cth

#!/home/fng514/bin/julia
#$ -S /home/fng514/bin/julia
#$ -cwd 

# Christian Theil Have, 2016.

using HypothesisTests
using StatsBase

nucleotides = Set{AbstractString}(["A","G","C","T"])

cth

#!/home/fng514/bin/julia
#$ -S /home/fng514/bin/julia
#$ -cwd 

# Christian Theil Have, 2016.

#import GZip 

function process_vcf_line(outfile,line, dpmin, gqmin)
	fields = split(line)

cth

#!/home/fng514/bin/julia
#$ -S /home/fng514/bin/julia
#$ -cwd 
#
# Split multi allelic sites:
# 1. Split multi-allelic sites into multiple VCF lines, s.t., a) each line has one unique alternative allele, and b) if person has an other alternative allele than the one specified for the line in question, then that particular genotype is coded as missing in that line.
# 2. Run bi-allelic hardy weinberg test for each of these lines.
# 
# Christian Theil Have, 2016.

cth

#!/home/fng514/bin/julia
#$ -S /home/fng514/bin/julia
#$ -cwd 
#
# Split multi allelic sites:
# 1. Split multi-allelic sites into multiple VCF lines, s.t., a) each line has one unique alternative allele, and b) if person has an other alternative allele than the one specified for the line in question, then that particular genotype is coded as missing in that line.
# 2. Run bi-allelic hardy weinberg test for each of these lines.
# 
# Christian Theil Have, 2016.

cth

#!/home/fng514/bin/julia
#$ -S /home/fng514/bin/julia
#$ -cwd 
#$ -pe smp 20

# Julia script to read a VCF file and calculate tstv by stepwise GQ and DP tresholds
# Christian Theil Have, 2016.

import GZip 

cth

#!/home/fng514/bin/julia
#$ -S /home/fng514/bin/julia
#$ -cwd 
#$ -l pe smp 20
# Julia script to to read VCF file and calculate quality and depth statistics (in parallel)
# Christian Theil Have, 2016.

import GZip 

println(string("Reading ",ARGS[1]))

cth

#!/home/fng514/bin/ruby
#$ -S /home/fng514/bin/ruby
#$ -cwd

# Script to calculate INFO (approx R-squared) and Allele frequency from a VCF file (with dosages).
# Like the ones produced with https://github.com/cth/vcf-misc-tools
# Christian Theil Have, 2016.
# Use as you see fit, but no warranties.
# Usage: 
# qsub yuvaraj_info.rb my.vcf my.info

cth

strandflip.R version 0.2
By Christian Theil Have
with inspiration from SAS script by Aldi Kraja

DISCLAIMER: NO WARRANTIES WHATSOEVER ARE PROVIDED WITH THIS
PROGRAM. YOU RUN IT AT YOUR OWN RISK.
YOU CAN DISTRIBUTE this utility pgm in other collaborations

purpose: flip dosage if the alleles in original data are
different when compared to 1000G set of nucmtDNA

cth

## Very simple power calculation
## assuming a true odds ratio of 3.1

pick_eater_obese <- function(n) table(runif(n) < 109/(330+109))
pick_eater_control <- function(n) table(runif(n) < 37/(37+38))
p.values <- replicate(1000, fisher.test( matrix(c(pick_eater_obese(50),pick_eater_control(50)),2) )$p )
significance <- c(5e-1, 5e-2, 5e-3, 5e-4, 5e-5)
powers <- data.frame(significance, power = sapply(significance,function(x) mean(p.values < x)))

cth

## Very simple power calculation
## assuming a true odds ratio of 3.1

pick_eater_obese <- function(n) table(runif(n) < 109/(330+109))
pick_eater_control <- function(n) table(runif(n) < 37/(37+38))

#controls <- data.frame(pheno = rep(1000,"case"), runif(

p.values <- replicate(1000, fisher.test( matrix(c(pick_eater_obese(50),pick_eater_control(50)),2) )$p )
significance <- c(5e-1, 5e-2, 5e-3, 5e-4, 5e-5)