Last updated: 2020-08-05
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Knit directory: gene_level_fine_mapping/
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This file is to merge the HiC cutoff5.txt file with the eqtl file.
HiC = read.delim2('/Users/nicholeyang/Desktop/Rotation2/data/phic/PCHiC_peak_matrix_cutoff5.txt',
header = TRUE, sep="\t", quote = '')head(HiC) baitChr baitStart baitEnd baitID baitName oeChr oeStart
1 1 831895 848168 218 RP11-54O7.16;RP11-54O7.1 1 850619
2 1 831895 848168 218 RP11-54O7.16;RP11-54O7.1 1 874082
3 1 831895 848168 218 RP11-54O7.16;RP11-54O7.1 1 889424
4 1 831895 848168 218 RP11-54O7.16;RP11-54O7.1 1 903641
5 1 831895 848168 218 RP11-54O7.16;RP11-54O7.1 1 1206874
6 1 831895 848168 218 RP11-54O7.16;RP11-54O7.1 1 1239426
oeEnd oeID oeName dist
1 874081 220 AL645608.1;RP11-54O7.3;SAMD11 22319
2 876091 221 . 35055
3 903640 223 KLHL17;NOC2L;PLEKHN1 56501
4 927394 224 C1orf170;PLEKHN1 75486
5 1212438 254 RP5-902P8.10;UBE2J2 369625
6 1278099 257 ACAP3;CPSF3L;GLTPD1;PUSL1;RP5-890O3.9;TAS1R3 418731
Mon Mac0 Mac1 Mac2
1 25.5465679105014 23.1552423883082 37.5487280828996 34.2516228390872
2 1.93228468639624 2.56439166855171 3.35832960953219 5.71781390625809
3 4.92356508877905 4.10863780276095 7.95191424258896 6.67100364028254
4 5.05024811682171 4.46056127181517 7.55274568483025 5.76434097597794
5 3.95344923709904 3.05342972692307 5.9266242722129 4.150801828427
6 0.0609499671511617 2.05386132479862 0.618138347628004 0.913792765770951
Neu MK EP Ery
1 28.2293866333124 25.4329063090307 36.9483228692508 30.4394201689412
2 0.610967942588545 1.58881606916046 3.4487280264009 1.8719919279133
3 1.17403563535694 4.10809950944708 5.5704135517231 3.20890617277731
4 1.38086678794133 3.26110508356071 5.31446634983579 4.7459001438327
5 0.705195645656159 1.92373630753447 4.26918359263104 1.51009840710612
6 1.62371231175963 0.0954144429114292 5.31410964122625 2.40994217190278
FoeT nCD4 tCD4 aCD4
1 13.4832147246969 11.8032695872144 26.7897908943447 11.2517812208312
2 0.887231881369446 0.459330395938111 1.95896718272313 0.459132392236852
3 1.9950819605042 2.80478364273435 5.39252321200808 2.57999302089654
4 2.12365103441838 3.18449816361427 4.80853619861617 3.61682722105978
5 0.728922297712605 2.30806186405942 2.47644450627396 3.53054817303909
6 0.972473318644208 2.07557541736959 1.63463813746841 2.64461892923435
naCD4 nCD8 tCD8 nB
1 13.0243895499638 20.7260360866038 20.4969874759968 14.1596546965918
2 0.808471646512836 0.985252844172257 1.44004824617697 0.399504574732776
3 4.1352263052192 3.69829715449806 3.03941508666268 1.30051276658696
4 2.56785725673803 8.14891311290246 5.26392207249727 2.85027486343664
5 4.26835821053397 3.59765758511374 1.96913723097127 2.16256176269305
6 1.31022047951012 0.26139793916709 1.07865712506174 0.0908178611310335
tB clusterID clusterPostProb
1 14.8452299904824 19 0.995
2 0.892454696469067 33 0.979
3 2.79496716119577 32 0.973
4 4.66582085120164 21 0.505
5 3.60529888022249 32 0.828
6 1.93304679521667 34 0.312dim(HiC)[1][1] 728838snps = read.csv("/Users/nicholeyang/Desktop/Rotation2/data/hglft_genome_38to19.bed", header = FALSE)
snp38 = read.csv('/Users/nicholeyang/Desktop/Rotation2/data/snps.txt', header = FALSE)
x = strsplit(as.character(snps$V1), '-')
x2 = unlist(lapply(x, function(y) y[1]))
snp_info = strsplit(x2, ':')
chr = unlist(lapply(snp_info, function(x) x[1]))
start = unlist(lapply(snp_info, function(x) x[2]))
dat_snp = data.frame(cbind(chr, start, as.character(snp38$V1)))
colnames(dat_snp) = c('chr', 'start', 'loc_38map')head(dat_snp) chr start loc_38map
1 chr1 100643730 chr1:100178174-100178174
2 chr1 101361178 chr1:100895622-100895622
3 chr1 10271688 chr1:10211630-10211630
4 chr1 104097685 chr1:103555063-103555063
5 chr1 108588372 chr1:108045750-108045750
6 chr1 108742123 chr1:108199501-108199501dat_snp$chr = gsub('\\D','', dat_snp$chr)
dat_snp$start = as.numeric(as.character(dat_snp$start))
index_list = c()
index = which((HiC$oeChr == dat_snp$chr[1]) & (HiC$oeStart < dat_snp$start[1]) & (HiC$oeEnd > dat_snp$start[1]))
df_snps = apply(dat_snp[1, ], 2, function(co) rep(co, each = length(index)))
index_list = c(index_list, index)
for(i in 2:dim(dat_snp)[1]){
index = which((HiC$oeChr == dat_snp$chr[i]) & (HiC$oeStart < dat_snp$start[i]) & (HiC$oeEnd > dat_snp$start[i]))
index_list = c(index_list, index)
snps = apply(dat_snp[i, ], 2, function(co) rep(co, each = length(index)))
df_snps = rbind(df_snps, snps)
}hic_eqtl = cbind(df_snps, HiC[index_list, ])
row.names(hic_eqtl) = seq(1:dim(hic_eqtl)[1])
save(hic_eqtl, file = '/Users/nicholeyang/Desktop/gene_level_fine_mapping/data/hic_eqtl.RData')
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.6.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4 rprojroot_1.3-2 digest_0.6.25 later_1.0.0
[5] R6_2.4.1 backports_1.1.5 git2r_0.26.1 magrittr_1.5
[9] evaluate_0.14 highr_0.8 stringi_1.4.6 rlang_0.4.5
[13] fs_1.3.2 promises_1.1.0 whisker_0.4 rmarkdown_2.1
[17] tools_3.6.3 stringr_1.4.0 glue_1.3.2 httpuv_1.5.2
[21] xfun_0.12 yaml_2.2.1 compiler_3.6.3 htmltools_0.4.0
[25] knitr_1.28