Last updated: 2020-02-07

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Knit directory: hgen471/

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File Version Author Date Message
Rmd 92e339b Hae Kyung Im 2020-02-07 GRM-LD-score
html 92e339b Hae Kyung Im 2020-02-07 GRM-LD-score 
library(tidyverse)
Registered S3 methods overwritten by 'ggplot2':
  method         from 
  [.quosures     rlang
  c.quosures     rlang
  print.quosures rlang
── Attaching packages ────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1     ✔ purrr   0.3.2
✔ tibble  2.1.2     ✔ dplyr   0.8.1
✔ tidyr   0.8.3     ✔ stringr 1.4.0
✔ readr   1.3.1     ✔ forcats 0.4.0
── Conflicts ───────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
work.dir ="~/Downloads/hapmap/"

functions

tileplot <- function(mat)
{
  mat = data.frame(mat)
  mat$Var1 = factor(rownames(mat), levels=rownames(mat)) ## preserve rowname order
  melted_mat <- gather(mat,key=Var2,value=value,-Var1)
  melted_mat$Var2 = factor(melted_mat$Var2, levels=colnames(mat)) ## preserve colname order
  rango = range(melted_mat$value)
  pp <- ggplot(melted_mat,aes(x=Var1,y=Var2,fill=value)) + geom_tile() ##+scale_fill_gradientn(colours = c("#C00000", "#FF0000", "#FF8080", "#FFC0C0", "#FFFFFF", "#C0C0FF", "#8080FF", "#0000FF", "#0000C0"), limit = c(-1,1))
  pp
}

plot GRM to show population structure

## calculate GRM using plink, use grm.gz format

grmhead = paste0(work.dir,"output/hapmapch22")
cl_calc_grm = glue::glue("plink --bfile hapmapch22 --make-grm-gz --out {grmhead}")
print(cl_calc_grm) ## run this in the command line
plink --bfile hapmapch22 --make-grm-gz --out ~/Downloads/hapmap/output/hapmapch22
## define function to read GRM (in grm-gz format) into matrix

grmgz2mat = function(grmhead)
{
  ## given plink like header, it reads thd grm file and returns matrix of grm
  grm = read.table(paste0(grmhead,".grm.gz"),header=F)
  grmid = read.table(paste0(grmhead,".grm.id"),header=F)
  grmat = matrix(0,max(grm$V1),max(grm$V2))
  rownames(grmat) = grmid$V2
  colnames(grmat) = grmid$V2
  ## fill lower matrix of GRM
  grmat[upper.tri(grmat,diag=TRUE)]= grm$V4
  ## make upper = lower, need to subtract diag
  grmat + t(grmat) - diag(diag(grmat))
}

## select a 2 CEU and 2 YRI individuals
## 1345    NA07349 NA07347 NA07346 1       0       CEU
## 1353    NA12376 NA12546 NA12489 2       0       CEU
## Y004    NA18500 NA18501 NA18502 1       0       YRI missing NA18502
## Y051    NA19208 NA19207 NA19206 1       0       YRI
## Y058    NA19221 NA19223 NA19222 2       0       YRI
ind = c("NA07349","NA07347","NA07346","NA12376","NA12546","NA12489",
        "NA19208","NA19207","NA19206",   "NA19221","NA19223","NA19222")

## read grm calculated in plink into R matrix
grmat = grmgz2mat(grmhead)

## plot grmat to see the population structure
tileplot(grmat[ind,ind])

Version Author Date
92e339b Hae Kyung Im 2020-02-07 

sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

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] forcats_0.4.0   stringr_1.4.0   dplyr_0.8.1     purrr_0.3.2    
[5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.2    ggplot2_3.1.1  
[9] tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       pillar_1.4.1    
 [5] compiler_3.6.0   git2r_0.25.2     workflowr_1.3.0  tools_3.6.0     
 [9] digest_0.6.19    lubridate_1.7.4  jsonlite_1.6     evaluate_0.14   
[13] nlme_3.1-139     gtable_0.3.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.1      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_2.1.0      xfun_0.7         withr_2.1.2      xml2_1.2.0      
[25] httr_1.4.0       knitr_1.23       hms_0.4.2        generics_0.0.2  
[29] fs_1.3.1         rprojroot_1.3-2  grid_3.6.0       tidyselect_0.2.5
[33] glue_1.3.1       R6_2.4.0         readxl_1.3.1     rmarkdown_1.13  
[37] modelr_0.1.4     magrittr_1.5     whisker_0.3-2    backports_1.1.4 
[41] scales_1.0.0     htmltools_0.4.0  rvest_0.3.4      assertthat_0.2.1
[45] colorspace_1.4-1 labeling_0.3     stringi_1.4.3    lazyeval_0.2.2  
[49] munsell_0.5.0    broom_0.5.2      crayon_1.3.4