= list(TE_status=c("TE_peak"="goldenrod",
"not_TE_peak"="lightblue"),
MRC = c("EAR_open" = "#F8766D",
"EAR_close" = "#f6483c",
"ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_clop"="tan",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
gwas_status=c("AF"="green",
"HF"="orange",
"AF;HF"="purple3"),
direct_overlap=c("0"="goldenrod4",
"10"="pink",
"20"="tan2",
"50"="grey8")))
simply_map_gwas <- ComplexHeatmap::Heatmap(new_gwas_mat,
left_annotation = row_anno_gwas,
row_names_max_width = max_text_width(rownames(new_gwas_mat),
gp=gpar(fontsize=16)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map_gwas, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
new_gwas_df %>% distinct# A tibble: 50 × 8
# Groups: Peakid [45]
name Peakid SNPS med_3h_lfc med_24h_lfc GWAS mrc dist_to_SNP
<chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl>
1 chr1.10736684.10… chr1.… rs88… -0.769 -1.08 AF ESR_… 0
2 chr1.109274953.1… chr1.… rs62… 0.315 1.33 AF;HF LR_o… 0
3 chr1.109274953.1… chr1.… rs66… 0.315 1.33 HF LR_o… 0
4 chr1.11792123.11… chr1.… rs17… -0.727 -1.22 AF ESR_… 0
5 chr1.154839629.1… chr1.… rs34… -0.957 -1.57 AF ESR_… 0
6 chr1.170224576.1… chr1.… rs12… -0.673 -2.37 AF LR_c… 0
7 chr1.170224576.1… chr1.… rs72… -0.673 -2.37 AF LR_c… 0
8 chr1.236688731.2… chr1.… rs12… 0.266 -1.11 HF LR_c… 0
9 chr10.103720444.… chr10… rs35… -0.975 -1.92 AF ESR_… 0
10 chr10.20953148.2… chr10… rs79… -0.160 -1.86 AF LR_c… 0
# ℹ 40 more rows
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] vargen_0.2.3
[2] devtools_2.4.5
[3] usethis_3.1.0
[4] readxl_1.4.5
[5] smplot2_0.2.5
[6] cowplot_1.1.3
[7] ComplexHeatmap_2.22.0
[8] ggrepel_0.9.6
[9] plyranges_1.26.0
[10] ggsignif_0.6.4
[11] genomation_1.38.0
[12] edgeR_4.4.2
[13] limma_3.62.2
[14] ggpubr_0.6.0
[15] BiocParallel_1.40.0
[16] ggVennDiagram_1.5.2
[17] scales_1.3.0
[18] VennDiagram_1.7.3
[19] futile.logger_1.4.3
[20] gridExtra_2.3
[21] ggfortify_0.4.17
[22] rtracklayer_1.66.0
[23] org.Hs.eg.db_3.20.0
[24] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[25] GenomicFeatures_1.58.0
[26] AnnotationDbi_1.68.0
[27] Biobase_2.66.0
[28] GenomicRanges_1.58.0
[29] GenomeInfoDb_1.42.3
[30] IRanges_2.40.1
[31] S4Vectors_0.44.0
[32] BiocGenerics_0.52.0
[33] RColorBrewer_1.1-3
[34] broom_1.0.7
[35] kableExtra_1.4.0
[36] lubridate_1.9.4
[37] forcats_1.0.0
[38] stringr_1.5.1
[39] dplyr_1.1.4
[40] purrr_1.0.4
[41] readr_2.1.5
[42] tidyr_1.3.1
[43] tibble_3.2.1
[44] ggplot2_3.5.1
[45] tidyverse_2.0.0
[46] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] later_1.4.1 BiocIO_1.16.0
[3] bitops_1.0-9 cellranger_1.1.0
[5] rpart_4.1.24 XML_3.99-0.18
[7] lifecycle_1.0.4 rstatix_0.7.2
[9] doParallel_1.0.17 rprojroot_2.0.4
[11] vroom_1.6.5 processx_3.8.6
[13] lattice_0.22-6 backports_1.5.0
[15] magrittr_2.0.3 Hmisc_5.2-2
[17] sass_0.4.9 rmarkdown_2.29
[19] remotes_2.5.0 jquerylib_0.1.4
[21] yaml_2.3.10 plotrix_3.8-4
[23] httpuv_1.6.15 sessioninfo_1.2.3
[25] pkgbuild_1.4.6 DBI_1.2.3
[27] pkgload_1.4.0 abind_1.4-8
[29] zlibbioc_1.52.0 RCurl_1.98-1.16
[31] nnet_7.3-20 git2r_0.35.0
[33] circlize_0.4.16 GenomeInfoDbData_1.2.13
[35] svglite_2.1.3 codetools_0.2-20
[37] DelayedArray_0.32.0 xml2_1.3.7
[39] tidyselect_1.2.1 shape_1.4.6.1
[41] farver_2.1.2 UCSC.utils_1.2.0
[43] base64enc_0.1-3 matrixStats_1.5.0
[45] GenomicAlignments_1.42.0 jsonlite_1.9.1
[47] GetoptLong_1.0.5 ellipsis_0.3.2
[49] Formula_1.2-5 iterators_1.0.14
[51] systemfonts_1.2.1 foreach_1.5.2
[53] tools_4.4.2 Rcpp_1.0.14
[55] glue_1.8.0 SparseArray_1.6.2
[57] xfun_0.51 MatrixGenerics_1.18.1
[59] withr_3.0.2 formatR_1.14
[61] fastmap_1.2.0 callr_3.7.6
[63] digest_0.6.37 mime_0.12
[65] timechange_0.3.0 R6_2.6.1
[67] seqPattern_1.38.0 colorspace_2.1-1
[69] RSQLite_2.3.9 utf8_1.2.4
[71] generics_0.1.3 data.table_1.17.0
[73] htmlwidgets_1.6.4 httr_1.4.7
[75] S4Arrays_1.6.0 whisker_0.4.1
[77] pkgconfig_2.0.3 gtable_0.3.6
[79] blob_1.2.4 impute_1.80.0
[81] XVector_0.46.0 htmltools_0.5.8.1
[83] carData_3.0-5 profvis_0.4.0
[85] pwr_1.3-0 clue_0.3-66
[87] png_0.1-8 knitr_1.49
[89] lambda.r_1.2.4 rstudioapi_0.17.1
[91] tzdb_0.4.0 reshape2_1.4.4
[93] rjson_0.2.23 checkmate_2.3.2
[95] curl_6.2.1 zoo_1.8-13
[97] cachem_1.1.0 GlobalOptions_0.1.2
[99] KernSmooth_2.23-26 miniUI_0.1.1.1
[101] parallel_4.4.2 foreign_0.8-88
[103] restfulr_0.0.15 pillar_1.10.1
[105] vctrs_0.6.5 urlchecker_1.0.1
[107] promises_1.3.2 car_3.1-3
[109] xtable_1.8-4 cluster_2.1.8.1
[111] htmlTable_2.4.3 evaluate_1.0.3
[113] magick_2.8.5 cli_3.6.4
[115] locfit_1.5-9.12 compiler_4.4.2
[117] futile.options_1.0.1 Rsamtools_2.22.0
[119] rlang_1.1.5 crayon_1.5.3
[121] ps_1.9.0 getPass_0.2-4
[123] plyr_1.8.9 fs_1.6.5
[125] stringi_1.8.4 viridisLite_0.4.2
[127] gridBase_0.4-7 munsell_0.5.1
[129] Biostrings_2.74.1 Matrix_1.7-3
[131] BSgenome_1.74.0 patchwork_1.3.0
[133] hms_1.1.3 bit64_4.6.0-1
[135] shiny_1.10.0 KEGGREST_1.46.0
[137] statmod_1.5.0 SummarizedExperiment_1.36.0
[139] memoise_2.0.1 bslib_0.9.0
[141] bit_4.6.0