Commit 9a615985 authored by Mattia Forneris's avatar Mattia Forneris

Updated comments

parent 8e47d2ad
......@@ -12,3 +12,6 @@ log/
analysis/
data/
*nohup*
*example*
*DS_Store*
*gitignore*
......@@ -367,6 +367,8 @@ summary {
<div id="gatk-joint-genotype-call-pipeline" class="section level1">
<h1>GATK Joint Genotype Call pipeline</h1>
<p><br /></p>
<p>Practice](<a href="https://software.broadinstitute.org/gatk/best-practices/workflow?id=11145" class="uri">https://software.broadinstitute.org/gatk/best-practices/workflow?id=11145</a>) in four snakemake pipelines. The workflow goes from fastq mapping and variant call to quality control and filtering.</p>
<p><br /><br /></p>
<div id="global-folder-structure-of-the-project" class="section level2">
<h2>Global folder structure of the project</h2>
......@@ -416,7 +418,7 @@ summary {
<tr class="header">
<th align="left">Software</th>
<th align="left">version</th>
<th align="center">conda install command</th>
<th align="center">Install command</th>
</tr>
</thead>
<tbody>
......
# GATK Joint Genotype Call pipeline
<br />
Practice](https://software.broadinstitute.org/gatk/best-practices/workflow?id=11145) in four snakemake pipelines. The workflow goes from fastq mapping and variant call to quality control and filtering.
<br /><br />
## Global folder structure of the project
......@@ -42,7 +48,7 @@ Here is a list a pre-defined directories and what they are made for:
Theis software is necessary to run the pipeline. In general, you need an [Anaconda](https://www.anaconda.com/) installation. All the software listed here can be installed trhough Anaconda. <br />
The table below lists the software necessary to run the pipeline, the version used in this pipeline and the conda command to install the software (simply run in a shell to install). If you want to install a specific version of the softare use '=='. E.g. to install version 0.11.5 of fastqc run 'conda install -c bioconda fastqc==0.11.5'
| Software | version | conda install command |
| Software | version | Install command |
|:---|:---|:---:|
| fastqc | 0.11.5 | conda install -c bioconda fastqc |
| multiqc | 1.6 | conda install -c bioconda multiqc |
......
## setup ##
library(yaml)
library(argparse)
library(ggplot2)
options(stringsAsFactors = FALSE)
CONF = yaml.load_file("../../config/config.yml")
PROJECTROOT = CONF$global$projectpath
make_recursive_dir = function(new_dir){
if(!dir.exists(new_dir)){ dir.create(new_dir, recursive = TRUE) }
}
## parse comand line arguments ##
parser <- ArgumentParser()
parser$add_argument("-k", default = 3, type="integer", help = "Number of clusters to find [default: %(default)s]")
parser$add_argument("-n", "--nstart", default = 20, type="integer", help = "How many random sets should be chosen? ('?kmeans' for more details) [default: %(default)s]")
parser$add_argument("-o", "--output", default = "plot.pdf", type = "character", help = "Name of the output file [default: %(default)s]")
parser$add_argument("--seed_number", type = "integer", help = "Set number to get reproducible results")
args = parser$parse_args()
k = args$k
n = args$n
file_name = args$output
## run analysis ##
data(iris)
if(exists("seed_number")) {set.seed(seed_number)}
irisCluster = kmeans(iris[, 3:4], centers = k, nstart = n)
iris$cluster <- as.factor(irisCluster$cluster)
p = ggplot(iris, aes(Petal.Length, Petal.Width, color = cluster)) + geom_point() + theme_bw()
# write output
make_recursive_dir("tmp/kmeans")
file_path = file.path("tmp/kmeans", file_name)
ggsave(file_path, p, device = "pdf")
print(paste0("Output plot saved to ", file_path))
......@@ -11,7 +11,7 @@
<meta name="date" content="2019-09-06" />
<meta name="date" content="2019-09-11" />
<title>GATK quality control</title>
......@@ -1944,7 +1944,7 @@ summary {
<h1 class="title toc-ignore">GATK quality control</h1>
<h4 class="date">6 September 2019</h4>
<h4 class="date">11 September 2019</h4>
</div>
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