{"id":27720,"date":"2024-02-02T16:21:44","date_gmt":"2024-02-02T08:21:44","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27720"},"modified":"2024-02-02T16:21:45","modified_gmt":"2024-02-02T08:21:45","slug":"gretta%ef%bc%9a%e6%8c%96%e6%8e%98%e5%9f%ba%e5%9b%a0%e7%9b%b8%e4%ba%92%e4%bd%9c%e7%94%a8%e5%92%8c%e5%8d%8f%e5%90%8c%e5%bf%85%e9%9c%80%e7%bd%91%e7%bb%9c%e7%9a%84%e5%a5%bd%e5%8c%85%ef%bc%81","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27720","title":{"rendered":"GRETTA\uff1a\u6316\u6398\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528\u548c\u534f\u540c\u5fc5\u9700\u7f51\u7edc\u7684\u597d\u5305\uff01"},"content":{"rendered":"<p>\u4eca\u5929\u5c0f\u82b1\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e2a<strong>2023\u5e746\u67081\u65e5<\/strong>\u53d1\u8868\u4e8e<em>bioinformatics<\/em>\u4e0a\u65b0\u9c9c\u51fa\u7089\u7684\u597d\u5305\u2014\u2014GRETTA\uff01<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" width=\"640\" height=\"182\" class=\"wp-image-27721\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig.png?resize=640%2C182\" alt=\"fig:\" srcset=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig.png?w=1872 1872w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig.png?resize=300%2C85 300w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig.png?resize=1024%2C291 1024w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig.png?resize=768%2C218 768w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig.png?resize=1536%2C437 1536w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig.png?resize=600%2C171 600w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig.png?w=1280 1280w\" sizes=\"(max-width: 640px) 100vw, 640px\" data-recalc-dims=\"1\" \/><\/p>\n<p>\u5728\u751f\u7269\u4fe1\u606f\u5b66\u9886\u57df\uff0c\u7814\u7a76\u57fa\u56e0\u4e0e\u57fa\u56e0\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\u4ee5\u53ca\u57fa\u56e0\u7684\u529f\u80fd\u81f3\u5173\u91cd\u8981\u3002\u7136\u800c\uff0c\u901a\u8fc7\u4f53\u5916\u548c\u4f53\u5185\u5b9e\u9a8c\u6765\u63ed\u793a\u8fd9\u4e9b\u7f51\u7edc\u5f80\u5f80\u9700\u8981\u5927\u91cf\u7684\u8d44\u6e90\uff0c\u9650\u5236\u4e86\u6837\u672c\u5206\u6790\u7684\u901a\u91cf\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0cYuka Takemon\u548cMarco A Marra\u5f00\u53d1\u4e86\u4e00\u4e2a\u540d\u4e3aGRETTA\u7684R\u5305\uff0c\u4f7f\u5f97\u5229\u7528\u516c\u5f00\u6570\u636e\u8fdb\u884c\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528\u548c\u534f\u540c\u5fc5\u9700\u7f51\u7edc\u5206\u6790\u53d8\u5f97\u66f4\u52a0\u7b80\u5355\u6613\u884c\u3002\u73b0\u5728\uff0c\u5c0f\u82b1\u5c06\u4e3a\u5927\u5bb6\u4ecb\u7ecd\u8fd9\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\u3002<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" width=\"640\" height=\"295\" class=\"wp-image-27722\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-1.png?resize=640%2C295\" alt=\"fig:\" srcset=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-1.png?w=1192 1192w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-1.png?resize=300%2C138 300w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-1.png?resize=1024%2C472 1024w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-1.png?resize=768%2C354 768w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-1.png?resize=600%2C277 600w\" sizes=\"(max-width: 640px) 100vw, 640px\" data-recalc-dims=\"1\" \/><\/p>\n<p><a href=\"#post-27720-gretta\u6316\u6398\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528\u548c\u534f\u540c\u5fc5\u9700\u7f51\u7edc\u7684\u597d\u5305\">GRETTA\uff1a\u6316\u6398\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528\u548c\u534f\u540c\u5fc5\u9700\u7f51\u7edc\u7684\u597d\u5305\uff01<\/a><br \/>\n<a href=\"#post-27720-\u5b89\u88c5\">\u5b89\u88c5<\/a><br \/>\n<a href=\"#post-27720-\u5de5\u4f5c\u6d41\u7a0b\">\u5de5\u4f5c\u6d41\u7a0b<\/a><br \/>\n<a href=\"#post-27720-\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528\">\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528<\/a><br \/>\n<a href=\"#post-27720-\u534f\u540c\u5fc5\u9700co-essential\uff09\u7f51\u7edc\">\u534f\u540c\u5fc5\u9700\uff08co-essential\uff09\u7f51\u7edc<\/a><br \/>\n<a href=\"#post-27720-\u4e00\u4e2a\u4f8b\u5b50\u9274\u5b9aarid1a\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528\">\u4e00\u4e2a\u4f8b\u5b50\uff1a\u9274\u5b9a<em>ARID1A<\/em>\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528<\/a><br \/>\n<a href=\"#post-27720-\u4e0b\u8f7d\u793a\u4f8b\u6570\u636e\">\u4e0b\u8f7d\u793a\u4f8b\u6570\u636e<\/a><br \/>\n<a href=\"#post-27720-\u63a2\u7d22\u7ec6\u80de\u7cfb\">\u63a2\u7d22\u7ec6\u80de\u7cfb<\/a><br \/>\n<a href=\"#post-27720-\u9009\u62e9\u7a81\u53d8\u7ec6\u80de\u7cfb\u548c\u5bf9\u7167\u7ec6\u80de\u7cfb\u7ec4\">\u9009\u62e9\u7a81\u53d8\u7ec6\u80de\u7cfb\u548c\u5bf9\u7167\u7ec6\u80de\u7cfb\u7ec4<\/a><br \/>\n<a href=\"#post-27720-\u53ef\u9009\u7684\u7ec6\u80de\u7cfb\u8fc7\u6ee4\u5668\">\u53ef\u9009\u7684\u7ec6\u80de\u7cfb\u8fc7\u6ee4\u5668<\/a><br \/>\n<a href=\"#post-27720-\u68c0\u6d4b\u5dee\u5f02\u8868\u8fbe\">\u68c0\u6d4b\u5dee\u5f02\u8868\u8fbe<\/a><br \/>\n<a href=\"#post-27720-\u8fdb\u884c\u5168\u57fa\u56e0\u7ec4in-silico\u9057\u4f20\u7b5b\u9009\">\u8fdb\u884c\u5168\u57fa\u56e0\u7ec4<em>in silico<\/em>\u9057\u4f20\u7b5b\u9009<\/a><br \/>\n<a href=\"#post-27720-\u53ef\u9009\u6b65\u9aa4\u8fdb\u884c\u5c0f\u89c4\u6a21\u7b5b\u9009\">\u53ef\u9009\u6b65\u9aa4\uff1a\u8fdb\u884c\u5c0f\u89c4\u6a21\u7b5b\u9009<\/a><br \/>\n<a href=\"#post-27720-\u53ef\u89c6\u5316\u7b5b\u9009\u7ed3\u679c\">\u53ef\u89c6\u5316\u7b5b\u9009\u7ed3\u679c<\/a><br \/>\n<a href=\"#post-27720-\u793a\u4f8b2\u9274\u5b9aarid1a\u534f\u540c\u5fc5\u9700co-essential\uff09\u57fa\u56e0\">\u793a\u4f8b2\uff1a\u9274\u5b9a<em>ARID1A<\/em>\u534f\u540c\u5fc5\u9700\uff08co-essential\uff09\u57fa\u56e0<\/a><br \/>\n<a href=\"#post-27720-\u9274\u5b9a\u5177\u6709\u6700\u9ad8\u76f8\u5173\u6027\u7cfb\u6570\u7684\u57fa\u56e0\">\u9274\u5b9a\u5177\u6709\u6700\u9ad8\u76f8\u5173\u6027\u7cfb\u6570\u7684\u57fa\u56e0<\/a><br \/>\n<a href=\"#post-27720-\u7279\u5b9a\u764c\u75c7\u7c7b\u578b\u7684\u53ef\u9009\u8fc7\u6ee4\u5668\">\u7279\u5b9a\u764c\u75c7\u7c7b\u578b\u7684\u53ef\u9009\u8fc7\u6ee4\u5668<\/a><br \/>\n<a href=\"#post-27720-\u9488\u5bf9\u81ea\u5b9a\u4e49\u7ec6\u80de\u7cfb\u7684\u53ef\u9009\u8fc7\u6ee4\u5668\">\u9488\u5bf9\u81ea\u5b9a\u4e49\u7ec6\u80de\u7cfb\u7684\u53ef\u9009\u8fc7\u6ee4\u5668<\/a><br \/>\n<a href=\"#post-27720-\u7ed3\u8bed\">\u7ed3\u8bed<\/a><br \/>\n<a href=\"#post-27720-\u53c2\u8003\u6587\u732e\">\u53c2\u8003\u6587\u732e<\/a><\/p>\n<h2>\u5b89\u88c5<\/h2>\n<p>install.packages(c(&#8220;devtools&#8221;, &#8220;dplyr&#8221;,&#8221;forcats&#8221;,&#8221;ggplot2&#8243;))<br \/>\ndevtools::install_github(&#8220;ytakemon\/GRETTA&#8221;)<\/p>\n<h2>\u5de5\u4f5c\u6d41\u7a0b<\/h2>\n<h3>\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528<\/h3>\n<ol>\n<li>\u5b89\u88c5\u201cGRETTA\u201d\u5e76\u4e0b\u8f7d\u9644\u5e26\u7684\u6570\u636e\u3002<\/li>\n<li>\u9009\u62e9\u57fa\u56e0\u611f\u5174\u8da3\u7684\u7a81\u53d8\u7ec6\u80de\u7cfb\u548c\u5bf9\u7167\u7ec6\u80de\u7cfb\u3002\n<ul>\n<li>\u53ef\u4ee5\u7528\u4e8e\u6839\u636e\u75be\u75c5\u7c7b\u578b\uff0c\u75be\u75c5\u4e9a\u578b\u6216\u6c28\u57fa\u9178\u53d8\u5316\u9009\u62e9\u7ec6\u80de\u7cfb\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\u786e\u5b9a\u7a81\u53d8\u7ec6\u80de\u7cfb\u548c\u5bf9\u7167\u7ec6\u80de\u7cfb\u7ec4\u4e4b\u95f4\u7684\u5dee\u5f02\u8868\u8fbe\u3002\n<ul>\n<li>\uff08\u53ef\u9009\u4f46\u63a8\u8350\uff09\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\u8fdb\u884c<em>in silico<\/em>\u57fa\u56e0\u7b5b\u67e5\u3002<\/li>\n<li>\u53ef\u89c6\u5316\u7ed3\u679c\u3002<\/li>\n<\/ol>\n<h3>\u534f\u540c\u5fc5\u9700\uff08co-essential\uff09\u7f51\u7edc<\/h3>\n<ol>\n<li>\u5b89\u88c5\u201cGRETTA\u201d\u5e76\u4e0b\u8f7d\u9644\u5e26\u7684\u8d44\u6599\u3002<\/li>\n<li>\u8fd0\u884c\u76f8\u5173\u6027\u7cfb\u6570\u5206\u6790\u3002\n<ul>\n<li>\u53ef\u4ee5\u7528\u4e8e\u5728\u7279\u5b9a\u75be\u75c5\u7c7b\u578b\u7684\u7ec6\u80de\u7cfb\u4e2d\u8fdb\u884c\u7684\u5206\u6790\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\u8ba1\u7b97\u8d1f\/\u6b63\u66f2\u7ebf\u7684\u62d0\u70b9\u4ee5\u786e\u5b9a\u9608\u503c\u3002<\/li>\n<li>\u5e94\u7528\u9608\u503c\u3002<\/li>\n<li>\u53ef\u89c6\u5316\u7ed3\u679c\u3002<\/li>\n<\/ol>\n<h2>\u4e00\u4e2a\u4f8b\u5b50\uff1a\u9274\u5b9a<em>ARID1A<\/em>\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528<\/h2>\n<p><em>ARID1A<\/em>\u7f16\u7801\u67d3\u8272\u8d28\u91cd\u5851SWItch\/\u975e\u53d1\u9175\u7cd6\uff08SWI\/SNF\uff09\u590d\u5408\u7269\u7684\u4e00\u4e2a\u6210\u5458\uff0c\u5e76\u4e14\u662f\u764c\u75c7\u4e2d\u9891\u7e41\u53d1\u751f\u7a81\u53d8\u7684\u57fa\u56e0\u3002\u5df2\u77e5<em>ARID1A<\/em>\u53ca\u5176\u540c\u6e90\u7269<em>ARID1B<\/em>\u662f\u5f7c\u6b64\u7684\u5408\u6210\u81f4\u6b7b\u57fa\u56e0\uff1aARID1A\u548c\u5176\u540c\u6e90\u7269ARID1B\u5728\u7ec6\u80de\u4e2d\u7684\u53cc\u91cd\u4e27\u5931\u662f\u81f4\u6b7b\u7684\uff1b\u7136\u800c\uff0c\u5355\u72ec\u4e27\u5931\u4efb\u4f55\u4e00\u4e2a\u57fa\u56e0\u90fd\u4e0d\u662f\u81f4\u6b7b\u7684\uff08<a href=\"https:\/\/doi.org\/10.1038\/nm.3480\">Helming\u7b49\uff0c2014<\/a>\uff09\u3002\u8fd9\u4e2a\u4f8b\u5b50\u5c06\u5c55\u793a\u5982\u4f55\u4f7f\u7528GRETTA\u9274\u5b9a<em>ARID1A<\/em>\u7684\u5408\u6210\u81f4\u6b7b\u4e92\u4f5c\u7269\u5e76\u9884\u6d4b\u8fd9\u4e00\u5df2\u77e5\u7684\u76f8\u4e92\u4f5c\u7528\u3002<br \/>\n\u5728\u672c\u4f8b\u4e2d\uff0c\u4f60\u9700\u8981\u8c03\u7528\u4ee5\u4e0b\u5e93\u3002\u5982\u679c\u8fd9\u4e9b\u5e93\u5c1a\u672a\u5b89\u88c5\uff0c\u8bf7\u4f7f\u7528install.packages()\uff08\u4f8b\u5982install.packages(&#8220;dplyr&#8221;)\uff09\u3002<\/p>\n<p># Load library<br \/>\nlibrary(tidyverse)<br \/>\n#&gt; \u2500\u2500 Attaching packages \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 tidyverse 1.3.2 \u2500\u2500<br \/>\n#&gt; \u2714 ggplot2 3.4.1 \u2714 purrr 1.0.1<br \/>\n#&gt; \u2714 tibble 3.2.1 \u2714 dplyr 1.1.1<br \/>\n#&gt; \u2714 tidyr 1.3.0 \u2714 stringr 1.5.0<br \/>\n#&gt; \u2714 readr 2.1.4 \u2714 forcats 1.0.0<br \/>\n#&gt; \u2500\u2500 Conflicts \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 tidyverse_conflicts() \u2500\u2500<br \/>\n#&gt; \u2716 dplyr::filter() masks stats::filter()<br \/>\n#&gt; \u2716 dplyr::lag() masks stats::lag()<br \/>\nlibrary(GRETTA)<br \/>\n#&gt;<br \/>\n#&gt; _______ .______ _______ .___________.___________. ___<br \/>\n#&gt; \/ _____|| _ \\ | ____|| | | \/ \\<br \/>\n#&gt; | | __ | |_) | | |__ `&#8212;| |&#8212;-`&#8212;| |&#8212;-` \/ ^ \\<br \/>\n#&gt; | | |_ | | \/ | __| | | | | \/ \/_\\ \\<br \/>\n#&gt; | |__| | | |\\ \\&#8212;-.| |____ | | | | \/ _____ \\<br \/>\n#&gt; \\______| | _| `._____||_______| |__| |__| \/__\/ \\__\\<br \/>\n#&gt;<br \/>\n#&gt; Welcome to GRETTA! The version loaded is: 0.99.2<br \/>\n#&gt; The latest DepMap dataset accompanying this package is v22Q2.<br \/>\n#&gt; Please refer to our tutorial on GitHub for loading DepMap data and details: https:\/\/github.com\/ytakemon\/GRETTA<\/p>\n<h3>\u4e0b\u8f7d\u793a\u4f8b\u6570\u636e<\/h3>\n<p>\u4e3a\u672c\u6559\u7a0b\u521b\u5efa\u4e86\u4e00\u4e2a\u5c0f\u578b\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u4e0b\u8f7d\u3002<\/p>\n<p>path &lt;- getwd()<br \/>\ndownload_example_data(path)<br \/>\n#&gt; Data saved to: \/projects\/marralab\/ytakemon_prj\/DepMap\/GRETTA\/GRETTA_example\/<\/p>\n<p>\u7136\u540e\uff0c\u5206\u914d\u53d8\u91cf\u6307\u5411\u5b58\u50a8 .rda \u6587\u4ef6\u7684\u4f4d\u7f6e\u548c\u7ed3\u679c\u6587\u4ef6\u5e94\u8be5\u5b58\u50a8\u7684\u4f4d\u7f6e\u3002<\/p>\n<p>gretta_data_dir &lt;- paste0(path,&#8221;\/GRETTA_example\/&#8221;)<br \/>\ngretta_output_dir &lt;- paste0(path,&#8221;\/GRETTA_example_output\/&#8221;)<\/p>\n<h3>\u63a2\u7d22\u7ec6\u80de\u7cfb<\/h3>\n<p>\u53ef\u4ee5\u901a\u8fc7DepMap\u7684\u95e8\u6237\u7f51\u7ad9\u6765\u63a2\u7d22\u53ef\u7528\u7684\u7ec6\u80de\u7cfb\uff0c\u95e8\u6237\u7f51\u7ad9\u5730\u5740\u4e3a\uff1a<a href=\"https:\/\/depmap.org\/portal\/\">https:\/\/depmap.org\/portal\/<\/a>\u3002\u7136\u800c\uff0c\u5728GRETTA\u4e2d\u8fd8\u6709\u4e00\u4e9b\u7b80\u5355\u5185\u5efa\u7684\u529f\u80fd\u53ef\u4ee5\u4f9b\u7528\u6237\u4f7f\u7528\u4e00\u7cfb\u5217list_available\u51fd\u6570\uff0c\u5305\u62eclist_mutations(), list_cancer_types(), list_cancer_subtypes()\u3002<br \/>\n\u76ee\u524d\u9ed8\u8ba4\u4f7f\u7528\u7684DepMap\u6570\u636e\u7248\u672c\u662f22Q2\uff0c\u5305\u542b1771\u4e2a\u764c\u7ec6\u80de\u7cfb\u7684\u5168\u57fa\u56e0\u7ec4\u6d4b\u5e8f\u6216\u5168\u5916\u663e\u5b50\u6d4b\u5e8f\u6ce8\u91ca\uff081406\u4e2a\u7ec6\u80de\u7cfb\u5177\u6709RNA-seq\u6570\u636e\uff0c375\u4e2a\u7ec6\u80de\u7cfb\u5177\u6709\u5b9a\u91cf\u86cb\u767d\u8d28\u7ec4\u5b66\u6570\u636e\uff0c1086\u4e2a\u7ec6\u80de\u7cfb\u5177\u6709CRISPR-Cas9\u6572\u9664\u7b5b\u9009\u6570\u636e\uff09\u3002<\/p>\n<p>## Find ARID1A hotspot mutations detected in all cell lines<br \/>\nlist_mutations(gene = &#8220;ARID1A&#8221;, is_hotspot = TRUE, data_dir = gretta_data_dir)<\/p>\n<p>## List all available cancer types<br \/>\nlist_cancer_types(data_dir = gretta_data_dir)<br \/>\n#&gt; [1] &#8220;Kidney Cancer&#8221; &#8220;Leukemia&#8221;<br \/>\n#&gt; [3] &#8220;Lung Cancer&#8221; &#8220;Non-Cancerous&#8221;<br \/>\n#&gt; [5] &#8220;Sarcoma&#8221; &#8220;Lymphoma&#8221;<br \/>\n#&gt; [7] &#8220;Colon\/Colorectal Cancer&#8221; &#8220;Pancreatic Cancer&#8221;<br \/>\n#&gt; [9] &#8220;Gastric Cancer&#8221; &#8220;Rhabdoid&#8221;<br \/>\n#&gt; [11] &#8220;Endometrial\/Uterine Cancer&#8221; &#8220;Esophageal Cancer&#8221;<br \/>\n#&gt; [13] &#8220;Breast Cancer&#8221; &#8220;Brain Cancer&#8221;<br \/>\n#&gt; [15] &#8220;Ovarian Cancer&#8221; &#8220;Bone Cancer&#8221;<br \/>\n#&gt; [17] &#8220;Myeloma&#8221; &#8220;Head and Neck Cancer&#8221;<br \/>\n#&gt; [19] &#8220;Bladder Cancer&#8221; &#8220;Skin Cancer&#8221;<br \/>\n#&gt; [21] &#8220;Bile Duct Cancer&#8221; &#8220;Prostate Cancer&#8221;<br \/>\n#&gt; [23] &#8220;Cervical Cancer&#8221; &#8220;Thyroid Cancer&#8221;<br \/>\n#&gt; [25] &#8220;Neuroblastoma&#8221; &#8220;Eye Cancer&#8221;<br \/>\n#&gt; [27] &#8220;Liposarcoma&#8221; &#8220;Gallbladder Cancer&#8221;<br \/>\n#&gt; [29] &#8220;Teratoma&#8221; &#8220;Unknown&#8221;<br \/>\n#&gt; [31] &#8220;Liver Cancer&#8221; &#8220;Adrenal Cancer&#8221;<br \/>\n#&gt; [33] &#8220;Embryonal Cancer&#8221;<\/p>\n<p>## List all available cancer subtypes<br \/>\nlist_cancer_subtypes(input_disease = &#8220;Lung Cancer&#8221;, data_dir = gretta_data_dir)<br \/>\n#&gt; [1] &#8220;Non-Small Cell Lung Cancer (NSCLC), Adenocarcinoma&#8221;<br \/>\n#&gt; [2] &#8220;Small Cell Lung Cancer (SCLC)&#8221;<br \/>\n#&gt; [3] &#8220;Non-Small Cell Lung Cancer (NSCLC), Squamous Cell Carcinoma&#8221;<br \/>\n#&gt; [4] &#8220;Mesothelioma&#8221;<br \/>\n#&gt; [5] &#8220;Non-Small Cell Lung Cancer (NSCLC), Large Cell Carcinoma&#8221;<br \/>\n#&gt; [6] NA<br \/>\n#&gt; [7] &#8220;Non-Small Cell Lung Cancer (NSCLC), unspecified&#8221;<br \/>\n#&gt; [8] &#8220;Non-Small Cell Lung Cancer (NSCLC), Adenosquamous Carcinoma&#8221;<br \/>\n#&gt; [9] &#8220;Carcinoid&#8221;<br \/>\n#&gt; [10] &#8220;Non-Small Cell Lung Cancer (NSCLC), Mucoepidermoid Carcinoma&#8221;<br \/>\n#&gt; [11] &#8220;Carcinoma&#8221;<\/p>\n<h3>\u9009\u62e9\u7a81\u53d8\u7ec6\u80de\u7cfb\u548c\u5bf9\u7167\u7ec6\u80de\u7cfb\u7ec4<\/h3>\n<p>\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0cselect_cell_lines()\u5c06\u6839\u636e\u6307\u5b9a\u57fa\u56e0\u7684\u529f\u80fd\u4e27\u5931\u6539\u53d8\u6765\u8bc6\u522b\u764c\u7ec6\u80de\u7cfb\uff0c\u5e76\u5c06\u5b83\u4eec\u5206\u4e3a\u516d\u7ec4\u4e0d\u540c\u7684\u7ec4\u3002<br \/>\n\u529f\u80fd\u4e27\u5931\u6027\u6539\u53d8\u5305\u62ec\u88ab\u6ce8\u91ca\u4e3a\u201cNonsense_Mutation\u201d\uff08\u65e0\u4e49\u7a81\u53d8\uff09\u3001\u201cFrame_Shift_Ins\u201d\uff08\u6846\u67b6\u63d2\u5165\uff09\u3001\u201cSplice_Site\u201d\uff08\u526a\u63a5\u4f4d\u70b9\uff09\u3001\u201cDe_novo_Start_OutOfFrame\u201d\uff08\u975e\u6846\u67b6\u542f\u52a8\u5b50\uff09\u3001\u201cFrame_Shift_Del\u201d\uff08\u6846\u67b6\u5220\u9664\uff09\u3001\u201cStart_Codon_SNP\u201d\uff08\u542f\u52a8\u5b50\u5bc6\u7801\u5b50\u5355\u500d\u578b\uff09\u3001\u201cStart_Codon_Del\u201d\uff08\u542f\u52a8\u5b50\u5bc6\u7801\u5b50\u5220\u9664\uff09\u548c\u201cStart_Codon_Ins\u201d\uff08\u542f\u52a8\u5b50\u5bc6\u7801\u5b50\u63d2\u5165\uff09\u7684\u53d8\u5f02\u3002\u540c\u65f6\uff0c\u590d\u5236\u6570\u6539\u53d8\u4e5f\u88ab\u7eb3\u5165\u8003\u8651\uff0c\u5e76\u5206\u4e3a\u201cDeep_del\u201d\uff08\u6df1\u5ea6\u7f3a\u5931\uff09\u3001\u201cLoss\u201d\uff08\u7f3a\u5931\uff09\u3001\u201cNeutral\u201d\uff08\u4e2d\u7acb\uff09\u3001\u548c\u201cAmplified\u201d\uff08\u6269\u589e\uff09\u3002<br \/>\n\u9ed8\u8ba4\u5206\u914d\u7684\u7ec6\u80de\u7cfb\u7ec4\u662f\uff1a<\/p>\n<ul>\n<li>Control\u7ec6\u80de\u7cfb\u4e0d\u643a\u5e26\u5177\u6709\u4e2d\u6027\u62f7\u8d1d\u6570(CN)\u7684\u4efb\u4f55\u5355\u6838\u82f7\u9178\u53d8\u5f02(SNVs)\u6216\u63d2\u5165\u548c\u7f3a\u5931(InDels)\u3002<\/li>\n<li>HomDel\u7ec6\u80de\u7cfb\u542b\u6709\u4e00\u4e2a\u6216\u591a\u4e2a\u7eaf\u5408\u6709\u5bb3SNVs\uff0c\u6216\u8005\u5177\u6709\u6df1\u5ea6\u62f7\u8d1d\u6570\u635f\u5931\u3002<\/li>\n<li>T-HetDel\u7ec6\u80de\u7cfb\u542b\u6709\u4e24\u4e2a\u6216\u66f4\u591a\u5177\u6709\u4e2d\u6027\u6216\u62f7\u8d1d\u6570\u635f\u5931\u7684\u6709\u5bb3SNVs\/InDels\u6742\u5408\u5b50\u3002<\/li>\n<li>HetDel\u7ec6\u80de\u7cfb\u542b\u6709\u4e00\u4e2a\u5177\u6709\u4e2d\u6027\u62f7\u8d1d\u6570\u7684\u6709\u5bb3SNVs\/InDels\u6742\u5408\u5b50\uff0c\u6216\u8005\u6ca1\u6709\u62f7\u8d1d\u6570\u635f\u5931\u7684SNVs\/InDels\u3002<\/li>\n<li>Amplified\u7ec6\u80de\u7cfb\u4e0d\u542b\u6709\u62f7\u8d1d\u6570\u589e\u52a0\u7684\u6709\u5bb3SNVs\/InDels\u3002<\/li>\n<li>Others\u7ec6\u80de\u7cfb\u542b\u6709\u6709\u5bb3SNVs\uff0c\u5176\u62f7\u8d1d\u6570\u589e\u52a0\u3002<\/li>\n<\/ul>\n<p>ARID1A_groups &lt;- select_cell_lines(input_gene = &#8220;ARID1A&#8221;, data_dir = gretta_data_dir)<br \/>\n#&gt; Selecting mutant groups for: ARID1A in all cancer cell lines<\/p>\n<p>## Show number of cell lines in each group<br \/>\ncount(ARID1A_groups, Group)<br \/>\n#&gt; # A tibble: 5 \u00d7 2<br \/>\n#&gt; Group n<br \/>\n#&gt; &lt;chr&gt; &lt;int&gt;<br \/>\n#&gt; 1 ARID1A_HetDel 61<br \/>\n#&gt; 2 ARID1A_HomDel 23<br \/>\n#&gt; 3 ARID1A_T-HetDel 30<br \/>\n#&gt; 4 Control 906<br \/>\n#&gt; 5 Others 66<\/p>\n<h3>\u53ef\u9009\u7684\u7ec6\u80de\u7cfb\u8fc7\u6ee4\u5668<\/h3>\n<p>\u8fd8\u6709\u51e0\u4e2a\u989d\u5916\u7684\u8fc7\u6ee4\u5668\u53ef\u4ee5\u7ec4\u5408\u5728\u4e00\u8d77\uff0c\u4ee5\u7f29\u5c0f\u6211\u4eec\u7684\u641c\u7d22\u8303\u56f4\u3002\u8fd9\u4e9b<\/p>\n<ul>\n<li>input_aa_change &#8211; \u901a\u8fc7\u6c28\u57fa\u9178\u53d8\u5316\uff08\u4f8b\u5982\u201cp.Q515*\u201d\uff09\u3002<\/li>\n<li>input_disease &#8211; \u901a\u8fc7\u75be\u75c5\u7c7b\u578b\uff08\u4f8b\u5982\u201c\u80f0\u817a\u764c\u201d\uff09<\/li>\n<li>input_disease_subtype &#8211; \u901a\u8fc7\u75be\u75c5\u4e9a\u578b\uff08\u4f8b\u5982\u201c\u5bfc\u7ba1\u817a\u9cde\u72b6\u7ec6\u80de\u764c\u201d\uff09<\/li>\n<\/ul>\n<p>## Find pancreatic cancer cell lines with ARID1A mutations<br \/>\nARID1A_pancr_groups &lt;- select_cell_lines(input_gene = &#8220;ARID1A&#8221;,<br \/>\ninput_disease = &#8220;Pancreatic Cancer&#8221;,<br \/>\ndata_dir = gretta_data_dir)<br \/>\n#&gt; Selecting mutant groups for: ARID1A in Pancreatic Cancer, cell lines<\/p>\n<p>## Show number of cell lines in each group<br \/>\ncount(ARID1A_pancr_groups, Group)<br \/>\n#&gt; # A tibble: 4 \u00d7 2<br \/>\n#&gt; Group n<br \/>\n#&gt; &lt;chr&gt; &lt;int&gt;<br \/>\n#&gt; 1 ARID1A_HetDel 5<br \/>\n#&gt; 2 ARID1A_HomDel 4<br \/>\n#&gt; 3 Control 36<br \/>\n#&gt; 4 Others 2<\/p>\n<h3>\u68c0\u6d4b\u5dee\u5f02\u8868\u8fbe<\/h3>\n<p>\u5728\u4e09\u79cd\u7a81\u53d8\u764c\u7ec6\u80de\u7cfb\u7ec4 ARID1A_HomDel\u3001ARID1A_T-HetDel \u548c ARID1A_HetDel \u4e2d\uff0c\u5177\u6709 ARID1A_HomDel \u7a81\u53d8\u7684\u764c\u7ec6\u80de\u7cfb\u6700\u53ef\u80fd\u5bfc\u81f4 <em>ARID1A<\/em> \u7684\u4e22\u5931\u6216\u8868\u8fbe\u51cf\u5c11\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u60f3\u8981\u68c0\u67e5 ARID1A_HomDel \u7a81\u53d8\u7ec4\u4e2d\u7684\u7ec6\u80de\u7cfb\u4e0e\u5bf9\u7167\u7ec6\u80de\u7cfb\u76f8\u6bd4\u662f\u5426\u5177\u6709\u663e\u7740\u8f83\u5c11\u7684 <em>ARID1A<\/em> RNA \u6216\u86cb\u767d\u8d28\u8868\u8fbe\u3002<\/p>\n<p>## Select only HomDel and Control cell lines<br \/>\nARID1A_groups_subset &lt;- ARID1A_groups %&gt;% filter(Group %in% c(&#8220;ARID1A_HomDel&#8221;, &#8220;Control&#8221;))<\/p>\n<p>## Get RNA expression<br \/>\nARID1A_rna_expr &lt;- extract_rna(<br \/>\ninput_samples = ARID1A_groups_subset$DepMap_ID,<br \/>\ninput_genes = &#8220;ARID1A&#8221;,<br \/>\ndata_dir = gretta_data_dir)<br \/>\n#&gt; Following sample did not contain RNA data: ACH-000047, ACH-000426, ACH-000658, ACH-000979, ACH-001039, ACH-001063, ACH-001065, ACH-001107, ACH-001126, ACH-001137, ACH-001205, ACH-001212, ACH-001227, ACH-001331, ACH-001544, ACH-001606, ACH-001639, ACH-001675, ACH-001955, ACH-001956, ACH-001957, ACH-002083, ACH-002106, ACH-002109, ACH-002110, ACH-002114, ACH-002116, ACH-002119, ACH-002140, ACH-002141, ACH-002143, ACH-002150, ACH-002156, ACH-002160, ACH-002161, ACH-002179, ACH-002181, ACH-002186, ACH-002189, ACH-002198, ACH-002202, ACH-002210, ACH-002212, ACH-002217, ACH-002228, ACH-002229, ACH-002230, ACH-002233, ACH-002234, ACH-002239, ACH-002243, ACH-002247, ACH-002249, ACH-002250, ACH-002257, ACH-002261, ACH-002263, ACH-002265, ACH-002269, ACH-002278, ACH-002280, ACH-002282, ACH-002283, ACH-002284, ACH-002285, ACH-002294, ACH-002295, ACH-002296, ACH-002297, ACH-002298, ACH-002304, ACH-002305, ACH-002399, ACH-002874, ACH-002875<\/p>\n<p>\u5e76\u975e\u6240\u6709\u7ec6\u80de\u7cfb\u90fd\u5305\u542bRNA\u548c\/\u6216\u86cb\u767d\u8d28\u8868\u8fbe\u8c31\uff0c\u5e76\u4e14\u5e76\u975e\u6240\u6709\u86cb\u767d\u8d28\u90fd\u901a\u8fc7\u8d28\u8c31\u68c0\u6d4b\u5230\u3002\uff08\u6709\u5173\u6570\u636e\u751f\u6210\u7684\u8be6\u7ec6\u4fe1\u606f\uff0c\u8bf7\u53c2\u89c1<a href=\"https:\/\/depmap.org\/portal\/\">DepMap\u7f51\u7ad9<\/a>\u3002\uff09<\/p>\n<p>## Get protein expression<br \/>\nARID1A_prot_expr &lt;- extract_prot(<br \/>\ninput_samples = ARID1A_groups_subset$DepMap_ID,<br \/>\ninput_genes = &#8220;ARID1A&#8221;,<br \/>\ndata_dir = gretta_data_dir)<\/p>\n<p>## Produces an error message since ARID1A protein data is not available<\/p>\n<p>\u4f7f\u7528Welch\u7684t\u68c0\u9a8c\uff0c\u6211\u4eec\u53ef\u4ee5\u68c0\u67e5\u76f8\u5bf9\u4e8e\u5bf9\u7167\u7ec6\u80de\u7cfb\uff0cARID1A_HomDel\u7ec6\u80de\u7cfb\u4e2d\u7684ARID1A RNA\u8868\u8fbe\u91cf\uff08\u4ee5TPM\u4e3a\u5355\u4f4d\uff09\u662f\u5426\u663e\u8457\u964d\u4f4e\u3002<\/p>\n<p>## Append groups and test differential expression<br \/>\nARID1A_rna_expr &lt;- left_join(<br \/>\nARID1A_rna_expr,<br \/>\nARID1A_groups_subset %&gt;% select(DepMap_ID, Group)) %&gt;%<br \/>\nmutate(Group = fct_relevel(Group,&#8221;Control&#8221;)) # show Control group first<br \/>\n#&gt; Joining with `by = join_by(DepMap_ID)`<\/p>\n<p>## T-test<br \/>\nt.test(ARID1A_8289 ~ Group, ARID1A_rna_expr)<br \/>\n#&gt;<br \/>\n#&gt; Welch Two Sample t-test<br \/>\n#&gt;<br \/>\n#&gt; data: ARID1A_8289 by Group<br \/>\n#&gt; t = 2.5764, df = 22.873, p-value = 0.01692<br \/>\n#&gt; alternative hypothesis: true difference in means between group Control and group ARID1A_HomDel is not equal to 0<br \/>\n#&gt; 95 percent confidence interval:<br \/>\n#&gt; 0.1146094 1.0498810<br \/>\n#&gt; sample estimates:<br \/>\n#&gt; mean in group Control mean in group ARID1A_HomDel<br \/>\n#&gt; 4.635784 4.053539<\/p>\n<p>## plot<br \/>\nggplot(ARID1A_rna_expr, aes(x = Group, y = ARID1A_8289)) +<br \/>\ngeom_boxplot()<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" width=\"640\" height=\"457\" class=\"wp-image-27723\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-2.png?resize=640%2C457\" alt=\"fig:\" srcset=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-2.png?w=1822 1822w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-2.png?resize=300%2C214 300w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-2.png?resize=1024%2C732 1024w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-2.png?resize=768%2C549 768w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-2.png?resize=1536%2C1098 1536w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-2.png?resize=600%2C429 600w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-2.png?w=1280 1280w\" sizes=\"(max-width: 640px) 100vw, 640px\" data-recalc-dims=\"1\" \/><\/p>\n<h3>\u8fdb\u884c\u5168\u57fa\u56e0\u7ec4<em>in silico<\/em>\u9057\u4f20\u7b5b\u9009<\/h3>\n<p>\u5728\u786e\u5b9aARID1A_HomDel\u7ec4\u4e2d\u7684\u7ec6\u80de\u7cfb\u4e0eControl\u7ec6\u80de\u7cfb\u76f8\u6bd4\u5728RNA\u8868\u8fbe\u4e0a\u5177\u6709\u7edf\u8ba1\u5b66\u4e0a\u663e\u8457\u964d\u4f4e\u4e4b\u540e\uff0c\u4e0b\u4e00\u6b65\u662f\u4f7f\u7528screen_results()\u6267\u884c<em>in silico<\/em>\u57fa\u56e0\u7b5b\u9009\u3002 \u8fd9\u4f7f\u7528DepMap\u7684\u5168\u57fa\u56e0\u7ec4CRISPR-Cas9\u6572\u9664\u7b5b\u9009\u4e2d\u4ea7\u751f\u7684\u4f9d\u8d56\u6982\u7387\uff08\u6216\u201c\u81f4\u6b7b\u6982\u7387\u201d\uff09\u3002<br \/>\n<strong>\u81f4\u6b7b\u6982\u7387\uff08Lethality probabilities\uff09<\/strong>\u7684\u8303\u56f4\u4ece0.0\u52301.0\uff0c\u6bcf\u4e2a\u88ab\u7b5b\u67e5\u7684\u764c\u7ec6\u80de\u7cfb\u4e2d\u6bcf\u4e2a\u57fa\u56e0\u6572\u9664\uff08\u5171\u9488\u5bf9739\u4e2a\u764c\u7ec6\u80de\u7cfb\u4e2d\u768418\uff0c334\u4e2a\u57fa\u56e0\uff09\u7684\u81f4\u6b7b\u6982\u7387\u90fd\u6709\u6240\u91cf\u5316\u3002\u4e00\u4e2a\u57fa\u56e0\u6572\u9664\u7684\u81f4\u6b7b\u6982\u7387\u4e3a0.0\u8868\u660e\u8be5\u57fa\u56e0\u5bf9\u4e8e\u8be5\u7ec6\u80de\u7cfb\u662f\u975e\u81f4\u547d\u7684\uff0c\u800c\u4e00\u4e2a\u57fa\u56e0\u6572\u9664\u7684\u81f4\u6b7b\u6982\u7387\u4e3a1.0\u8868\u660e\u8be5\u57fa\u56e0\u662f\u81f4\u547d\u7684\uff08\u5373\u975e\u5e38\u81f4\u547d\uff09\u3002\u66f4\u591a\u8be6\u7ec6\u4fe1\u606f\u53ef\u53c2\u89c1<a href=\"https:\/\/doi.org\/10.1038\/ng.3984\">Meyers, R., et al., 2017<\/a>\u3002<br \/>\nscreen_results()\u51fd\u6570\u7684\u6838\u5fc3\u8fdb\u884c\u4e86\u591a\u4e2aMann-Whitney U\u68c0\u9a8c\uff0c\u6bd4\u8f83\u4e86\u7a81\u53d8\u7ec4\u548c\u5bf9\u7167\u7ec4\u4e2d\u6bcf\u4e2a\u9776\u57fa\u56e0\u7684\u81f4\u6b7b\u6982\u7387\u3002\u751f\u6210\u7684\u6570\u636e\u6846\u5177\u6709\u4ee5\u4e0b\u5217\uff1a<\/p>\n<ul>\n<li>GeneName_ID\uff1aHugo symbol\u5e26\u6709NCBI\u57fa\u56e0ID<\/li>\n<li>GeneNames\uff1aHugo symbol<\/li>\n<li><em>median\uff0c<\/em>mean\uff0c<em>sd\uff0c<\/em>iqr\uff1a\u63a7\u5236\u548c\u7a81\u53d8\u7ec4\u4f9d\u8d56\u6982\u7387\u7684\u4e2d\u4f4d\u6570\u3001\u5747\u503c\u3001\u6807\u51c6\u5dee\uff08sd\uff09\u548c\u56db\u5206\u4f4d\u6570\u8303\u56f4\uff08iqr\uff09\u3002\u4f9d\u8d56\u6982\u7387\u7684\u8303\u56f4\u4ece\u96f6\u5230\u4e00\uff0c\u5176\u4e2d\u4e00\u8868\u793a\u5fc5\u9700\u57fa\u56e0\uff08\u5373\u57fa\u56e0\u6572\u9664\u662f\u81f4\u6b7b\u7684\uff09\uff0c\u96f6\u8868\u793a\u975e\u5fc5\u9700\u57fa\u56e0\uff08\u57fa\u56e0\u6572\u9664\u4e0d\u662f\u81f4\u6b7b\u7684\uff09<\/li>\n<li>Pval\uff1a\u63a7\u5236\u548c\u7a81\u53d8\u7ec4\u4e4b\u95f4\u7684Mann-Whitney U\u68c0\u9a8c\u7684P\u503c\u3002<\/li>\n<li>Adj_pval\uff1a\u7ecf\u8fc7BH\u6821\u6b63\u7684P\u503c\u3002<\/li>\n<li>log2FC_by_median\uff1a\u4ee5log2\u4e3a\u57fa\u51c6\u5f52\u4e00\u5316\u7684\u4f9d\u8d56\u6982\u7387\u4e2d\u4f4d\u6570\u53d8\u5316\u500d\u6570\uff08\u7a81\u53d8\/\u5bf9\u7167\uff09\u3002<\/li>\n<li>log2FC_by_mean\uff1a\u4ee5log2\u4e3a\u57fa\u51c6\u5f52\u4e00\u5316\u7684\u4f9d\u8d56\u6982\u7387\u5747\u503c\u53d8\u5316\u500d\u6570\uff08\u7a81\u53d8\/\u5bf9\u7167\uff09\u3002<\/li>\n<li>CliffDelta\uff1a\u7a81\u53d8\u548c\u5bf9\u7167\u4f9d\u8d56\u6982\u7387\u4e4b\u95f4\u7684Cliff&#8217;s delta\u975e\u53c2\u6570\u6548\u5e94\u5927\u5c0f\u3002\u8303\u56f4\u4ecb\u4e8e-1\u52301\u4e4b\u95f4\u3002<\/li>\n<li>dip_pval\uff1aHartigan\u7684dip\u6d4b\u8bd5\u7684p\u503c\u3002\u6d4b\u8bd5\u7a81\u53d8\u4f9d\u8d56\u6982\u7387\u7684\u5206\u5e03\u662f\u5426\u4e3a\u5355\u6a21\u6001\u3002\u5982\u679cdip\u6d4b\u8bd5\u88ab\u62d2\u7edd\uff08p\u503c\uff1c0.05\uff09\uff0c\u8fd9\u8868\u660e\u5b58\u5728\u591a\u6a21\u6001\u4f9d\u8d56\u6982\u7387\u5206\u5e03\uff0c\u5e76\u4e14\u53ef\u80fd\u6709\u5176\u4ed6\u56e0\u7d20\u5bfc\u81f4\u8fd9\u79cd\u5206\u79bb\u3002<\/li>\n<li>Interaction_score\uff1a\u4ece\u5e26\u7b26\u53f7\u7684p\u503c\u4e2d\u5f97\u51fa\u7684\u7ec4\u5408\u503c\uff1a-log10(Pval) * sign(log2FC_by_median)\u3002\u8d1f\u5206\u8868\u793a\u81f4\u6b7b\u9057\u4f20\u76f8\u4e92\u4f5c\u7528\uff0c\u6b63\u5206\u8868\u793a\u7f13\u89e3\u9057\u4f20\u76f8\u4e92\u4f5c\u7528\u3002<\/li>\n<\/ul>\n<p><strong>\u6ce8\u610f<\/strong> \u8fd9\u4e2a\u8fc7\u7a0b\u53ef\u80fd\u9700\u8981\u51e0\u4e2a\u5c0f\u65f6\uff0c\u5177\u4f53\u53d6\u51b3\u4e8e\u5206\u914d\u7684\u6838\u5fc3\u6570\u3002\u5c0f\u82b1\u4e0b\u9762\u7684\u4f8b\u5b50 GI_screen() \u82b1\u4e86\u5927\u7ea62\u4e2a\u5c0f\u65f6\u6765\u5904\u7406\u3002\u4e3a\u4e86\u8282\u7701\u65f6\u95f4\uff0c\u5c0f\u82b1\u5df2\u7ecf\u4e3a\u60a8\u9884\u5148\u5904\u7406\u4e86\u8fd9\u4e00\u6b65\u3002<\/p>\n<p>ARID1A_mutant_id &lt;- ARID1A_groups %&gt;% filter(Group %in% c(&#8220;ARID1A_HomDel&#8221;)) %&gt;% pull(DepMap_ID)<br \/>\nARID1A_control_id &lt;- ARID1A_groups %&gt;% filter(Group %in% c(&#8220;Control&#8221;)) %&gt;% pull(DepMap_ID)<\/p>\n<p>## See warning above.<br \/>\n## This can take several hours depending on number of lines\/cores used.<br \/>\nscreen_results &lt;- GI_screen(<br \/>\ncontrol_id = ARID1A_control_id,<br \/>\nmutant_id = ARID1A_mutant_id,<br \/>\ncore_num = 5, # depends on how many cores you have<br \/>\noutput_dir = gretta_output_dir, # Will save your results here as well as in the variable<br \/>\ndata_dir = gretta_data_dir,<br \/>\ntest = FALSE) # use TRUE to run a short test to make sure all will run overnight.<\/p>\n<p>## Load prepared ARID1A screen result<br \/>\nload(paste0(gretta_data_dir,&#8221;\/sample_22Q2_ARID1A_KO_screen.rda&#8221;), envir = environment())<\/p>\n<p>\u6211\u4eec\u53ef\u4ee5\u5feb\u901f\u786e\u5b9aGRETTA\u662f\u5426\u9884\u6d4b\u4e86\u4efb\u4f55\u81f4\u6b7b\u6027\u9057\u4f20\u76f8\u4e92\u4f5c\u7528\u3002\u6211\u4eec\u4f7f\u7528Pval\u622a\u65ad\u503c\u4e3a0.05\uff0c\u5e76\u6839\u636eInteraction_score\u8fdb\u884c\u6392\u540d\u3002<\/p>\n<p>screen_results %&gt;%<br \/>\nfilter(Pval &lt; 0.05) %&gt;%<br \/>\narrange(-Interaction_score) %&gt;%<br \/>\nselect(GeneNames:Mutant_median, Pval, Interaction_score) %&gt;% head<br \/>\n#&gt; # A tibble: 6 \u00d7 5<br \/>\n#&gt; GeneNames Control_median Mutant_median Pval Interaction_score<br \/>\n#&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;<br \/>\n#&gt; 1 ARID1B 0.0579 0.515 6.84e-10 9.16<br \/>\n#&gt; 2 CCDC110 0.0165 0.0303 3.54e- 4 3.45<br \/>\n#&gt; 3 APOO 0.0168 0.0283 9.61e- 4 3.02<br \/>\n#&gt; 4 NHS 0.0352 0.0539 9.69e- 4 3.01<br \/>\n#&gt; 5 SLC66A2 0.00793 0.0134 1.06e- 3 2.98<br \/>\n#&gt; 6 ATXN7L1 0.0138 0.0259 1.78e- 3 2.75<\/p>\n<p>\u5c0f\u82b1\u53ef\u4ee5\u53d1\u73b0\uff0c<em>ARID1B<\/em>\uff0c\u4e00\u79cd\u5df2\u77e5\u7684\u4e0e<em>ARID1A<\/em>\u7684\u5408\u6210\u81f4\u6b7b\u76f8\u4e92\u4f5c\u7528\uff0c\u662f\u6b64\u5217\u8868\u7684\u9876\u90e8\u3002<\/p>\n<h3>\u53ef\u9009\u6b65\u9aa4\uff1a\u8fdb\u884c\u5c0f\u89c4\u6a21\u7b5b\u9009<\/h3>\n<p>\u8981\u8fdb\u884c\u5c0f\u578b\u4f53\u5185\u7b5b\u9009\uff0c\u53ef\u4ee5\u5728 gene_list = \u53c2\u6570\u4e2d\u63d0\u4f9b\u57fa\u56e0\u5217\u8868\u3002<\/p>\n<p>small_screen_results &lt;- GI_screen(<br \/>\ncontrol_id = ARID1A_control_id,<br \/>\nmutant_id = ARID1A_mutant_id,<br \/>\ngene_list = c(&#8220;ARID1A&#8221;, &#8220;ARID1B&#8221;, &#8220;SMARCA2&#8221;, &#8220;GAPDH&#8221;, &#8220;SMARCC2&#8221;),<br \/>\ncore_num = 5, # depends on how many cores you have<br \/>\noutput_dir = gretta_output_dir, # Will save your results here as well as in the variable<br \/>\ndata_dir = gretta_data_dir)<\/p>\n<h3>\u53ef\u89c6\u5316\u7b5b\u9009\u7ed3\u679c<\/h3>\n<p>\u6700\u540e\uff0c\u4e00\u65e6\u5b8c\u6210<em>\u8ba1\u7b97\u673a\u7b5b\u9009<\/em>\uff0c\u5c31\u53ef\u4ee5\u4f7f\u7528plot_screen()\u51fd\u6570\u5feb\u901f\u53ef\u89c6\u5316\u7ed3\u679c\u3002\u9633\u6027\u9057\u4f20\u76f8\u4e92\u4f5c\u7528\u5206\u6570\u8868\u793a\u6f5c\u5728\u7684\u5408\u6210\u81f4\u6b7b\u9057\u4f20\u4e92\u4f5c\u7269\uff0c\u800c\u8d1f\u5206\u6570\u8868\u793a\u6f5c\u5728\u7684\u7f13\u89e3\u9057\u4f20\u4e92\u4f5c\u7269\u3002\u4e0d\u51fa\u6240\u6599\uff0c\u5c0f\u82b1\u786e\u5b9a\u4e86<em>ARID1B<\/em>\u662f<em>ARID1A<\/em>\u7684\u5408\u6210\u81f4\u6b7b\u76f8\u4e92\u4f5c\u7528\u7269\u3002<\/p>\n<p>## Visualize results, turn on gene labels,<br \/>\n## and label three genes each that are predicted to have<br \/>\n## lethal and alleviating genetic interactions, respectively<\/p>\n<p>plot_screen(result_df = screen_results,<br \/>\nlabel_genes = TRUE,<br \/>\nlabel_n = 3)<br \/>\n#&gt; Warning: Removed 7 rows containing missing values (`geom_point()`).<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" width=\"640\" height=\"458\" class=\"wp-image-27724\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-3.png?resize=640%2C458\" alt=\"fig:\" srcset=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-3.png?w=1292 1292w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-3.png?resize=300%2C215 300w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-3.png?resize=1024%2C732 1024w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-3.png?resize=768%2C549 768w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-3.png?resize=600%2C429 600w\" sizes=\"(max-width: 640px) 100vw, 640px\" data-recalc-dims=\"1\" \/><\/p>\n<h2>\u793a\u4f8b2\uff1a\u9274\u5b9a<em>ARID1A<\/em>\u534f\u540c\u5fc5\u9700\uff08co-essential\uff09\u57fa\u56e0<\/h2>\n<p>\u4f5c\u7528\u4e8e\u76f8\u540c\u6216\u534f\u540c\u9014\u5f84\u4ee5\u53ca\u76f8\u540c\u590d\u5408\u7269\u4e2d\u7684\u57fa\u56e0\uff0c\u5176\u529f\u80fd\u53d7\u5230\u6270\u52a8\u540e\u8868\u73b0\u51fa\u76f8\u4f3c\u7684\u9002\u5e94\u6548\u5e94\uff0c\u8fd9\u4e9b\u5177\u6709\u7c7b\u4f3c\u6548\u5e94\u7684\u57fa\u56e0\u88ab\u89c6\u4e3a\u201c\u5171\u540c\u5fc5\u9700\u57fa\u56e0\u201d\u3002\u591a\u4e2a\u7814\u7a76\u4f7f\u7528\u7ed8\u5236\u5171\u540c\u5fc5\u9700\u57fa\u56e0\u56fe\u8c31\u7684\u7b56\u7565\uff0c\u4e3a\u5148\u524d\u6ce8\u91ca\u7684\u57fa\u56e0\u5206\u914d\u529f\u80fd\u4ee5\u53ca\u9274\u5b9a\u5927\u578b\u590d\u5408\u7269\u7684\u65b0\u4e9a\u57fa\uff08<a href=\"https:\/\/doi.org\/10.1038\/s41588-021-00840-z\">Wainberg et al. 2021<\/a>; <a href=\"https:\/\/doi.org\/10.1016\/j.cels.2018.04.011\">Pan et al. 2018<\/a>\uff09\u3002<br \/>\n\u8003\u8651\u5230ARID1A\u662f\u54fa\u4e73\u52a8\u7269SWI\/SNF\u590d\u5408\u7269\u5df2\u77e5\u7684\u4e9a\u57fa(<a href=\"https:\/\/doi.org\/10.1016\/j.cell.2018.09.032\">Mashtalir\u7b492018<\/a>)\uff0c\u5c0f\u82b1\u9884\u8ba1SWI\/SNF\u590d\u5408\u7269\u7684\u6210\u5458\u5c06\u4e0e<em>ARID1A<\/em>\u5171\u4eab\u5171\u540c\u5fc5\u9700\u6027\u3002\u672c\u793a\u4f8b\u5c06\u5c55\u793a\u5982\u4f55\u4f7f\u7528GRETTA\u7ed8\u5236<em>ARID1A<\/em>\u7684\u5171\u5fc5\u9700\u57fa\u56e0\u7f51\u7edc\u3002<\/p>\n<h3>\u9274\u5b9a\u5177\u6709\u6700\u9ad8\u76f8\u5173\u6027\u7cfb\u6570\u7684\u57fa\u56e0<\/h3>\n<p>\u4e3a\u4e86\u786e\u5b9a\u5171\u5fc5\u9700\u57fa\u56e0\uff0c\u6211\u4eec\u5c06\u5bf9<em>ARID1A<\/em>\u57fa\u56e0\u6572\u9664\u6548\u5e94\u4e0e18,333\u4e2a\u57fa\u56e0\u7684\u6572\u9664\u6548\u5e94\u4e4b\u95f4\u8fdb\u884c\u591a\u4e2a\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570\u5206\u6790\u3002\u6211\u4eec\u5c06\u901a\u8fc7\u8ba1\u7b97\u6392\u5e8f\u7cfb\u6570\u66f2\u7ebf\u7684\u62d0\u70b9\u6765\u786e\u5b9a\u4e00\u4e2a\u622a\u65ad\u503c\u3002\u4e0d\u51fa\u6240\u6599\uff0c\u6211\u4eec\u53d1\u73b0\u7f16\u7801SWI \/ SNF\u4e9a\u57fa\u7684\u57fa\u56e0<em>SMARCE1<\/em>\u548c<em>SMARCB1<\/em>\u662f\u524d\u4e24\u4e2a\u5171\u5fc5\u9700\u57fa\u56e0\u3002<\/p>\n<p><strong>\u6ce8\u610f<\/strong> \u6b64\u8fc7\u7a0b\u53ef\u80fd\u9700\u8981\u51e0\u5206\u949f\u7684\u65f6\u95f4\u3002\u5c0f\u82b1\u4e0b\u9762\u7684\u793a\u4f8b coessential_map() + get_inflection_points() \u5904\u7406\u5927\u7ea6\u9700\u898117\u5206\u949f\u7684\u65f6\u95f4\u3002\u4e3a\u4e86\u8282\u7701\u65f6\u95f4\uff0c\u5c0f\u82b1\u5df2\u7ecf\u4e3a\u60a8\u9884\u5148\u5904\u7406\u4e86\u8fd9\u4e00\u6b65\u9aa4\u3002<\/p>\n<p>## Map co-essential genes<br \/>\ncoess_df &lt;- coessential_map(<br \/>\ninput_gene = &#8220;ARID1A&#8221;,<br \/>\ndata_dir = gretta_data_dir,<br \/>\noutput_dir = gretta_output_dir)<\/p>\n<p>## Calculate inflection points of positive and negative curve using co-essential gene results.<br \/>\ncoess_inflection_df &lt;- get_inflection_points(coess_df)<\/p>\n<p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5bf9\u5305\u542b\u5171 essential \u7f51\u7edc\u6570\u636e\u7684\u6570\u636e\u6846\u8fdb\u884c\u6ce8\u91ca\u5e76\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n<p>## Combine and annotate data frame containing co-essential genes<br \/>\ncoess_annotated_df &lt;- annotate_coess(coess_df, coess_inflection_df)<\/p>\n<p>plot_coess(<br \/>\nresult_df = coess_annotated_df,<br \/>\ninflection_df = coess_inflection_df,<br \/>\nlabel_genes = TRUE, # Should gene names be labeled?<br \/>\nlabel_n = 3) # Number of genes to display from each end<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" width=\"640\" height=\"457\" class=\"wp-image-27725\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-4.png?resize=640%2C457\" alt=\"fig:\" srcset=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-4.png?w=1292 1292w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-4.png?resize=300%2C214 300w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-4.png?resize=1024%2C732 1024w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-4.png?resize=768%2C549 768w, https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/fig-4.png?resize=600%2C429 600w\" sizes=\"(max-width: 640px) 100vw, 640px\" data-recalc-dims=\"1\" \/><br \/>\n\u5c0f\u82b1\u8fd8\u53d1\u73b0\uff0c\u4e0eARID1A\u5171\u540c\u81f3\u5173\u91cd\u8981\u7684\u524d\u5341\u4e2a\u57fa\u56e0\u4e2d\u6709\u516b\u4e2a\u5df2\u77e5\u7684SWI\/SNF\u590d\u5408\u7269\u4e9a\u57fa\uff0c\u5373ARID1A\u3001SMARCB1\u3001SMARCE1\u3001SMARCC1\u3001SS18\u3001DPF2\u3001SMARCC2\u548cSMARCD2\u3002<\/p>\n<p>## Show top 10 co-essential genes.<br \/>\ncoess_annotated_df %&gt;% arrange(Rank) %&gt;% head(10)<br \/>\n#&gt; # A tibble: 10 \u00d7 9<br \/>\n#&gt; GeneNameID_A GeneNameID_B estimate statistic p.value parameter Rank<br \/>\n#&gt; &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;int&gt;<br \/>\n#&gt; 1 ARID1A_8289 ARID1A_8289 1 Inf 0 1086 1<br \/>\n#&gt; 2 ARID1A_8289 SMARCB1_6598 0.477 17.9 7.45e-59 1086 2<br \/>\n#&gt; 3 ARID1A_8289 SMARCE1_6605 0.399 14.3 4.30e-39 1086 3<br \/>\n#&gt; 4 ARID1A_8289 SMARCC1_6599 0.369 13.1 9.35e-33 1086 4<br \/>\n#&gt; 5 ARID1A_8289 SS18_6760 0.332 11.6 4.85e-26 1086 5<br \/>\n#&gt; 6 ARID1A_8289 DPF2_5977 0.330 11.5 1.15e-25 1086 6<br \/>\n#&gt; 7 ARID1A_8289 SMARCD2_6603 0.270 9.22 1.10e-16 1086 7<br \/>\n#&gt; 8 ARID1A_8289 SMARCC2_6601 0.242 8.22 2.34e-13 1086 8<br \/>\n#&gt; 9 ARID1A_8289 BCL2_596 0.231 7.82 4.05e-12 1086 9<br \/>\n#&gt; 10 ARID1A_8289 CBFB_865 0.224 7.58 2.07e-11 1086 10<br \/>\n#&gt; # \u2139 2 more variables: Padj_BH &lt;dbl&gt;, Candidate_gene &lt;lgl&gt;<\/p>\n<h3>\u7279\u5b9a\u764c\u75c7\u7c7b\u578b\u7684\u53ef\u9009\u8fc7\u6ee4\u5668<\/h3>\n<p>\u7528\u6237\u8fd8\u53ef\u4ee5\u4f7f\u7528input_disease = &#8220;&#8221;\u9009\u9879\u6309\u75be\u75c5\u7c7b\u578b\u8fdb\u884c\u5b50\u96c6\u5212\u5206\uff0c\u6216\u4f7f\u7528input_cell_lines = c()\u9009\u9879\u5728\u9884\u5148\u9009\u5b9a\u7684\u7ec6\u80de\u7cfb\u7ec4\u5185\u8fdb\u884c\u5b50\u96c6\u5212\u5206\uff0c\u800c\u4e0d\u662f\u5728\u6574\u4e2a\u53ef\u7528\u7ec6\u80de\u7cfb\u4e2d\u6620\u5c04\u91cd\u8981\u6027\u3002\u4e0b\u9762\u5c0f\u82b1\u63d0\u4f9b\u4e86\u4e00\u4e2aARID1A\u5fc5\u9700\u57fa\u56e0\u6620\u5c04\u7528\u4e8e\u80f0\u817a\u764c\u7684\u793a\u4f8b\u3002<\/p>\n<p><strong>\u6ce8\u610f<\/strong> \u6839\u636e\u5b50\u96c6\u6b65\u9aa4\u540e\u53ef\u7528\u7684\u7ec6\u80de\u7cfb\u6570\u91cf\uff0c\u5c48\u66f2\u70b9\u8ba1\u7b97\u548c\u9608\u503c\u53ef\u80fd\u4e0d\u662f\u6700\u4f18\u7684\u3002\u5728\u89e3\u91ca\u8fd9\u4e9b\u7ed3\u679c\u65f6\u8bf7\u8c28\u614e\u4f7f\u7528\u3002<\/p>\n<p>## Map co-essential genes in pancreatic cancers only<br \/>\ncoess_df &lt;- coessential_map(<br \/>\ninput_gene = &#8220;ARID1A&#8221;,<br \/>\ninput_disease = &#8220;Pancreatic Cancer&#8221;,<br \/>\ncore_num = 5, ## Depending on how many cores you have access to, increase this value to shorten processing time.<br \/>\ndata_dir = gretta_data_dir,<br \/>\noutput_dir = gretta_output_dir,<br \/>\ntest = FALSE)<\/p>\n<h3>\u9488\u5bf9\u81ea\u5b9a\u4e49\u7ec6\u80de\u7cfb\u7684\u53ef\u9009\u8fc7\u6ee4\u5668<\/h3>\n<p>\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528 input_cell_lines = c() \u9009\u9879\uff0c\u5728\u624b\u52a8\u5b9a\u4e49\u7684\u7ec6\u80de\u7cfb\u5217\u8868\u4e2d\u6620\u5c04\u5fc5\u9700\u6027\u3002<\/p>\n<p><strong>\u6ce8\u610f<\/strong> \u6839\u636e\u6240\u63d0\u4f9b\u7684\u7ec6\u80de\u7cfb\u6570\u91cf\uff0c\u53ef\u80fd\u4e0d\u4f1a\u8ba1\u7b97\u62d0\u70b9\u548c\u53d8\u5316\u70b9\u3002\u5728\u89e3\u91ca\u8fd9\u4e9b\u7ed3\u679c\u65f6\u8bf7\u8c28\u614e\u4f7f\u7528\u3002<\/p>\n<p>custom_lines &lt;- c(&#8220;ACH-000001&#8221;, &#8220;ACH-000002&#8221;, &#8220;ACH-000003&#8221;,&#8230;)<\/p>\n<p>coess_df &lt;- coessential_map(<br \/>\ninput_gene = &#8220;ARID1A&#8221;,<br \/>\ninput_cell_lines = custom_lines,<br \/>\ncore_num = 5, ## Depending on how many cores you have access to, increase this value to shorten processing time.<br \/>\ndata_dir = gretta_data_dir,<br \/>\noutput_dir = gretta_output_dir,<br \/>\ntest = FALSE)<\/p>\n<h2>\u7ed3\u8bed<\/h2>\n<p>\u4f60\u60f3\u8981\u5206\u6790\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528\u548c\u534f\u540c\u5fc5\u9700\u7f51\u7edc\u5417\uff1f\u8bd5\u8bd5GRETTA\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528DepMap\u7684\u516c\u5f00\u6570\u636e\u8fdb\u884cin silico\u7684\u57fa\u56e0\u7b5b\u9009\u3002GRETTA\u8fd8\u53ef\u4ee5\u5e2e\u52a9\u4f60\u53ef\u89c6\u5316\u548c\u89e3\u91ca\u7ed3\u679c\u3002\u8bb0\u5f97\u6211\u4eec\u4e0a\u9762\u7684\u4f8b\u5b50\u5417\uff0c\u5982\u4f55\u7528GRETTA\u53d1\u73b0ARID1A\u7684\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528\u548c\u534f\u540c\u5fc5\u9700\u57fa\u56e0\u3002GRETTA\u5df2\u7ecf\u6709365\u4e2a\u8bbf\u95ee\u8005\u548c135\u4e2a\u5b89\u88c5\u8005\uff0c\u672a\u6765\u53ef\u671f\uff01<\/p>\n<h2>\u53c2\u8003\u6587\u732e<\/h2>\n<p>[1] Takemon Y, Marra MA. GRETTA: an R package for mapping in silico genetic interaction and essentiality networks. Bioinformatics. 2023 Jun 1;39(6):btad381. doi: 10.1093\/bioinformatics\/btad381. PMID: 37326978; PMCID: PMC10284671.<\/p>\n<p>\u5982\u679c\u5c0f\u4f19\u4f34\u6709\u5176\u4ed6\u6570\u636e\u5206\u6790\u9700\u6c42\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4f7f\u7528\u672c\u516c\u53f8\u65b0\u5f00\u53d1\u7684\u751f\u4fe1\u5206\u6790\u5c0f\u5de5\u5177\u4e91\u5e73\u53f0\uff0c\u96f6\u4ee3\u7801\u5b8c\u6210\u5206\u6790\uff0c\u975e\u5e38\u65b9\u4fbf\u5965\uff0c\u4e91\u5e73\u53f0\u7f51\u5740\u4e3a\uff1a(<a href=\"http:\/\/www.biocloudservice.com\/home.html\">http:\/\/www.biocloudservice.com\/home.html<\/a>)\uff0c\u5176\u4e2d\u4e5f\u5305\u62ec\u4e86\u901a\u8def\u8868\u8fbe\u5206\u6790(<a href=\"http:\/\/www.biocloudservice.com\/313\/313.php\">http:\/\/www.biocloudservice.com\/313\/313.php<\/a>)\uff0c\u5355\u7ec6\u80de\u7684\u57fa\u56e0\u5171\u8868\u8fbe\u5206\u6790(<a href=\"http:\/\/www.biocloudservice.com\/906\/906.php\">http:\/\/www.biocloudservice.com\/906\/906.php<\/a>)\u7b49\u5404\u79cd\u5c0f\u5de5\u5177\u54e6~\uff0c\u6709\u5174\u8da3\u7684\u5c0f\u4f19\u4f34\u53ef\u4ee5\u767b\u5f55\u7f51\u7ad9\u8fdb\u884c\u4e86\u89e3\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4eca\u5929\u5c0f\u82b1\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e2a2023\u5e746\u67081\u65e5\u53d1\u8868\u4e8ebioinformatics\u4e0a\u65b0\u9c9c\u51fa\u7089\u7684\u597d\u5305\u2014\u2014GRETTA\uff01  [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":""},"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/27720"}],"collection":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=27720"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/27720\/revisions"}],"predecessor-version":[{"id":27726,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/27720\/revisions\/27726"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=27720"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=27720"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=27720"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}