ohofmann

Individual filtered VCFs vs filtered standard (variants present in 5/9) samples, `rtg vcfeval -b Shared.vcf.gz -c foo.vcf.gz -t /cm/shared/apps/bcbio/20150720-devel/data/genomes/Hsapiens/GRCh37/rtg/GRCh37.sdf -o eval`:

File  					True-pos  False-pos  False-neg  Precision  Sensitivity  F-measure
filtered.Fresh_4706			3338450      54119     339450     0.9840       0.9077     0.9443
filtered.Fresh_4707			3409349     154908     268551     0.9565       0.9270     0.9415
filtered.Fresh_Normal_4708		3464245     190615     213655     0.9478       0.9419     0.9449
filtered.UMFIX_4085			3134462     145638     543438     0.9556       0.8522     0.9010
filtered.UMFIX_4090			3101645     105139     576255     0.9672       0.8433     0.9010
filtered.UMFIX_Normal_4088		3268466     157860     409434     0.9539       0.8887     0.9201
filtered.NBF_4087			3217841     109510     460059     0.9671       0.8749     0.9187

ohofmann

Running three different comparisons (see `scripts/rtg*`) using the _filtered_ SNP data this time around. First, the gold standard (5/9 samples) against each filtered call set, `rtg vcfeval -b Shared.vcf.gz -c foo.vcf.gz -t /cm/shared/apps/bcbio/20150720-devel/data/genomes/Hsapiens/GRCh37/rtg/GRCh37.sdf -o eval`:


File  					True-pos  False-pos  False-neg  Precision  Sensitivity  F-measure
filtered.Fresh_4706			3338450      54119     339450     0.9840       0.9077     0.9443
filtered.Fresh_4707			3409349     154908     268551     0.9565       0.9270     0.9415
filtered.Fresh_Normal_4708		3464245     190615     213655     0.9478       0.9419     0.9449
filtered.UMFIX_4085			3134462     145638     543438     0.9556       0.8522     0.9010
filtered.UMFIX_4090			3101645     105139     576255     0.9672       0.8433     0.9010
filtered.UMFIX_Normal_4088		3268466     157860     409434     0.9539       0.8887     0.9201

ohofmann

Running three different comparisons (see `scripts/rtg*`) using the _filtered_ SNP data this time around. First, the gold standard (5/9 samples) against each filtered call set, `rtg vcfeval -b Shared.vcf.gz -c foo.vcf.gz -t /cm/shared/apps/bcbio/20150720-devel/data/genomes/Hsapiens/GRCh37/rtg/GRCh37.sdf -o eval`:


File  							True-pos  False-pos  False-neg  Precision  Sensitivity  F-measure
filtered.Fresh_4706				3338450      54119      339450     0.9840       0.9077     0.9443
filtered.Fresh_4707				3409349     154908     268551     0.9565       0.9270     0.9415
filtered.Fresh_Normal_4708		3464245     190615     213655     0.9478       0.9419     0.9449
filtered.UMFIX_4085				3134462     145638     543438     0.9556       0.8522     0.9010
filtered.UMFIX_4090				3101645     105139     576255     0.9672       0.8433     0.9010
filtered.UMFIX_Normal_4088		3268466     157860     409434     0.9539       0.8887     0.9201

ohofmann

Running three different comparisons (see `scripts/rtg*`) using the _filtered_ SNP data this time around. First, the gold standard (5/9 samples) against each filtered call set, `rtg vcfeval -b Shared.vcf.gz -c foo.vcf.gz -t /cm/shared/apps/bcbio/20150720-devel/data/genomes/Hsapiens/GRCh37/rtg/GRCh37.sdf -o eval`:


File  							        True-pos  False-pos  False-neg  Precision  Sensitivity  F-measure
filtered.Fresh_4706				  3338450      54119     339450     0.9840       0.9077     0.9443
filtered.Fresh_4707				  3409349     154908     268551     0.9565       0.9270     0.9415
filtered.Fresh_Normal_4708	3464245     190615     213655     0.9478       0.9419     0.9449
filtered.UMFIX_4085				  3134462     145638     543438     0.9556       0.8522     0.9010
filtered.UMFIX_4090				  3101645     105139     576255     0.9672       0.8433     0.9010
filtered.UMFIX_Normal_4088	3268466     157860     409434     0.9539       0.8887     0.9201

ohofmann

```{r custom}
library(VariantAnnotation)
library(ggplot2)
library(pheatmap)
library(scales)
library(gridExtra)
library(gtools)
library(RColorBrewer)
library(knitr)
library(tidyr)

ohofmann

#!/bin/sh
#SBATCH -p defq
#SBATCH -J bcb-prep-e[1-6]
#SBATCH -o bcbio-ipengine.out.%%j
#SBATCH -e bcbio-ipengine.err.%%j
#SBATCH --cpus-per-task=5
#SBATCH --array=1-6
#SBATCH -t 05-00:00:00

ohofmann

# Install collectd if needed. For Debian-based systems:

$ sudo apt-get update
$ sudo apt-get -y install collectd

# Open collectd.conf for editing

$ sudo vi /etc/collectd/collectd.conf

Uncomment the line containing "LoadPlugin write_http"

ohofmann

# Create a vector of sex-specific positive control genes
sex.symbols <- c("XIST","CYorf15A","DDX3Y","KDM5D","RPS4Y1","USP9Y","UTY");
sex.genes <- featureNames(dataset)[match(sex.symbols, fData(dataset)$Symbol)];
names(sex.genes) <- sex.symbols;
sex.genes <- na.omit(sex.genes);

# Draw stripcharts of expression of each sex-specific gene
pdf.filename <- file.path(project.path, sprintf("%s_sex_stripcharts.pdf", project.prefix));
if (!file.exists(pdf.filename)) {
	pdf(pdf.filename, width=11, height=8.5);

ohofmann

library(getopt)

options <- c('help', 'h', 0, 'logical',
             'reads', 'r', 1, 'character',
             'targets', 't', 1, 'character')

opt = getopt(matrix(options, ncol=4, byrow=T))

# Help was asked for.
if (!is.null(opt$help)) {

ohofmann

KNITR SETUP

opts_chunk$set(tidy=TRUE, cache=FALSE, highlight=TRUE, figalign="center", warning=FALSE,  error=FALSE, message=FALSE, fig.height=11, fig.width=11)

EXAMPLE CHUNK

library(ggplot2)
library(xtable)