Commit 1d9cea78 authored by Hanna  JULIENNE's avatar Hanna JULIENNE
Browse files

added a few explanatory texts

parent ffd27c10
......@@ -38,7 +38,7 @@
Jass is developed at Institut Pasteur by the <a href="https://research.pasteur.fr/en/team/statistical-genetics/">Statistical Genetics group</a> and the <a href="https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/">Biostatistic and Bioinformatic HUB</a>
<p>
<a href="https://research.pasteur.fr/en/team/statistical-genetics/"> The Statistical Genetics group</a>, lead by Hugues Aschard, is interested in developping new methods to analyze GWAS data. In particular, in JASS, we leverage publicly available GWAS results to discover new signals and understand pleiotropy. While our team focus mainly on developping the theory behind the tools, we also aim to make our software widely available and to follow high quality software development standards. Hence, our close collaboration with the hub and more specifically with its WINTER group dedicated to web developement. <a href="https://research.pasteur.fr/en/team/hub-winter/">The WINTER group</a> is a software development team focusing mainly on Web technologies for publishing and sharing scientific tools, analysis, data and workflows. We work hand in hand to develop JASS website.
<a href="https://research.pasteur.fr/en/team/statistical-genetics/"> The Statistical Genetics unit</a>, lead by Hugues Aschard, is interested in developping new methods to analyze GWAS data. In particular, in JASS, we leverage publicly available GWAS results to discover new signals and understand pleiotropy. While our team focus mainly on developping the theory behind the tools, we also aim to make our software widely available and to follow high quality software development standards. Hence, our close collaboration with the hub and more specifically with its WINTER group dedicated to web developement. <a href="https://research.pasteur.fr/en/team/hub-winter/">The WINTER group</a> is a software development team focusing mainly on Web technologies for publishing and sharing scientific tools, analysis, data and workflows. We work hand in hand to develop JASS website.
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</v-row>
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......@@ -7,7 +7,7 @@
Please select the traits you want to analyze jointly with the Omnibus test. A research box bellow allows you to query our current database. The maximum number of trait that can be analyzed jointly is 64 traits. Computation time for a large number of trait can take up to half an hour.
</p>
</div>
<h2>Select the GWAS to be analyzed jointly:</h2>
<h2>Select at least two GWASs to be analyzed jointly:</h2>
<div id="app">
<v-app>
<v-data-table
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......@@ -10,7 +10,8 @@
class="text-subtitle-1 text-center"
cols="12"
>
<h2>Creating the worktable ! It might take a few minutes</h2>
<h2>Creating the worktable ! It might take a few minutes </h2>
<p> you might need to refresh the page to see jobs' progression </p>
</v-col>
<v-col
class="text-subtitle-1 text-center"
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......@@ -262,9 +262,7 @@ def create_quadrant_plot(work_file_path: str,
def create_qq_plot(work_file_path: str, qq_plot_path: str):
df = read_hdf(work_file_path, "SumStatTab")
pval_median = df.JASS_PVAL.median()
print("median pval")
print(pval_median)
df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']] = replaceZeroes(
df[['JASS_PVAL', 'UNIVARIATE_MIN_PVAL']])
......@@ -274,6 +272,8 @@ def create_qq_plot(work_file_path: str, qq_plot_path: str):
y = pvalue.sort_values()
plt.scatter(x[::-1], y, s=5)
pval_median = df.JASS_PVAL.median()
print("median pval")
print(pval_median)
lambda_value = pval_median / 0.5
x_1 = np.linspace(0, 6)
y_1 = lambda_value * x_1
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