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Merge branch 'tumor-gene-target-screener-winfix'
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u8sand committed Oct 20, 2023
2 parents 864e38f + c4b0210 commit 8572f20
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Showing 3 changed files with 8 additions and 5 deletions.
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"outputs": [],
"source": [
"%%appyter code_exec\n",
"import os\n",
"import qnorm\n",
"import numpy as np\n",
"import pandas as pd\n",
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"import sys\n",
"import contextlib\n",
"@contextlib.contextmanager\n",
"def suppress_output(stdout=True, stderr=True, dest='/dev/null'):\n",
"def suppress_output(stdout=True, stderr=True, dest=os.devnull):\n",
" ''' Usage:\n",
" with suppress_output():\n",
" print('hi')\n",
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"%%appyter code_eval\n",
"\n",
"with suppress_output():\n",
" df_bg_expr_norm.columns = df_bg_expr_norm.columns.to_flat_index().map(lambda s: ', '.join(s))\n",
" dge = limma_voom_differential_expression(\n",
" df_bg_expr_norm, df_expr_norm,\n",
" voom_design=True,\n",
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"outputs": [],
"source": [
"%%appyter code_exec\n",
"dge_final = dge[dge.score >= 1].sort_values(['score', 't'], ascending=False).iloc[:16]\n",
"dge_final = dge[dge.score >= 1].sort_values(['score', '-log(adj.P.Val)'], ascending=False).iloc[:16]\n",
"pd.set_option('display.max_colwidth', None)\n",
"dge_final['Link'] = dge_final['gene_symbol'].map(lambda g: f\"<a href=\\\"https://cfde-gene-pages.cloud/gene/{g}\\\">{g}</a>\")\n",
"{% if membrane_screener %}\n",
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2 changes: 1 addition & 1 deletion appyters/Tumor_Gene_Target_Screener/appyter.json
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"$schema": "https://raw.githubusercontent.com/MaayanLab/appyter-catalog/main/schema/appyter-validator.json",
"name": "Tumor_Gene_Target_Screener",
"title": "Tumor Gene Target Screener",
"version": "0.3.7",
"version": "0.3.9",
"description": "An appyter for gene target screening of tumors",
"authors": [
{
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5 changes: 3 additions & 2 deletions appyters/Tumor_Gene_Target_Screener/requirements.txt
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@@ -1,10 +1,11 @@
appyter
maayanlab-bioinformatics @ git+https://github.com/Maayanlab/maayanlab-bioinformatics@v0.5.5
maayanlab-bioinformatics @ git+https://github.com/Maayanlab/maayanlab-bioinformatics@v0.5.9
matplotlib
matplotlib_venn
numpy
pandas
pandas==2.0
plotly
qnorm
rpy2
s3fs
scikit-learn

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