nievergeltlab

library(Hmisc)
library(data.table)
cormethod="pearson"
windowsize=300000 #
windowjump=10000 #Jump over this increment for correlation analysis
minR=0.6 #minimum correlation to report

#GeMes https://www.cell.com/ajhg/pdfExtended/S0002-9297(14)00069-X 
#http://www.sthda.com/english/wiki/correlation-matrix-a-quick-start-guide-to-analyze-format-and-visualize-a-correlation-matrix-using-r-software
flattenCorrMatrix <- function(cormat, pmat) {

nievergeltlab

for chr in 10 # X # 2 6 11 # {7..20} # {2..20} # {1..20} #22 # {1..8} # {1..22} X MT XY Y
do
echo $chr
date
 mkdir temporary_files/chr"$chr"_baf1
 mkdir temporary_files/chr"$chr"_baf2
 mkdir temporary_files/chr"$chr"_baf3
 mkdir temporary_files/chr"$chr"_baf4
 mkdir temporary_files/chr"$chr"_l2r1

nievergeltlab

#!/usr/bin/env python
import math
########################################################
# plink2metasoft.py                                    
#   Convert Plink .assoc files to Metasoft input file  
#   Free license -- you are free to use it in any ways 
#   Buhm Han (2012)                                    
########################################################

import sys, subprocess, os

nievergeltlab

  BOLT-LMM_v2.3.2/bolt \
    --bed=pca/UKB_tinnitus_eur_unrelated_{1..22}.bed \
    --bim=pca/UKB_tinnitus_eur_unrelated_{1..22}.bim \
    --fam=pca/UKB_tinnitus_eur_unrelated_1.fam  \
    --LDscoresFile=BOLT-LMM_v2.3.2/tables/LDSCORE.1000G_EUR.tab.gz \
    --geneticMapFile=BOLT-LMM_v2.3.2/tables/genetic_map_hg19_withX.txt.gz \
    --lmmForceNonInf \
    --numThreads=6 \
    --bgenFile=/mnt/ukbb/adam/bgen/ukb_imp_chr{1..22}_v3.bgen \
    --bgenMinMAF=1e-3 \

nievergeltlab

nsub=30000
nr=nsub
ncol=nsub

dummy_datamatrix <- data.frame(pc1=rnorm(nsub), pc2=rnorm(nsub),studytype=round(runif(nsub)))

distmat <- matrix(rnorm(nsub^2),ncol=nsub,nrow=nsub)


library(vegan)

nievergeltlab

args <- commandArgs(TRUE)
parmfile <- args[1]
linestart <- args[2]
linestop <- args[3]
run_no <- args[4]

library(lmtest)


nrep=100 #Number of simulation repetitions per parameter set

nievergeltlab

for chr in {1..22} 
do
for pwd in    $(cat  /mnt/sdb/genetics/hrv_2/umichpwds.txt) #supply a file with umich password, i can never keep track of these
do
 echo $pwd
 unzip -P $pwd chr_"$chr".zip
done
#rm chr_"$chr".zip
done

nievergeltlab

args = commandArgs(trailingOnly=TRUE)
#Rscript p2_phenocode.r mrsc > test.txt

study <- args[1] 

#study='mrsc'

library(psych)
dat <- read.csv(paste(study,'_p2.csv',sep=''),stringsAsFactors=F,header=T,na.strings=c(NA,"#N/A","-9"))

nievergeltlab

# Will Rayner provides a great toolbox to prepare data: HRC or 1000G Pre-imputation Checks.

# The main steps for HRC are:
# Download tool and sites

#wget http://www.well.ox.ac.uk/~wrayner/tools/HRC-1000G-check-bim-v4.2.7.zip
#wget ftp://ngs.sanger.ac.uk/production/hrc/HRC.r1-1/HRC.r1-1.GRCh37.wgs.mac5.sites.tab.gz

# Convert ped/map to bed

nievergeltlab

##Part 1: If final report is not split into 1 subject per file, this will split it up. Otherwise don't run this part!

#User: List name of final report file here
final_report=Akesogen_Cohen_FC_CNV_V.1_FinalReport.txt

#Creates a header
head -n10 $final_report > header.txt

#Column $1 is the default here, but the number should correspond to whatever number the subjects are named in
awk  'NR>10{print >$1}' $final_report