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solid #e1e1e1;margin-top:10px;}.msgBodyReplyListTop{border-top:0;}.reply_like_num{float:right;font-size:14px;color:#c7c7c7;}.msgData{margin-top:20px;color:#626262;}.msgData span{font-size:14px;padding-right:15px;}.msgData .likes{float:right;padding-right:0;}.js_text_content p{font-size:18px;}.rich_media_meta_link{font-size:15px;}blockquote {padding-left: 10px;border-left: 3px solid #dbdbdb;color: rgba(0,0,0,0.5);font-size: 15px;padding-top: 4px;margin: 1em 0;}.video_iframe{width:500px;height:400px;}.blockquote_info{color:#b5b5b5;margin-top:10px;}#copyright_logo{color:#bdbdbd;}.rich_media_meta_list{margin-bottom:10px;}.reprint{background:#efefef;border-radius:5px;padding:8px;color:#1f1f1f;}.reprint a{word-break:break-all;}.topic{color:#8e8e8e;background:#f7f7f7;border-radius:5px;padding:10px 8px;}.topic a{padding-right:5px;}.topic p{margin-bottom:5px;}\n<\/style>\n<link href=\"https:\/\/www.juyifx.cn\/config\/css\/wxArticle.css\" rel=\"stylesheet\"\/>\n<\/head><script>\nvar data={\"mp\":\"\u751f\u4fe1\u679c\",\"title\":\"GRETTA\uff1a\u6316\u6398\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528\u548c\u534f\u540c\u5fc5\u9700\u7f51\u7edc\u7684\u597d\u5305\uff01\",\"time\":\"2023-12-29 19:00:39\",\"timeStamp\":\"1703847639\"};\n<\/script> \n<body><\/p>\n<div class=\"rich_media\">\n<h1 class=\"rich_media_title\" id=\"activity-name\"><a href=\"http:\/\/mp.weixin.qq.com\/s?__biz=Mzg3OTg3MjQyNg==&mid=2247498308&idx=1&sn=f764278795ad73f50be4638481e0719e&chksm=cf7f6a81f808e3978d245944ec80203aed7eac0663ae0eeb3bb4dd3d627780f6cafbf093f59e#rd\" target=\"_blank\" rel=\"noopener\">GRETTA\uff1a\u6316\u6398\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528\u548c\u534f\u540c\u5fc5\u9700\u7f51\u7edc\u7684\u597d\u5305\uff01<\/a><\/h1>\n<div id=\"meta_content\" class=\"rich_media_meta_list\">\u539f\u521b <\/span>\u5c0f\u679c <\/span><a href=\"javascript:void(0);\" class=\"wx_tap_link js_wx_tap_highlight weui-wa-hotarea\" id=\"js_name\">\u751f\u4fe1\u679c <\/a><\/span>2023-12-29 19:00:39<\/em><\/div>\n<content><\/p>\n<div class=\"topic\">\n\u6536\u5f55\u4e8e\u8bdd\u9898<\/p>\n<a href=\"https:\/\/mp.weixin.qq.com\/mp\/appmsgalbum?__biz=Mzg3OTg3MjQyNg==&action=getalbum&album_id=2812010014583390210#wechat_redirect\" title=\"R\u4ee3\u7801\" target=\"_blank\" rel=\"noopener\">#R\u4ee3\u7801<\/a><a href=\"https:\/\/mp.weixin.qq.com\/mp\/appmsgalbum?__biz=Mzg3OTg3MjQyNg==&action=getalbum&album_id=2664545260517294084#wechat_redirect\" title=\"\u751f\u4fe1\u5b9e\u64cd\" target=\"_blank\" rel=\"noopener\">#\u751f\u4fe1\u5b9e\u64cd<\/a><a href=\"https:\/\/mp.weixin.qq.com\/mp\/appmsgalbum?__biz=Mzg3OTg3MjQyNg==&action=getalbum&album_id=3138206666342268940#wechat_redirect\" title=\"\u751f\u4fe1\u5c0f\u767d\u76f8\u5173\" target=\"_blank\" rel=\"noopener\">#\u751f\u4fe1\u5c0f\u767d\u76f8\u5173<\/a><a href=\"https:\/\/mp.weixin.qq.com\/mp\/appmsgalbum?__biz=Mzg3OTg3MjQyNg==&action=getalbum&album_id=2821072943068839938#wechat_redirect\" title=\"\u751f\u4fe1\u7ed8\u56fe\" target=\"_blank\" rel=\"noopener\">#\u751f\u4fe1\u7ed8\u56fe<\/a><\/p>\n<\/div>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100014480\" data-ratio=\"0.23333333333333334\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/1_rE2v0O5WiaUDJjgv5bxK2G1ePHyH2g-59.jpg?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_jpg\/zcicibWZessAysHY49vPGCraibakiaNq6hQgn8C5MfvVLQGxlNZPEia2PajKAzrE2v0O5WiaUDJjgv5bxK2G1ePHyH2g\/640?wx_fmt=jpeg'\" data-type=\"jpeg\" data-w=\"1080\" style=\"letter-spacing: 0.578px;text-wrap: wrap;font-family: \u5b8b\u4f53;font-size: 15px;text-align: center;\" data-recalc-dims=\"1\" \/>\u4eca\u5929\u5c0f\u679c\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e2a<\/span>2023\u5e746\u67081\u65e5<\/span>\u53d1\u8868\u4e8e<\/span>bioinformatics<\/span>\u4e0a\u65b0\u9c9c\u51fa\u7089\u7684\u597d\u5305\u2014\u2014GRETTA\uff01<\/span><\/span><\/p>\n<p style=\"margin-top: 9.0pt;margin-bottom: 9.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\"><img decoding=\"async\" loading=\"lazy\" class=\"rich_pages wxw-img\" data-imgfileid=\"100014282\" data-ratio=\"0.28425925925925927\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/2_EPsZMnyBwpZtXqCJqt2IiaWruQwLhg.png?resize=560%2C159\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAwaOtnqQlN0kicqNFL8Q1tVStuvTKicsIxNumCPeEnratVLLOibEPsZMnyBwpZtXqCJqt2IiaWruQwLhg\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" height=\"159 .14999389648438\" width=\"560 .010009765625\" data-recalc-dims=\"1\" \/><\/span><\/p>\n<p style=\"margin-top: 9.0pt;margin-bottom: 9.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\">\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\u679c\u5c06\u4e3a\u5927\u5bb6\u4ecb\u7ecd\u8fd9\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\u3002<\/span><\/p>\n<p style=\"margin-top: 9.0pt;margin-bottom: 9.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\"><img decoding=\"async\" loading=\"lazy\" class=\"rich_pages wxw-img\" data-imgfileid=\"100014281\" data-ratio=\"0.46111111111111114\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/3_e4g6NSoshhUrbFBE3QRC5ib6Tbcu1w.png?resize=560%2C258\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAwaOtnqQlN0kicqNFL8Q1tVSDGotXFXHX00Cib3N1TpDxd0L0Fe4g6NSoshhUrbFBE3QRC5ib6Tbcu1w\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" height=\"258 .3900146484375\" width=\"560 .010009765625\" data-recalc-dims=\"1\" \/><\/span><\/p>\n\u5b89\u88c5<\/span><\/strong><\/span><o:page><\/o:page><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"cpp\"><code>install.packages(c(\"devtools\"<\/span>, \"dplyr\"<\/span>,\"forcats\"<\/span>,\"ggplot2\"<\/span>)) <\/span><\/code><code>devtools::install_github(\"ytakemon\/GRETTA\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n<section style=\"font-size: 16px;margin-bottom: 0px;\">\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 100%;vertical-align: top;align-self: flex-start;flex: 0 0 auto;background-color: rgb(69, 107, 176);padding-right: 6px;padding-bottom: 6px;padding-left: 6px;\">\n<section style=\"margin-top: 10px;margin-bottom: 10px;transform: translate3d(19px, 0px, 0px);\" powered-by=\"xiumi.us\">\n<section style=\"text-align: justify;color: rgb(255, 255, 255);\">\n <\/p>\n<\/section>\n<\/section>\n<section style=\"justify-content: flex-start;display: flex;flex-flow: row;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 100%;vertical-align: top;align-self: flex-start;flex: 0 0 auto;background-color: rgb(255, 255, 255);padding: 23px;\">\n<section style=\"text-align: justify;\" powered-by=\"xiumi.us\">\n\u516c\u4f17\u53f7\u540e\u53f0\u56de\u590d\u201c111\u201d<\/span>\u9886\u53d6\u672c\u7bc7\u4ee3\u7801\u3001\u57fa\u56e0\u96c6\u6216\u793a\u4f8b\u6570\u636e\u7b49\u6587\u4ef6\uff1b<\/p>\n\u6587\u4ef6\u7f16\u53f7\uff1a231207<\/strong><\/em><\/span><\/p>\n\u679c\u7c89\u798f\u5229<\/span><\/strong>\uff1a\u751f\u4fe1\u4eba\u5fc5\u5907\u795e\u5668\u2014\u2014\u670d\u52a1\u5668<\/strong><\/span> <\/p>\n\u5e73\u65f6\u751f\u4fe1\u5206\u6790\u5b66\u4e60\u4e2d\u6709\u8981\u7684\u5c0f\u4f19\u4f34\u53ef\u4ee5\u8054\u7cfb\u5c0f\u679c\u79df\u8d41\uff0c\u7c89\u4e1d\u798f\u5229\u90fd\u662f\u5e02\u573a\u8d85\u4f4e\u4ef7\u683c\uff0c\u8d76\u5feb\u627e\u5c0f\u679c\u9886\u53d6\u514d\u8d39\u7684\u8bd5\u7528\u8d26\u53f7\u5427\uff01<\/p>\n<a target=\"_blank\" href=\"http:\/\/mp.weixin.qq.com\/s?__biz=Mzg3OTg3MjQyNg==&mid=2247495825&idx=2&sn=a22a72496a1c082ea5b827be94ce4d99&chksm=cf7f6054f808e94264e14bfed4359cab3fcaaca249952a9f761c37ece352dcd7108579788b89&scene=21#wechat_redirect\" textvalue=\"\u670d\u52a1\u5668\u4ef7\u683c\u914d\u7f6e\u8868\uff08\u70b9\u51fb\u94fe\u63a5\u67e5\u770b\uff09\" linktype=\"text\" imgurl=\"\" imgdata=\"null\" data-itemshowtype=\"0\" tab=\"innerlink\" data-linktype=\"2\" rel=\"noopener\">\u670d\u52a1\u5668\u4ef7\u683c\u914d\u7f6e\u8868\uff08\u70b9\u51fb\u94fe\u63a5\u67e5\u770b\uff09<\/a><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n\u5de5\u4f5c\u6d41\u7a0b<\/strong><\/span><\/p>\n\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528<\/strong><\/span><\/p>\n1.\u5b89\u88c5\u201cGRETTA\u201d\u5e76\u4e0b\u8f7d\u9644\u5e26\u7684\u6570\u636e\u3002<\/span><\/p>\n2.\u9009\u62e9\u57fa\u56e0\u611f\u5174\u8da3\u7684\u7a81\u53d8\u7ec6\u80de\u7cfb\u548c\u5bf9\u7167\u7ec6\u80de\u7cfb\u3002<\/span><\/p>\n\u2013\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<\/span><\/p>\n3.\u786e\u5b9a\u7a81\u53d8\u7ec6\u80de\u7cfb\u548c\u5bf9\u7167\u7ec6\u80de\u7cfb\u7ec4\u4e4b\u95f4\u7684\u5dee\u5f02\u8868\u8fbe\u3002<\/span><\/p>\n\u2013\uff08\u53ef\u9009\u4f46\u63a8\u8350\uff09\u3002<\/span><\/p>\n4.\u8fdb\u884c<\/span>in silico<\/span>\u57fa\u56e0\u7b5b\u67e5\u3002<\/span><\/span><\/p>\n5.\u53ef\u89c6\u5316\u7ed3\u679c\u3002<\/span> <\/span><o:page><\/o:page><\/p>\n\u534f\u540c\u5fc5\u9700\uff08co-essential\uff09\u7f51\u7edc<\/strong><\/span><\/p>\n1.\u5b89\u88c5\u201cGRETTA\u201d\u5e76\u4e0b\u8f7d\u9644\u5e26\u7684\u8d44\u6599\u3002<\/span><\/p>\n2.\u8fd0\u884c\u76f8\u5173\u6027\u7cfb\u6570\u5206\u6790\u3002<\/span><\/p>\n\u2013\u53ef\u4ee5\u7528\u4e8e\u5728\u7279\u5b9a\u75be\u75c5\u7c7b\u578b\u7684\u7ec6\u80de\u7cfb\u4e2d\u8fdb\u884c\u7684\u5206\u6790\u3002<\/span><\/p>\n3.\u8ba1\u7b97\u8d1f\/\u6b63\u66f2\u7ebf\u7684\u62d0\u70b9\u4ee5\u786e\u5b9a\u9608\u503c\u3002<\/span><\/p>\n4.\u5e94\u7528\u9608\u503c\u3002<\/span><\/p>\n5.\u53ef\u89c6\u5316\u7ed3\u679c\u3002<\/span><\/p>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100014485\" data-ratio=\"0.38981481481481484\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/4_IWicymEetCc6Enf2BkUE4Yz1Ya5e5w-5.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyuYsqlJnQUJvnS2iciaDibtfbdV0VmHlriaSjYwjFPLp1BLjokHIWicymEetCc6Enf2BkUE4Yz1Ya5e5w\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" style=\"letter-spacing: 0.578px;text-wrap: wrap;color: rgb(34, 34, 38);font-family: "Times New Roman";font-size: 15px;font-weight: 700;background-color: rgb(255, 255, 255);text-align: center;\" data-recalc-dims=\"1\" \/><\/strong><\/span><\/p>\n\u4e00\u4e2a\u4f8b\u5b50\uff1a\u9274\u5b9aARID1A\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528<\/strong><\/span><\/p>\nARID1A<\/span>\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<\/span>ARID1A<\/span>\u53ca\u5176\u540c\u6e90\u7269<\/span>ARID1B<\/span>\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<\/span>Helming\u7b49\uff0c2014<\/span>\uff09\u3002\u8fd9\u4e2a\u4f8b\u5b50\u5c06\u5c55\u793a\u5982\u4f55\u4f7f\u7528<\/span>GRETTA\u9274\u5b9a<\/span>ARID1A<\/span>\u7684\u5408\u6210\u81f4\u6b7b\u4e92\u4f5c\u7269\u5e76\u9884\u6d4b\u8fd9\u4e00\u5df2\u77e5\u7684\u76f8\u4e92\u4f5c\u7528\u3002 \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\u7528<\/span>install.packages()\uff08\u4f8b\u5982<\/span>install.packages(“dplyr”)\uff09\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"shell\"><code># Load library <\/span><\/span><\/code><code>library(tidyverse) <\/span><\/span><\/code><code>#> \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 <\/span><\/span><\/code><code>#> \u2714 ggplot2 3.4.1 \u2714 purrr 1.0.1 <\/span><\/span><\/code><code>#> \u2714 tibble 3.2.1 \u2714 dplyr 1.1.1 <\/span><\/span><\/code><code>#> \u2714 tidyr 1.3.0 \u2714 stringr 1.5.0 <\/span><\/span><\/code><code>#> \u2714 readr 2.1.4 \u2714 forcats 1.0.0 <\/span><\/span><\/code><code>#> \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 <\/span><\/span><\/code><code>#> \u2716 dplyr::filter() masks stats::filter() <\/span><\/span><\/code><code>#> \u2716 dplyr::lag() masks stats::lag() <\/span><\/span><\/code><code>library(GRETTA) <\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> _______ .______ _______ .___________.___________. ___ <\/span><\/span><\/code><code>#> \/ _____|| _ | ____|| | | \/ <\/span><\/span><\/code><code>#> | | __ | |_) | | |__ `---| |----`---| |----` \/ ^ <\/span><\/span><\/code><code>#> | | |_ | | \/ | __| | | | | \/ \/_ <\/span><\/span><\/code><code>#> | |__| | | | ----.| |____ | | | | \/ _____ <\/span><\/span><\/code><code>#> ______| | _| `._____||_______| |__| |__| \/__\/ __ <\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> Welcome to GRETTA! The version loaded is: 0.99.2 <\/span><\/span><\/code><code>#> The latest DepMap dataset accompanying this package is v22Q2. <\/span><\/span><\/code><code>#> Please refer to our tutorial on GitHub for loading DepMap data and details: https:\/\/github.com\/ytakemon\/GRETTA<\/span><\/span><\/code><\/pre>\n<\/section>\n\u4e0b\u8f7d\u793a\u4f8b\u6570\u636e<\/strong><\/span><\/p>\n\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<\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"nginx\"><code>path <- getwd() <\/span><\/span><\/code><code>download_example_data(path) <\/span><\/span><\/code><code>#> Data saved to: \/projects\/marralab\/ytakemon_prj\/DepMap\/GRETTA\/GRETTA_example\/<\/span><\/span><\/code><\/pre>\n<\/section>\n<p style=\"mso-style-name: 'Source Code';margin-top: 0.0pt;margin-bottom: 10.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;word-break: break-all;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\"><span style=\"font-size: 15px;font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;letter-spacing: 0.034em;text-align: justify;\">\u7136\u540e\uff0c\u5206\u914d\u53d8\u91cf\u6307\u5411\u5b58\u50a8 <\/span>.rda \u6587\u4ef6\u7684\u4f4d\u7f6e\u548c\u7ed3\u679c\u6587\u4ef6\u5e94\u8be5\u5b58\u50a8\u7684\u4f4d\u7f6e\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"nginx\"><code>gretta_data_dir <- paste0(path,\"\/GRETTA_example\/\") <\/span><\/span><\/code><code>gretta_output_dir <- paste0(path,\"\/GRETTA_example_output\/\")<\/span><\/span><\/code><\/pre>\n<\/section>\n\u63a2\u7d22\u7ec6\u80de\u7cfb<\/strong><\/span><\/p>\n\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<\/span>https:\/\/depmap.org\/portal\/<\/span>\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\u62ec<\/span>list_mutations(), <\/span>list_cancer_types(), <\/span>list_cancer_subtypes()\u3002 \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<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"shell\"><code>## Find ARID1A hotspot mutations detected in all cell lines <\/span><\/span><\/code><code>list_mutations(gene = \"ARID1A\", is_hotspot = TRUE, data_dir = gretta_data_dir)<\/span><\/span><\/code><code>## List all available cancer types <\/span><\/span><\/code><code>list_cancer_types(data_dir = gretta_data_dir) <\/span><\/span><\/code><code>#> [1] \"Kidney Cancer\" \"Leukemia\"<\/span><\/span><\/code><code>#> [3] \"Lung Cancer\" \"Non-Cancerous\"<\/span><\/span><\/code><code>#> [5] \"Sarcoma\" \"Lymphoma\"<\/span><\/span><\/code><code>#> [7] \"Colon\/Colorectal Cancer\" \"Pancreatic Cancer\"<\/span><\/span><\/code><code>#> [9] \"Gastric Cancer\" \"Rhabdoid\"<\/span><\/span><\/code><code>#> [11] \"Endometrial\/Uterine Cancer\" \"Esophageal Cancer\"<\/span><\/span><\/code><code>#> [13] \"Breast Cancer\" \"Brain Cancer\"<\/span><\/span><\/code><code>#> [15] \"Ovarian Cancer\" \"Bone Cancer\"<\/span><\/span><\/code><code>#> [17] \"Myeloma\" \"Head and Neck Cancer\"<\/span><\/span><\/code><code>#> [19] \"Bladder Cancer\" \"Skin Cancer\"<\/span><\/span><\/code><code>#> [21] \"Bile Duct Cancer\" \"Prostate Cancer\"<\/span><\/span><\/code><code>#> [23] \"Cervical Cancer\" \"Thyroid Cancer\"<\/span><\/span><\/code><code>#> [25] \"Neuroblastoma\" \"Eye Cancer\"<\/span><\/span><\/code><code>#> [27] \"Liposarcoma\" \"Gallbladder Cancer\"<\/span><\/span><\/code><code>#> [29] \"Teratoma\" \"Unknown\"<\/span><\/span><\/code><code>#> [31] \"Liver Cancer\" \"Adrenal Cancer\"<\/span><\/span><\/code><code>#> [33] \"Embryonal Cancer\"<\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## List all available cancer subtypes <\/span><\/span><\/code><code>list_cancer_subtypes(input_disease = \"Lung Cancer\", data_dir = gretta_data_dir) <\/span><\/span><\/code><code>#> [1] \"Non-Small Cell Lung Cancer (NSCLC), Adenocarcinoma\"<\/span><\/span><\/code><code>#> [2] \"Small Cell Lung Cancer (SCLC)\"<\/span><\/span><\/code><code>#> [3] \"Non-Small Cell Lung Cancer (NSCLC), Squamous Cell Carcinoma\"<\/span><\/span><\/code><code>#> [4] \"Mesothelioma\"<\/span><\/span><\/code><code>#> [5] \"Non-Small Cell Lung Cancer (NSCLC), Large Cell Carcinoma\"<\/span><\/span><\/code><code>#> [6] NA <\/span><\/span><\/code><code>#> [7] \"Non-Small Cell Lung Cancer (NSCLC), unspecified\"<\/span><\/span><\/code><code>#> [8] \"Non-Small Cell Lung Cancer (NSCLC), Adenosquamous Carcinoma\"<\/span><\/span><\/code><code>#> [9] \"Carcinoid\"<\/span><\/span><\/code><code>#> [10] \"Non-Small Cell Lung Cancer (NSCLC), Mucoepidermoid Carcinoma\"<\/span><\/span><\/code><code>#> [11] \"Carcinoma\"<\/span><\/span><\/code><\/pre>\n<\/section>\n\u9009\u62e9\u7a81\u53d8\u7ec6\u80de\u7cfb\u548c\u5bf9\u7167\u7ec6\u80de\u7cfb\u7ec4<\/strong><\/span><\/p>\n\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c<\/span>select_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 \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 \u9ed8\u8ba4\u5206\u914d\u7684\u7ec6\u80de\u7cfb\u7ec4\u662f\uff1a<\/span><\/span><\/p>\n\u2022<\/span>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<\/span><\/span><\/p>\n\u2022<\/span>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<\/span><\/span><\/p>\n\u2022<\/span>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<\/span><\/span><\/p>\n\u2022<\/span>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<\/span> <\/span><o:page><\/o:page><\/p>\n\u2022<\/span>Amplified\u7ec6\u80de\u7cfb\u4e0d\u542b\u6709\u62f7\u8d1d\u6570\u589e\u52a0\u7684\u6709\u5bb3SNVs\/InDels\u3002<\/span><\/span><\/p>\n\u2022<\/span>Others\u7ec6\u80de\u7cfb\u542b\u6709\u6709\u5bb3SNVs\uff0c\u5176\u62f7\u8d1d\u6570\u589e\u52a0\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"shell\"><code>ARID1A_groups <- select_cell_lines(input_gene = \"ARID1A\", data_dir = gretta_data_dir) <\/span><\/span><\/code><code>#> Selecting mutant groups for: ARID1A in all cancer cell lines <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## Show number of cell lines in each group <\/span><\/span><\/code><code>count(ARID1A_groups, Group) <\/span><\/span><\/code><code>#> # A tibble: 5 \u00d7 2 <\/span><\/span><\/code><code>#> Group n <\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> 1 ARID1A_HetDel 61 <\/span><\/span><\/code><code>#> 2 ARID1A_HomDel 23 <\/span><\/span><\/code><code>#> 3 ARID1A_T-HetDel 30 <\/span><\/span><\/code><code>#> 4 Control 906 <\/span><\/span><\/code><code>#> 5 Others 66<\/span><\/span><\/code><\/pre>\n<\/section>\n\u53ef\u9009\u7684\u7ec6\u80de\u7cfb\u8fc7\u6ee4\u5668<\/strong><\/span><\/p>\n\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<\/span><\/p>\n\u2022<\/span>input_aa_change – \u901a\u8fc7\u6c28\u57fa\u9178\u53d8\u5316\uff08\u4f8b\u5982\u201cp.Q515*\u201d\uff09\u3002<\/span><\/span><\/p>\n\u2022<\/span>input_disease – \u901a\u8fc7\u75be\u75c5\u7c7b\u578b\uff08\u4f8b\u5982\u201c\u80f0\u817a\u764c\u201d\uff09<\/span><\/span><\/p>\n\u2022<\/span>input_disease_subtype – \u901a\u8fc7\u75be\u75c5\u4e9a\u578b\uff08\u4f8b\u5982\u201c\u5bfc\u7ba1\u817a\u9cde\u72b6\u7ec6\u80de\u764c\u201d\uff09<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"shell\"><code>## Find pancreatic cancer cell lines with ARID1A mutations <\/span><\/span><\/code><code>ARID1A_pancr_groups <- select_cell_lines(input_gene = \"ARID1A\", <\/span><\/span><\/code><code> input_disease = \"Pancreatic Cancer\", <\/span><\/span><\/code><code> data_dir = gretta_data_dir) <\/span><\/span><\/code><code>#> Selecting mutant groups for: ARID1A in Pancreatic Cancer, cell lines <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## Show number of cell lines in each group <\/span><\/span><\/code><code>count(ARID1A_pancr_groups, Group) <\/span><\/span><\/code><code>#> # A tibble: 4 \u00d7 2 <\/span><\/span><\/code><code>#> Group n <\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> 1 ARID1A_HetDel 5 <\/span><\/span><\/code><code>#> 2 ARID1A_HomDel 4 <\/span><\/span><\/code><code>#> 3 Control 36 <\/span><\/span><\/code><code>#> 4 Others 2<\/span><\/span><\/code><\/pre>\n<\/section>\n\u68c0\u6d4b\u5dee\u5f02\u8868\u8fbe<\/strong><\/span><\/p>\n\u5728\u4e09\u79cd\u7a81\u53d8\u764c\u7ec6\u80de\u7cfb\u7ec4 <\/span>ARID1A_HomDel\u3001<\/span>ARID1A_T-HetDel \u548c <\/span>ARID1A_HetDel \u4e2d\uff0c\u5177\u6709 <\/span>ARID1A_HomDel \u7a81\u53d8\u7684\u764c\u7ec6\u80de\u7cfb\u6700\u53ef\u80fd\u5bfc\u81f4 <\/span>ARID1A<\/span> \u7684\u4e22\u5931\u6216\u8868\u8fbe\u51cf\u5c11\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u60f3\u8981\u68c0\u67e5 <\/span>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 <\/span>ARID1A<\/span> RNA \u6216\u86cb\u767d\u8d28\u8868\u8fbe\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"powershell\"><code>## Select only HomDel and Control cell lines <\/span><\/span><\/code><code>ARID1A_groups_subset <- ARID1A_groups %>% filter(Group %in% c(\"ARID1A_HomDel\", \"Control\")) <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## Get RNA expression <\/span><\/span><\/code><code>ARID1A_rna_expr <- extract_rna( <\/span><\/span><\/code><code> input_samples = ARID1A_groups_subset$DepMap_ID, <\/span><\/span><\/code><code> input_genes = \"ARID1A\", <\/span><\/span><\/code><code> data_dir = gretta_data_dir) <\/span><\/span><\/code><code>#> 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<\/span><\/span><\/code><\/pre>\n<\/section>\n<p style=\"mso-style-name: 'Source Code';margin-top: 0.0pt;margin-bottom: 10.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;word-break: break-all;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\">\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<\/span>DepMap\u7f51\u7ad9<\/span>\u3002\uff09<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"properties\"><code>## Get protein expression <\/span><\/span><\/code><code>ARID1A_prot_expr <- extract_prot( <\/span><\/span><\/code><code>input_samples = ARID1A_groups_subset$DepMap_ID, <\/span><\/span><\/code><code>input_genes = \"ARID1A\", <\/span><\/span><\/code><code>data_dir = gretta_data_dir) <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## Produces an error message since ARID1A protein data is not available<\/span><\/span><\/code><\/pre>\n<\/section>\n\u4f7f\u7528Welch\u7684t\u68c0\u9a8c\uff0c\u6211\u4eec\u53ef\u4ee5\u68c0\u67e5\u76f8\u5bf9\u4e8e\u5bf9\u7167\u7ec6\u80de\u7cfb\uff0c<\/span>ARID1A_HomDel\u7ec6\u80de\u7cfb\u4e2d\u7684<\/span>ARID1A RNA\u8868\u8fbe\u91cf\uff08\u4ee5TPM\u4e3a\u5355\u4f4d\uff09\u662f\u5426\u663e\u8457\u964d\u4f4e\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"shell\"><code>## Append groups and test differential expression <\/span><\/span><\/code><code>ARID1A_rna_expr <- left_join( <\/span><\/span><\/code><code> ARID1A_rna_expr, <\/span><\/span><\/code><code> ARID1A_groups_subset %>% select(DepMap_ID, Group)) %>% <\/span><\/span><\/code><code> mutate(Group = fct_relevel(Group,\"Control\")) # show Control group first <\/span><\/span><\/code><code>#> Joining with `by = join_by(DepMap_ID)` <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## T-test <\/span><\/span><\/code><code>t.test(ARID1A_8289 ~ Group, ARID1A_rna_expr) <\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> Welch Two Sample t-test <\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> data: ARID1A_8289 by Group <\/span><\/span><\/code><code>#> t = 2.5764, df = 22.873, p-value = 0.01692 <\/span><\/span><\/code><code>#> alternative hypothesis: true difference in means between group Control and group ARID1A_HomDel is not equal to 0 <\/span><\/span><\/code><code>#> 95 percent confidence interval: <\/span><\/span><\/code><code>#> 0.1146094 1.0498810 <\/span><\/span><\/code><code>#> sample estimates: <\/span><\/span><\/code><code>#> mean in group Control mean in group ARID1A_HomDel <\/span><\/span><\/code><code>#> 4.635784 4.053539 <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## plot <\/span><\/span><\/code><code>ggplot(ARID1A_rna_expr, aes(x = Group, y = ARID1A_8289)) + <\/span><\/span><\/code><code> geom_boxplot()<\/span><\/span><\/code><\/pre>\n<\/section>\n<img decoding=\"async\" loading=\"lazy\" class=\"rich_pages wxw-img\" data-imgfileid=\"100014278\" data-ratio=\"0.7148148148148148\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/5_mUbL57XEoELxhj4lvADBMwfHRz0Gxw.png?resize=560%2C400\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAwaOtnqQlN0kicqNFL8Q1tVST9AujhNfgtxla0bhibJGqbI1amUbL57XEoELxhj4lvADBMwfHRz0Gxw\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" height=\"400 .17999267578125\" width=\"560 .010009765625\" data-recalc-dims=\"1\" \/><\/span> <\/span><o:page><\/o:page><\/p>\n\u8fdb\u884c\u5168\u57fa\u56e0\u7ec4in silico\u9057\u4f20\u7b5b\u9009<\/strong><\/span><\/p>\n\u5728\u786e\u5b9a<\/span>ARID1A_HomDel\u7ec4\u4e2d\u7684\u7ec6\u80de\u7cfb\u4e0e<\/span>Control\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\u7528<\/span>screen_results()\u6267\u884c<\/span>in silico<\/span>\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 <\/span>\u81f4\u6b7b\u6982\u7387\uff08Lethality probabilities\uff09<\/span>\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<\/span>Meyers, R., et al., 2017<\/span>\u3002 <\/span>screen_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<\/span><\/span><\/p>\n\u2022GeneName_ID\uff1aHugo symbol\u5e26\u6709NCBI\u57fa\u56e0ID<\/span><\/p>\n\u2022GeneNames\uff1aHugo symbol<\/span><\/p>\n\u2022<\/span>median\uff0c<\/span>mean\uff0c<\/span>sd\uff0c<\/span>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<\/span><\/span><\/p>\n\u2022Pval\uff1a\u63a7\u5236\u548c\u7a81\u53d8\u7ec4\u4e4b\u95f4\u7684Mann-Whitney U\u68c0\u9a8c\u7684P\u503c\u3002<\/span><\/p>\n\u2022Adj_pval\uff1a\u7ecf\u8fc7BH\u6821\u6b63\u7684P\u503c\u3002<\/span><\/p>\n\u2022log2FC_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<\/span><\/p>\n\u2022log2FC_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<\/span><\/p>\n\u2022CliffDelta\uff1a\u7a81\u53d8\u548c\u5bf9\u7167\u4f9d\u8d56\u6982\u7387\u4e4b\u95f4\u7684Cliff’s delta\u975e\u53c2\u6570\u6548\u5e94\u5927\u5c0f\u3002\u8303\u56f4\u4ecb\u4e8e-1\u52301\u4e4b\u95f4\u3002<\/span><\/p>\n\u2022dip_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<\/span> <\/span><o:page><\/o:page><\/p>\n\u2022Interaction_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<\/span><\/p>\n\u6ce8\u610f<\/span> \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\u679c\u4e0b\u9762\u7684\u4f8b\u5b50 <\/span>GI_screen() \u82b1\u4e86\u5927\u7ea62\u4e2a\u5c0f\u65f6\u6765\u5904\u7406\u3002\u4e3a\u4e86\u8282\u7701\u65f6\u95f4\uff0c\u5c0f\u679c\u5df2\u7ecf\u4e3a\u60a8\u9884\u5148\u5904\u7406\u4e86\u8fd9\u4e00\u6b65\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"bash\"><code>ARID1A_mutant_id <- ARID1A_groups %>% filter(Group %in% c(\"ARID1A_HomDel\")) %>% pull(DepMap_ID) <\/span><\/span><\/code><code>ARID1A_control_id <- ARID1A_groups %>% filter(Group %in% c(\"Control\")) %>% pull(DepMap_ID) <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## See warning above. <\/span><\/span><\/code><code>## This can take several hours depending on number of lines\/cores used. <\/span><\/span><\/code><code>screen_results <- GI_screen( <\/span><\/span><\/code><code> control_id = ARID1A_control_id, <\/span><\/span><\/code><code> mutant_id = ARID1A_mutant_id, <\/span><\/span><\/code><code> core_num = 5, # depends on how many cores you have <\/span><\/span><\/code><code> output_dir = gretta_output_dir, # Will save your results here as well as in the variable <\/span><\/span><\/code><code> data_dir = gretta_data_dir, <\/span><\/span><\/code><code>test = FALSE) # use TRUE to run a short test to make sure all will run overnight.<\/span><\/span><\/code><code>## Load prepared ARID1A screen result <\/span><\/span><\/code><code>load(paste0(gretta_data_dir,\"\/sample_22Q2_ARID1A_KO_screen.rda\"), envir = environment())<\/span><\/span><\/code><\/pre>\n<\/section>\n\u6211\u4eec\u53ef\u4ee5\u5feb\u901f\u786e\u5b9a<\/span>GRETTA\u662f\u5426\u9884\u6d4b\u4e86\u4efb\u4f55\u81f4\u6b7b\u6027\u9057\u4f20\u76f8\u4e92\u4f5c\u7528\u3002\u6211\u4eec\u4f7f\u7528<\/span>Pval\u622a\u65ad\u503c\u4e3a0.05\uff0c\u5e76\u6839\u636e<\/span>Interaction_score\u8fdb\u884c\u6392\u540d\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"css\"><code>screen_results %>% <\/span><\/span><\/code><code>filter(Pval < 0.05) %>% <\/span><\/span><\/code><code>arrange(-Interaction_score) %>% <\/span><\/span><\/code><code>select(GeneNames:Mutant_median, Pval, Interaction_score) %>% head<\/span><\/span><\/code><code>#> # A tibble: 6 \u00d7 5 <\/span><\/span><\/code><code>#> GeneNames Control_median Mutant_median Pval Interaction_score<\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> 1 ARID1B 0.0579 0.515 6.84e-10 9.16<\/span><\/span><\/code><code>#> 2 CCDC110 0.0165 0.0303 3.54e- 4 3.45<\/span><\/span><\/code><code>#> 3 APOO 0.0168 0.0283 9.61e- 4 3.02<\/span><\/span><\/code><code>#> 4 NHS 0.0352 0.0539 9.69e- 4 3.01<\/span><\/span><\/code><code>#> 5 SLC66A2 0.00793 0.0134 1.06e- 3 2.98<\/span><\/span><\/code><code>#> 6 ATXN7L1 0.0138 0.0259 1.78e- 3 2.75<\/span><\/span><\/code><\/pre>\n<\/section>\n<p style=\"mso-style-name: 'Source Code';margin-top: 0.0pt;margin-bottom: 10.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;word-break: break-all;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\"><span style=\"font-size: 15px;font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;letter-spacing: 0.034em;text-align: justify;\">\u5c0f\u679c\u53ef\u4ee5\u53d1\u73b0\uff0c<\/span>ARID1B<\/span>\uff0c\u4e00\u79cd\u5df2\u77e5\u7684\u4e0e<\/span>ARID1A<\/span>\u7684\u5408\u6210\u81f4\u6b7b\u76f8\u4e92\u4f5c\u7528\uff0c\u662f\u6b64\u5217\u8868\u7684\u9876\u90e8\u3002<\/span> <\/span><\/p>\n<o:page><\/o:page><\/p>\n\u53ef\u9009\u6b65\u9aa4\uff1a\u8fdb\u884c\u5c0f\u89c4\u6a21\u7b5b\u9009<\/strong><\/span><\/span><\/p>\n\u8981\u8fdb\u884c\u5c0f\u578b\u4f53\u5185\u7b5b\u9009\uff0c\u53ef\u4ee5\u5728 <\/span>gene_list = \u53c2\u6570\u4e2d\u63d0\u4f9b\u57fa\u56e0\u5217\u8868\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"nginx\"><code>small_screen_results <- GI_screen( <\/span><\/span><\/code><code> control_id = ARID1A_control_id, <\/span><\/span><\/code><code> mutant_id = ARID1A_mutant_id, <\/span><\/span><\/code><code> gene_list = c(\"ARID1A\", \"ARID1B\", \"SMARCA2\", \"GAPDH\", \"SMARCC2\"), <\/span><\/span><\/code><code> core_num = 5, # depends on how many cores you have <\/span><\/span><\/code><code> output_dir = gretta_output_dir, # Will save your results here as well as in the variable <\/span><\/span><\/code><code> data_dir = gretta_data_dir)<\/span><\/span><\/code><\/pre>\n<\/section>\n\u53ef\u89c6\u5316\u7b5b\u9009\u7ed3\u679c<\/strong><\/span><\/p>\n\u6700\u540e\uff0c\u4e00\u65e6\u5b8c\u6210<\/span>\u8ba1\u7b97\u673a\u7b5b\u9009<\/span>\uff0c\u5c31\u53ef\u4ee5\u4f7f\u7528<\/span>plot_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\u679c\u786e\u5b9a\u4e86<\/span>ARID1B<\/span>\u662f<\/span>ARID1A<\/span>\u7684\u5408\u6210\u81f4\u6b7b\u76f8\u4e92\u4f5c\u7528\u7269\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"shell\"><code>## Visualize results, turn on gene labels, <\/span><\/span><\/code><code>## and label three genes each that are predicted to have <\/span><\/span><\/code><code>## lethal and alleviating genetic interactions, respectively <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>plot_screen(result_df = screen_results, <\/span><\/span><\/code><code> label_genes = TRUE, <\/span><\/span><\/code><code> label_n = 3) <\/span><\/span><\/code><code>#> Warning: Removed 7 rows containing missing values (`geom_point()`).<\/span><\/span><\/code><\/pre>\n<\/section>\n<p style=\"mso-style-name: 'Source Code';margin-top: 0.0pt;margin-bottom: 10.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;word-break: break-all;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\"><o:page><\/o:page><\/p>\n<img decoding=\"async\" loading=\"lazy\" class=\"rich_pages wxw-img\" data-imgfileid=\"100014279\" data-ratio=\"0.7148148148148148\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/6_CrlwIibzr30hN5VpLqZXDzibJCrURg.png?resize=560%2C400\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAwaOtnqQlN0kicqNFL8Q1tVSuxYTV7uoW5QibI6ORJEraxybiaCACrlwIibzr30hN5VpLqZXDzibJCrURg\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" height=\"400 .5\" width=\"560 .010009765625\" data-recalc-dims=\"1\" \/><\/span><\/p>\n\u793a\u4f8b2\uff1a\u9274\u5b9aARID1A\u534f\u540c\u5fc5\u9700\uff08co-essential\uff09\u57fa\u56e0<\/strong><\/span><\/p>\n\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<\/span>Wainberg et al. 2021<\/span>; <\/span>Pan et al. 2018<\/span>\uff09\u3002 \u8003\u8651\u5230ARID1A\u662f\u54fa\u4e73\u52a8\u7269SWI\/SNF\u590d\u5408\u7269\u5df2\u77e5\u7684\u4e9a\u57fa(<\/span>Mashtalir\u7b492018<\/span>)\uff0c\u5c0f\u679c\u9884\u8ba1SWI\/SNF\u590d\u5408\u7269\u7684\u6210\u5458\u5c06\u4e0e<\/span>ARID1A<\/span>\u5171\u4eab\u5171\u540c\u5fc5\u9700\u6027\u3002\u672c\u793a\u4f8b\u5c06\u5c55\u793a\u5982\u4f55\u4f7f\u7528<\/span>GRETTA\u7ed8\u5236<\/span>ARID1A<\/span>\u7684\u5171\u5fc5\u9700\u57fa\u56e0\u7f51\u7edc\u3002<\/span><\/span><\/p>\n\u9274\u5b9a\u5177\u6709\u6700\u9ad8\u76f8\u5173\u6027\u7cfb\u6570\u7684\u57fa\u56e0<\/strong><\/span><\/p>\n\u4e3a\u4e86\u786e\u5b9a\u5171\u5fc5\u9700\u57fa\u56e0\uff0c\u6211\u4eec\u5c06\u5bf9<\/span>ARID1A<\/span>\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<\/span>SMARCE1<\/span>\u548c<\/span>SMARCB1<\/span>\u662f\u524d\u4e24\u4e2a\u5171\u5fc5\u9700\u57fa\u56e0\u3002<\/span> <\/span><o:page><\/o:page><\/p>\n\u6ce8\u610f<\/span> \u6b64\u8fc7\u7a0b\u53ef\u80fd\u9700\u8981\u51e0\u5206\u949f\u7684\u65f6\u95f4\u3002\u5c0f\u679c\u4e0b\u9762\u7684\u793a\u4f8b <\/span>coessential_map() + <\/span>get_inflection_points() \u5904\u7406\u5927\u7ea6\u9700\u898117\u5206\u949f\u7684\u65f6\u95f4\u3002\u4e3a\u4e86\u8282\u7701\u65f6\u95f4\uff0c\u5c0f\u679c\u5df2\u7ecf\u4e3a\u60a8\u9884\u5148\u5904\u7406\u4e86\u8fd9\u4e00\u6b65\u9aa4\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"properties\"><code>## Map co-essential genes <\/span><\/span><\/code><code>coess_df <- coessential_map( <\/span><\/span><\/code><code>input_gene = \"ARID1A\", <\/span><\/span><\/code><code>data_dir = gretta_data_dir, <\/span><\/span><\/code><code>output_dir = gretta_output_dir) <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## Calculate inflection points of positive and negative curve using co-essential gene results. <\/span><\/span><\/code><code>coess_inflection_df <- get_inflection_points(coess_df)<\/span><\/span><\/code><code>\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<\/span><\/span><\/code><\/pre>\n<\/section>\n<p style=\"mso-style-name: 'Source Code';margin-top: 0.0pt;margin-bottom: 10.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;word-break: break-all;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\">\u5316\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"properties\"><code>## Combine and annotate data frame containing co-essential genes <\/span><\/span><\/code><code>coess_annotated_df <- annotate_coess(coess_df, coess_inflection_df) <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>plot_coess( <\/span><\/span><\/code><code>result_df = coess_annotated_df, <\/span><\/span><\/code><code>inflection_df = coess_inflection_df, <\/span><\/span><\/code><code>label_genes = TRUE, # Should gene names be labeled? <\/span><\/span><\/code><code>label_n = 3) # Number of genes to display from each end<\/span><\/span><\/code><\/pre>\n<\/section>\n<p style=\"mso-style-name: 'Source Code';margin-top: 0.0pt;margin-bottom: 10.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;word-break: break-all;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\"> <\/p>\n<img decoding=\"async\" loading=\"lazy\" class=\"rich_pages wxw-img\" data-imgfileid=\"100014280\" data-ratio=\"0.7148148148148148\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/7_B1knaL7IS7c4fI6kMkwTA7K4Ydicug.png?resize=560%2C400\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAwaOtnqQlN0kicqNFL8Q1tVS8QRqXN2DZgeicaG9ia7OdEHzyecB1knaL7IS7c4fI6kMkwTA7K4Ydicug\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" height=\"400 .07000732421875\" width=\"560 .010009765625\" data-recalc-dims=\"1\" \/> \u5c0f\u679c\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<\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"css\"><code>## Show top 10 co-essential genes. <\/span><\/span><\/code><code>coess_annotated_df %>% arrange(Rank) %>% head(10) <\/span><\/span><\/code><code>#> # A tibble: 10 \u00d7 9 <\/span><\/span><\/code><code>#> GeneNameID_A GeneNameID_B estimate statistic p.value parameter Rank<\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> 1 ARID1A_8289 ARID1A_8289 1 Inf 0 1086 1 <\/span><\/span><\/code><code>#> 2 ARID1A_8289 SMARCB1_6598 0.477 17.9 7.45e-59 1086 2 <\/span><\/span><\/code><code>#> 3 ARID1A_8289 SMARCE1_6605 0.399 14.3 4.30e-39 1086 3 <\/span><\/span><\/code><code>#> 4 ARID1A_8289 SMARCC1_6599 0.369 13.1 9.35e-33 1086 4 <\/span><\/span><\/code><code>#> 5 ARID1A_8289 SS18_6760 0.332 11.6 4.85e-26 1086 5 <\/span><\/span><\/code><code>#> 6 ARID1A_8289 DPF2_5977 0.330 11.5 1.15e-25 1086 6 <\/span><\/span><\/code><code>#> 7 ARID1A_8289 SMARCD2_6603 0.270 9.22 1.10e-16 1086 7 <\/span><\/span><\/code><code>#> 8 ARID1A_8289 SMARCC2_6601 0.242 8.22 2.34e-13 1086 8 <\/span><\/span><\/code><code>#> 9 ARID1A_8289 BCL2_596 0.231 7.82 4.05e-12 1086 9 <\/span><\/span><\/code><code>#> 10 ARID1A_8289 CBFB_865 0.224 7.58 2.07e-11 1086 10 <\/span><\/span><\/code><code>#> # \u2139 2 more variables: Padj_BH, Candidate_gene<\/span><\/span><\/code><\/pre>\n<\/section>\n\u7279\u5b9a\u764c\u75c7\u7c7b\u578b\u7684\u53ef\u9009\u8fc7\u6ee4\u5668<\/strong><\/span><\/p>\n\u7528\u6237\u8fd8\u53ef\u4ee5\u4f7f\u7528<\/span>input_disease = “”\u9009\u9879\u6309\u75be\u75c5\u7c7b\u578b\u8fdb\u884c\u5b50\u96c6\u5212\u5206\uff0c\u6216\u4f7f\u7528<\/span>input_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\u679c\u63d0\u4f9b\u4e86\u4e00\u4e2aARID1A\u5fc5\u9700\u57fa\u56e0\u6620\u5c04\u7528\u4e8e\u80f0\u817a\u764c\u7684\u793a\u4f8b\u3002<\/span><\/span><\/p>\n\u6ce8\u610f<\/span> \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<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"properties\"><code>## Map co-essential genes in pancreatic cancers only <\/span><\/span><\/code><code>coess_df <- coessential_map( <\/span><\/span><\/code><code>input_gene = \"ARID1A\", <\/span><\/span><\/code><code>input_disease = \"Pancreatic Cancer\", <\/span><\/span><\/code><code>core_num = 5, ## Depending on how many cores you have access to, increase this value to shorten processing time. <\/span><\/span><\/code><code>data_dir = gretta_data_dir, <\/span><\/span><\/code><code>output_dir = gretta_output_dir, <\/span><\/span><\/code><code>test = FALSE)<\/span><\/span><\/code><\/pre>\n<\/section>\n\u9488\u5bf9\u81ea\u5b9a\u4e49\u7ec6\u80de\u7cfb\u7684\u53ef\u9009\u8fc7\u6ee4\u5668<\/strong><\/span><\/p>\n\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528 <\/span>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<\/span><\/span><\/p>\n\u6ce8\u610f<\/span> \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<\/span> <\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"properties\"><code>custom_lines <- c(\"ACH-000001\", \"ACH-000002\", \"ACH-000003\",...) <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>coess_df <- coessential_map( <\/span><\/span><\/code><code>input_gene = \"ARID1A\", <\/span><\/span><\/code><code>input_cell_lines = custom_lines, <\/span><\/span><\/code><code>core_num = 5, ## Depending on how many cores you have access to, increase this value to shorten processing time. <\/span><\/span><\/code><code>data_dir = gretta_data_dir, <\/span><\/span><\/code><code>output_dir = gretta_output_dir, <\/span><\/span><\/code><code>test = FALSE)<\/span><\/span><\/code><\/pre>\n<\/section>\n\u7ed3\u8bed<\/strong><\/span><\/p>\n\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<\/span><\/p>\n\u53c2\u8003\u6587\u732e<\/strong><\/span><\/p>\n[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.<\/span><\/p>\n<section powered-by=\"xiumi.us\" style=\"margin-bottom: 0px;\">\n<section>\n<section powered-by=\"xiumi.us\">\n<section>\n<section powered-by=\"xiumi.us\">\n<section>\n<section powered-by=\"xiumi.us\">\n<section>\n<section>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100014392\" data-ratio=\"0.5555555555555556\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/8_VmRkv7tSNKliafDnsuFR7BoeDgBrww-5.jpg?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_jpg\/zcicibWZessAxPqeNQsFjm2HibIuSld9Nh9GicQSWhJwHjia5tEevQvDIebLErVmRkv7tSNKliafDnsuFR7BoeDgBrww\/640?wx_fmt=jpeg&from=appmsg'\" data-type=\"jpeg\" data-w=\"900\" style=\"font-size: var(--articleFontsize);letter-spacing: 0.034em;\" data-recalc-dims=\"1\"> <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<p style=\"margin-top: 0pt;margin-bottom: 0pt;margin-left: 0pt;letter-spacing: 0.578px;text-indent: 0pt;text-wrap: wrap;text-align: justify;line-height: normal;font-size: 12pt;font-family: helvetica;color: rgb(0, 0, 0);break-after: auto;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-imgfileid=\"100014393\" data-ratio=\"0.38981481481481484\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/9_FbEg9fibwQicL8l2ExKOZZTmAHXabQ-6.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAx3HetBkB7aOGibFWKqibqIATyC4zFibzCgdrxFwRep6cDMLIbA0FbEg9fibwQicL8l2ExKOZZTmAHXabQ\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" style=\"font-size: var(--articleFontsize);letter-spacing: 0.034em;caret-color: rgba(0, 0, 0, 0);\" data-recalc-dims=\"1\"><\/p>\n<section 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data={\"mp\":\"\u751f\u4fe1\u679c\",\"title\":\"GRETTA\uff1a\u6316\u6398\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528\u548c\u534f\u540c\u5fc5\u9700\u7f51\u7edc\u7684\u597d\u5305\uff01\",\"time\":\"2023-12-29 19:00:39\",\"timeStamp\":\"1703847639\"};\n<\/script> \n<body><\/p>\n<div class=\"rich_media\">\n<h1 class=\"rich_media_title\" id=\"activity-name\"><a href=\"http:\/\/mp.weixin.qq.com\/s?__biz=Mzg3OTg3MjQyNg==&mid=2247498308&idx=1&sn=f764278795ad73f50be4638481e0719e&chksm=cf7f6a81f808e3978d245944ec80203aed7eac0663ae0eeb3bb4dd3d627780f6cafbf093f59e#rd\" target=\"_blank\" rel=\"noopener\">GRETTA\uff1a\u6316\u6398\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528\u548c\u534f\u540c\u5fc5\u9700\u7f51\u7edc\u7684\u597d\u5305\uff01<\/a><\/h1>\n<div id=\"meta_content\" class=\"rich_media_meta_list\">\u539f\u521b <\/span>\u5c0f\u679c <\/span><a href=\"javascript:void(0);\" class=\"wx_tap_link js_wx_tap_highlight weui-wa-hotarea\" id=\"js_name\">\u751f\u4fe1\u679c <\/a><\/span>2023-12-29 19:00:39<\/em><\/div>\n<content><\/p>\n<div class=\"topic\">\n\u6536\u5f55\u4e8e\u8bdd\u9898<\/p>\n<a href=\"https:\/\/mp.weixin.qq.com\/mp\/appmsgalbum?__biz=Mzg3OTg3MjQyNg==&action=getalbum&album_id=2812010014583390210#wechat_redirect\" title=\"R\u4ee3\u7801\" target=\"_blank\" rel=\"noopener\">#R\u4ee3\u7801<\/a><a href=\"https:\/\/mp.weixin.qq.com\/mp\/appmsgalbum?__biz=Mzg3OTg3MjQyNg==&action=getalbum&album_id=2664545260517294084#wechat_redirect\" title=\"\u751f\u4fe1\u5b9e\u64cd\" target=\"_blank\" rel=\"noopener\">#\u751f\u4fe1\u5b9e\u64cd<\/a><a href=\"https:\/\/mp.weixin.qq.com\/mp\/appmsgalbum?__biz=Mzg3OTg3MjQyNg==&action=getalbum&album_id=3138206666342268940#wechat_redirect\" title=\"\u751f\u4fe1\u5c0f\u767d\u76f8\u5173\" target=\"_blank\" rel=\"noopener\">#\u751f\u4fe1\u5c0f\u767d\u76f8\u5173<\/a><a href=\"https:\/\/mp.weixin.qq.com\/mp\/appmsgalbum?__biz=Mzg3OTg3MjQyNg==&action=getalbum&album_id=2821072943068839938#wechat_redirect\" title=\"\u751f\u4fe1\u7ed8\u56fe\" target=\"_blank\" rel=\"noopener\">#\u751f\u4fe1\u7ed8\u56fe<\/a><\/p>\n<\/div>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100014480\" data-ratio=\"0.23333333333333334\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/1_rE2v0O5WiaUDJjgv5bxK2G1ePHyH2g-59.jpg?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_jpg\/zcicibWZessAysHY49vPGCraibakiaNq6hQgn8C5MfvVLQGxlNZPEia2PajKAzrE2v0O5WiaUDJjgv5bxK2G1ePHyH2g\/640?wx_fmt=jpeg'\" data-type=\"jpeg\" data-w=\"1080\" style=\"letter-spacing: 0.578px;text-wrap: wrap;font-family: \u5b8b\u4f53;font-size: 15px;text-align: center;\" data-recalc-dims=\"1\" \/>\u4eca\u5929\u5c0f\u679c\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e2a<\/span>2023\u5e746\u67081\u65e5<\/span>\u53d1\u8868\u4e8e<\/span>bioinformatics<\/span>\u4e0a\u65b0\u9c9c\u51fa\u7089\u7684\u597d\u5305\u2014\u2014GRETTA\uff01<\/span><\/span><\/p>\n<p style=\"margin-top: 9.0pt;margin-bottom: 9.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\"><img decoding=\"async\" loading=\"lazy\" class=\"rich_pages wxw-img\" data-imgfileid=\"100014282\" data-ratio=\"0.28425925925925927\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/2_EPsZMnyBwpZtXqCJqt2IiaWruQwLhg.png?resize=560%2C159\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAwaOtnqQlN0kicqNFL8Q1tVStuvTKicsIxNumCPeEnratVLLOibEPsZMnyBwpZtXqCJqt2IiaWruQwLhg\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" height=\"159 .14999389648438\" width=\"560 .010009765625\" data-recalc-dims=\"1\" \/><\/span><\/p>\n<p style=\"margin-top: 9.0pt;margin-bottom: 9.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\">\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\u679c\u5c06\u4e3a\u5927\u5bb6\u4ecb\u7ecd\u8fd9\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\u3002<\/span><\/p>\n<p style=\"margin-top: 9.0pt;margin-bottom: 9.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\"><img decoding=\"async\" loading=\"lazy\" class=\"rich_pages wxw-img\" data-imgfileid=\"100014281\" data-ratio=\"0.46111111111111114\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/3_e4g6NSoshhUrbFBE3QRC5ib6Tbcu1w.png?resize=560%2C258\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAwaOtnqQlN0kicqNFL8Q1tVSDGotXFXHX00Cib3N1TpDxd0L0Fe4g6NSoshhUrbFBE3QRC5ib6Tbcu1w\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" height=\"258 .3900146484375\" width=\"560 .010009765625\" data-recalc-dims=\"1\" \/><\/span><\/p>\n\u5b89\u88c5<\/span><\/strong><\/span><o:page><\/o:page><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"cpp\"><code>install.packages(c(\"devtools\"<\/span>, \"dplyr\"<\/span>,\"forcats\"<\/span>,\"ggplot2\"<\/span>)) <\/span><\/code><code>devtools::install_github(\"ytakemon\/GRETTA\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n<section style=\"font-size: 16px;margin-bottom: 0px;\">\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 100%;vertical-align: top;align-self: flex-start;flex: 0 0 auto;background-color: rgb(69, 107, 176);padding-right: 6px;padding-bottom: 6px;padding-left: 6px;\">\n<section style=\"margin-top: 10px;margin-bottom: 10px;transform: translate3d(19px, 0px, 0px);\" powered-by=\"xiumi.us\">\n<section style=\"text-align: justify;color: rgb(255, 255, 255);\">\n <\/p>\n<\/section>\n<\/section>\n<section style=\"justify-content: flex-start;display: flex;flex-flow: row;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 100%;vertical-align: top;align-self: flex-start;flex: 0 0 auto;background-color: rgb(255, 255, 255);padding: 23px;\">\n<section style=\"text-align: justify;\" powered-by=\"xiumi.us\">\n\u516c\u4f17\u53f7\u540e\u53f0\u56de\u590d\u201c111\u201d<\/span>\u9886\u53d6\u672c\u7bc7\u4ee3\u7801\u3001\u57fa\u56e0\u96c6\u6216\u793a\u4f8b\u6570\u636e\u7b49\u6587\u4ef6\uff1b<\/p>\n\u6587\u4ef6\u7f16\u53f7\uff1a231207<\/strong><\/em><\/span><\/p>\n\u679c\u7c89\u798f\u5229<\/span><\/strong>\uff1a\u751f\u4fe1\u4eba\u5fc5\u5907\u795e\u5668\u2014\u2014\u670d\u52a1\u5668<\/strong><\/span> <\/p>\n\u5e73\u65f6\u751f\u4fe1\u5206\u6790\u5b66\u4e60\u4e2d\u6709\u8981\u7684\u5c0f\u4f19\u4f34\u53ef\u4ee5\u8054\u7cfb\u5c0f\u679c\u79df\u8d41\uff0c\u7c89\u4e1d\u798f\u5229\u90fd\u662f\u5e02\u573a\u8d85\u4f4e\u4ef7\u683c\uff0c\u8d76\u5feb\u627e\u5c0f\u679c\u9886\u53d6\u514d\u8d39\u7684\u8bd5\u7528\u8d26\u53f7\u5427\uff01<\/p>\n<a target=\"_blank\" href=\"http:\/\/mp.weixin.qq.com\/s?__biz=Mzg3OTg3MjQyNg==&mid=2247495825&idx=2&sn=a22a72496a1c082ea5b827be94ce4d99&chksm=cf7f6054f808e94264e14bfed4359cab3fcaaca249952a9f761c37ece352dcd7108579788b89&scene=21#wechat_redirect\" textvalue=\"\u670d\u52a1\u5668\u4ef7\u683c\u914d\u7f6e\u8868\uff08\u70b9\u51fb\u94fe\u63a5\u67e5\u770b\uff09\" linktype=\"text\" imgurl=\"\" imgdata=\"null\" data-itemshowtype=\"0\" tab=\"innerlink\" data-linktype=\"2\" rel=\"noopener\">\u670d\u52a1\u5668\u4ef7\u683c\u914d\u7f6e\u8868\uff08\u70b9\u51fb\u94fe\u63a5\u67e5\u770b\uff09<\/a><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n\u5de5\u4f5c\u6d41\u7a0b<\/strong><\/span><\/p>\n\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528<\/strong><\/span><\/p>\n1.\u5b89\u88c5\u201cGRETTA\u201d\u5e76\u4e0b\u8f7d\u9644\u5e26\u7684\u6570\u636e\u3002<\/span><\/p>\n2.\u9009\u62e9\u57fa\u56e0\u611f\u5174\u8da3\u7684\u7a81\u53d8\u7ec6\u80de\u7cfb\u548c\u5bf9\u7167\u7ec6\u80de\u7cfb\u3002<\/span><\/p>\n\u2013\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<\/span><\/p>\n3.\u786e\u5b9a\u7a81\u53d8\u7ec6\u80de\u7cfb\u548c\u5bf9\u7167\u7ec6\u80de\u7cfb\u7ec4\u4e4b\u95f4\u7684\u5dee\u5f02\u8868\u8fbe\u3002<\/span><\/p>\n\u2013\uff08\u53ef\u9009\u4f46\u63a8\u8350\uff09\u3002<\/span><\/p>\n4.\u8fdb\u884c<\/span>in silico<\/span>\u57fa\u56e0\u7b5b\u67e5\u3002<\/span><\/span><\/p>\n5.\u53ef\u89c6\u5316\u7ed3\u679c\u3002<\/span> <\/span><o:page><\/o:page><\/p>\n\u534f\u540c\u5fc5\u9700\uff08co-essential\uff09\u7f51\u7edc<\/strong><\/span><\/p>\n1.\u5b89\u88c5\u201cGRETTA\u201d\u5e76\u4e0b\u8f7d\u9644\u5e26\u7684\u8d44\u6599\u3002<\/span><\/p>\n2.\u8fd0\u884c\u76f8\u5173\u6027\u7cfb\u6570\u5206\u6790\u3002<\/span><\/p>\n\u2013\u53ef\u4ee5\u7528\u4e8e\u5728\u7279\u5b9a\u75be\u75c5\u7c7b\u578b\u7684\u7ec6\u80de\u7cfb\u4e2d\u8fdb\u884c\u7684\u5206\u6790\u3002<\/span><\/p>\n3.\u8ba1\u7b97\u8d1f\/\u6b63\u66f2\u7ebf\u7684\u62d0\u70b9\u4ee5\u786e\u5b9a\u9608\u503c\u3002<\/span><\/p>\n4.\u5e94\u7528\u9608\u503c\u3002<\/span><\/p>\n5.\u53ef\u89c6\u5316\u7ed3\u679c\u3002<\/span><\/p>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100014485\" data-ratio=\"0.38981481481481484\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/4_IWicymEetCc6Enf2BkUE4Yz1Ya5e5w-5.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyuYsqlJnQUJvnS2iciaDibtfbdV0VmHlriaSjYwjFPLp1BLjokHIWicymEetCc6Enf2BkUE4Yz1Ya5e5w\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" style=\"letter-spacing: 0.578px;text-wrap: wrap;color: rgb(34, 34, 38);font-family: "Times New Roman";font-size: 15px;font-weight: 700;background-color: rgb(255, 255, 255);text-align: center;\" data-recalc-dims=\"1\" \/><\/strong><\/span><\/p>\n\u4e00\u4e2a\u4f8b\u5b50\uff1a\u9274\u5b9aARID1A\u57fa\u56e0\u76f8\u4e92\u4f5c\u7528<\/strong><\/span><\/p>\nARID1A<\/span>\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<\/span>ARID1A<\/span>\u53ca\u5176\u540c\u6e90\u7269<\/span>ARID1B<\/span>\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<\/span>Helming\u7b49\uff0c2014<\/span>\uff09\u3002\u8fd9\u4e2a\u4f8b\u5b50\u5c06\u5c55\u793a\u5982\u4f55\u4f7f\u7528<\/span>GRETTA\u9274\u5b9a<\/span>ARID1A<\/span>\u7684\u5408\u6210\u81f4\u6b7b\u4e92\u4f5c\u7269\u5e76\u9884\u6d4b\u8fd9\u4e00\u5df2\u77e5\u7684\u76f8\u4e92\u4f5c\u7528\u3002 \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\u7528<\/span>install.packages()\uff08\u4f8b\u5982<\/span>install.packages(“dplyr”)\uff09\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"shell\"><code># Load library <\/span><\/span><\/code><code>library(tidyverse) <\/span><\/span><\/code><code>#> \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 <\/span><\/span><\/code><code>#> \u2714 ggplot2 3.4.1 \u2714 purrr 1.0.1 <\/span><\/span><\/code><code>#> \u2714 tibble 3.2.1 \u2714 dplyr 1.1.1 <\/span><\/span><\/code><code>#> \u2714 tidyr 1.3.0 \u2714 stringr 1.5.0 <\/span><\/span><\/code><code>#> \u2714 readr 2.1.4 \u2714 forcats 1.0.0 <\/span><\/span><\/code><code>#> \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 <\/span><\/span><\/code><code>#> \u2716 dplyr::filter() masks stats::filter() <\/span><\/span><\/code><code>#> \u2716 dplyr::lag() masks stats::lag() <\/span><\/span><\/code><code>library(GRETTA) <\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> _______ .______ _______ .___________.___________. ___ <\/span><\/span><\/code><code>#> \/ _____|| _ | ____|| | | \/ <\/span><\/span><\/code><code>#> | | __ | |_) | | |__ `---| |----`---| |----` \/ ^ <\/span><\/span><\/code><code>#> | | |_ | | \/ | __| | | | | \/ \/_ <\/span><\/span><\/code><code>#> | |__| | | | ----.| |____ | | | | \/ _____ <\/span><\/span><\/code><code>#> ______| | _| `._____||_______| |__| |__| \/__\/ __ <\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> Welcome to GRETTA! The version loaded is: 0.99.2 <\/span><\/span><\/code><code>#> The latest DepMap dataset accompanying this package is v22Q2. <\/span><\/span><\/code><code>#> Please refer to our tutorial on GitHub for loading DepMap data and details: https:\/\/github.com\/ytakemon\/GRETTA<\/span><\/span><\/code><\/pre>\n<\/section>\n\u4e0b\u8f7d\u793a\u4f8b\u6570\u636e<\/strong><\/span><\/p>\n\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<\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"nginx\"><code>path <- getwd() <\/span><\/span><\/code><code>download_example_data(path) <\/span><\/span><\/code><code>#> Data saved to: \/projects\/marralab\/ytakemon_prj\/DepMap\/GRETTA\/GRETTA_example\/<\/span><\/span><\/code><\/pre>\n<\/section>\n<p style=\"mso-style-name: 'Source Code';margin-top: 0.0pt;margin-bottom: 10.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;word-break: break-all;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\"><span style=\"font-size: 15px;font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;letter-spacing: 0.034em;text-align: justify;\">\u7136\u540e\uff0c\u5206\u914d\u53d8\u91cf\u6307\u5411\u5b58\u50a8 <\/span>.rda \u6587\u4ef6\u7684\u4f4d\u7f6e\u548c\u7ed3\u679c\u6587\u4ef6\u5e94\u8be5\u5b58\u50a8\u7684\u4f4d\u7f6e\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"nginx\"><code>gretta_data_dir <- paste0(path,\"\/GRETTA_example\/\") <\/span><\/span><\/code><code>gretta_output_dir <- paste0(path,\"\/GRETTA_example_output\/\")<\/span><\/span><\/code><\/pre>\n<\/section>\n\u63a2\u7d22\u7ec6\u80de\u7cfb<\/strong><\/span><\/p>\n\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<\/span>https:\/\/depmap.org\/portal\/<\/span>\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\u62ec<\/span>list_mutations(), <\/span>list_cancer_types(), <\/span>list_cancer_subtypes()\u3002 \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<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"shell\"><code>## Find ARID1A hotspot mutations detected in all cell lines <\/span><\/span><\/code><code>list_mutations(gene = \"ARID1A\", is_hotspot = TRUE, data_dir = gretta_data_dir)<\/span><\/span><\/code><code>## List all available cancer types <\/span><\/span><\/code><code>list_cancer_types(data_dir = gretta_data_dir) <\/span><\/span><\/code><code>#> [1] \"Kidney Cancer\" \"Leukemia\"<\/span><\/span><\/code><code>#> [3] \"Lung Cancer\" \"Non-Cancerous\"<\/span><\/span><\/code><code>#> [5] \"Sarcoma\" \"Lymphoma\"<\/span><\/span><\/code><code>#> [7] \"Colon\/Colorectal Cancer\" \"Pancreatic Cancer\"<\/span><\/span><\/code><code>#> [9] \"Gastric Cancer\" \"Rhabdoid\"<\/span><\/span><\/code><code>#> [11] \"Endometrial\/Uterine Cancer\" \"Esophageal Cancer\"<\/span><\/span><\/code><code>#> [13] \"Breast Cancer\" \"Brain Cancer\"<\/span><\/span><\/code><code>#> [15] \"Ovarian Cancer\" \"Bone Cancer\"<\/span><\/span><\/code><code>#> [17] \"Myeloma\" \"Head and Neck Cancer\"<\/span><\/span><\/code><code>#> [19] \"Bladder Cancer\" \"Skin Cancer\"<\/span><\/span><\/code><code>#> [21] \"Bile Duct Cancer\" \"Prostate Cancer\"<\/span><\/span><\/code><code>#> [23] \"Cervical Cancer\" \"Thyroid Cancer\"<\/span><\/span><\/code><code>#> [25] \"Neuroblastoma\" \"Eye Cancer\"<\/span><\/span><\/code><code>#> [27] \"Liposarcoma\" \"Gallbladder Cancer\"<\/span><\/span><\/code><code>#> [29] \"Teratoma\" \"Unknown\"<\/span><\/span><\/code><code>#> [31] \"Liver Cancer\" \"Adrenal Cancer\"<\/span><\/span><\/code><code>#> [33] \"Embryonal Cancer\"<\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## List all available cancer subtypes <\/span><\/span><\/code><code>list_cancer_subtypes(input_disease = \"Lung Cancer\", data_dir = gretta_data_dir) <\/span><\/span><\/code><code>#> [1] \"Non-Small Cell Lung Cancer (NSCLC), Adenocarcinoma\"<\/span><\/span><\/code><code>#> [2] \"Small Cell Lung Cancer (SCLC)\"<\/span><\/span><\/code><code>#> [3] \"Non-Small Cell Lung Cancer (NSCLC), Squamous Cell Carcinoma\"<\/span><\/span><\/code><code>#> [4] \"Mesothelioma\"<\/span><\/span><\/code><code>#> [5] \"Non-Small Cell Lung Cancer (NSCLC), Large Cell Carcinoma\"<\/span><\/span><\/code><code>#> [6] NA <\/span><\/span><\/code><code>#> [7] \"Non-Small Cell Lung Cancer (NSCLC), unspecified\"<\/span><\/span><\/code><code>#> [8] \"Non-Small Cell Lung Cancer (NSCLC), Adenosquamous Carcinoma\"<\/span><\/span><\/code><code>#> [9] \"Carcinoid\"<\/span><\/span><\/code><code>#> [10] \"Non-Small Cell Lung Cancer (NSCLC), Mucoepidermoid Carcinoma\"<\/span><\/span><\/code><code>#> [11] \"Carcinoma\"<\/span><\/span><\/code><\/pre>\n<\/section>\n\u9009\u62e9\u7a81\u53d8\u7ec6\u80de\u7cfb\u548c\u5bf9\u7167\u7ec6\u80de\u7cfb\u7ec4<\/strong><\/span><\/p>\n\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c<\/span>select_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 \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 \u9ed8\u8ba4\u5206\u914d\u7684\u7ec6\u80de\u7cfb\u7ec4\u662f\uff1a<\/span><\/span><\/p>\n\u2022<\/span>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<\/span><\/span><\/p>\n\u2022<\/span>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<\/span><\/span><\/p>\n\u2022<\/span>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<\/span><\/span><\/p>\n\u2022<\/span>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<\/span> <\/span><o:page><\/o:page><\/p>\n\u2022<\/span>Amplified\u7ec6\u80de\u7cfb\u4e0d\u542b\u6709\u62f7\u8d1d\u6570\u589e\u52a0\u7684\u6709\u5bb3SNVs\/InDels\u3002<\/span><\/span><\/p>\n\u2022<\/span>Others\u7ec6\u80de\u7cfb\u542b\u6709\u6709\u5bb3SNVs\uff0c\u5176\u62f7\u8d1d\u6570\u589e\u52a0\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"shell\"><code>ARID1A_groups <- select_cell_lines(input_gene = \"ARID1A\", data_dir = gretta_data_dir) <\/span><\/span><\/code><code>#> Selecting mutant groups for: ARID1A in all cancer cell lines <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## Show number of cell lines in each group <\/span><\/span><\/code><code>count(ARID1A_groups, Group) <\/span><\/span><\/code><code>#> # A tibble: 5 \u00d7 2 <\/span><\/span><\/code><code>#> Group n <\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> 1 ARID1A_HetDel 61 <\/span><\/span><\/code><code>#> 2 ARID1A_HomDel 23 <\/span><\/span><\/code><code>#> 3 ARID1A_T-HetDel 30 <\/span><\/span><\/code><code>#> 4 Control 906 <\/span><\/span><\/code><code>#> 5 Others 66<\/span><\/span><\/code><\/pre>\n<\/section>\n\u53ef\u9009\u7684\u7ec6\u80de\u7cfb\u8fc7\u6ee4\u5668<\/strong><\/span><\/p>\n\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<\/span><\/p>\n\u2022<\/span>input_aa_change – \u901a\u8fc7\u6c28\u57fa\u9178\u53d8\u5316\uff08\u4f8b\u5982\u201cp.Q515*\u201d\uff09\u3002<\/span><\/span><\/p>\n\u2022<\/span>input_disease – \u901a\u8fc7\u75be\u75c5\u7c7b\u578b\uff08\u4f8b\u5982\u201c\u80f0\u817a\u764c\u201d\uff09<\/span><\/span><\/p>\n\u2022<\/span>input_disease_subtype – \u901a\u8fc7\u75be\u75c5\u4e9a\u578b\uff08\u4f8b\u5982\u201c\u5bfc\u7ba1\u817a\u9cde\u72b6\u7ec6\u80de\u764c\u201d\uff09<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"shell\"><code>## Find pancreatic cancer cell lines with ARID1A mutations <\/span><\/span><\/code><code>ARID1A_pancr_groups <- select_cell_lines(input_gene = \"ARID1A\", <\/span><\/span><\/code><code> input_disease = \"Pancreatic Cancer\", <\/span><\/span><\/code><code> data_dir = gretta_data_dir) <\/span><\/span><\/code><code>#> Selecting mutant groups for: ARID1A in Pancreatic Cancer, cell lines <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## Show number of cell lines in each group <\/span><\/span><\/code><code>count(ARID1A_pancr_groups, Group) <\/span><\/span><\/code><code>#> # A tibble: 4 \u00d7 2 <\/span><\/span><\/code><code>#> Group n <\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> 1 ARID1A_HetDel 5 <\/span><\/span><\/code><code>#> 2 ARID1A_HomDel 4 <\/span><\/span><\/code><code>#> 3 Control 36 <\/span><\/span><\/code><code>#> 4 Others 2<\/span><\/span><\/code><\/pre>\n<\/section>\n\u68c0\u6d4b\u5dee\u5f02\u8868\u8fbe<\/strong><\/span><\/p>\n\u5728\u4e09\u79cd\u7a81\u53d8\u764c\u7ec6\u80de\u7cfb\u7ec4 <\/span>ARID1A_HomDel\u3001<\/span>ARID1A_T-HetDel \u548c <\/span>ARID1A_HetDel \u4e2d\uff0c\u5177\u6709 <\/span>ARID1A_HomDel \u7a81\u53d8\u7684\u764c\u7ec6\u80de\u7cfb\u6700\u53ef\u80fd\u5bfc\u81f4 <\/span>ARID1A<\/span> \u7684\u4e22\u5931\u6216\u8868\u8fbe\u51cf\u5c11\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u60f3\u8981\u68c0\u67e5 <\/span>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 <\/span>ARID1A<\/span> RNA \u6216\u86cb\u767d\u8d28\u8868\u8fbe\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"powershell\"><code>## Select only HomDel and Control cell lines <\/span><\/span><\/code><code>ARID1A_groups_subset <- ARID1A_groups %>% filter(Group %in% c(\"ARID1A_HomDel\", \"Control\")) <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## Get RNA expression <\/span><\/span><\/code><code>ARID1A_rna_expr <- extract_rna( <\/span><\/span><\/code><code> input_samples = ARID1A_groups_subset$DepMap_ID, <\/span><\/span><\/code><code> input_genes = \"ARID1A\", <\/span><\/span><\/code><code> data_dir = gretta_data_dir) <\/span><\/span><\/code><code>#> 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<\/span><\/span><\/code><\/pre>\n<\/section>\n<p style=\"mso-style-name: 'Source Code';margin-top: 0.0pt;margin-bottom: 10.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;word-break: break-all;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\">\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<\/span>DepMap\u7f51\u7ad9<\/span>\u3002\uff09<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"properties\"><code>## Get protein expression <\/span><\/span><\/code><code>ARID1A_prot_expr <- extract_prot( <\/span><\/span><\/code><code>input_samples = ARID1A_groups_subset$DepMap_ID, <\/span><\/span><\/code><code>input_genes = \"ARID1A\", <\/span><\/span><\/code><code>data_dir = gretta_data_dir) <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## Produces an error message since ARID1A protein data is not available<\/span><\/span><\/code><\/pre>\n<\/section>\n\u4f7f\u7528Welch\u7684t\u68c0\u9a8c\uff0c\u6211\u4eec\u53ef\u4ee5\u68c0\u67e5\u76f8\u5bf9\u4e8e\u5bf9\u7167\u7ec6\u80de\u7cfb\uff0c<\/span>ARID1A_HomDel\u7ec6\u80de\u7cfb\u4e2d\u7684<\/span>ARID1A RNA\u8868\u8fbe\u91cf\uff08\u4ee5TPM\u4e3a\u5355\u4f4d\uff09\u662f\u5426\u663e\u8457\u964d\u4f4e\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"shell\"><code>## Append groups and test differential expression <\/span><\/span><\/code><code>ARID1A_rna_expr <- left_join( <\/span><\/span><\/code><code> ARID1A_rna_expr, <\/span><\/span><\/code><code> ARID1A_groups_subset %>% select(DepMap_ID, Group)) %>% <\/span><\/span><\/code><code> mutate(Group = fct_relevel(Group,\"Control\")) # show Control group first <\/span><\/span><\/code><code>#> Joining with `by = join_by(DepMap_ID)` <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## T-test <\/span><\/span><\/code><code>t.test(ARID1A_8289 ~ Group, ARID1A_rna_expr) <\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> Welch Two Sample t-test <\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> data: ARID1A_8289 by Group <\/span><\/span><\/code><code>#> t = 2.5764, df = 22.873, p-value = 0.01692 <\/span><\/span><\/code><code>#> alternative hypothesis: true difference in means between group Control and group ARID1A_HomDel is not equal to 0 <\/span><\/span><\/code><code>#> 95 percent confidence interval: <\/span><\/span><\/code><code>#> 0.1146094 1.0498810 <\/span><\/span><\/code><code>#> sample estimates: <\/span><\/span><\/code><code>#> mean in group Control mean in group ARID1A_HomDel <\/span><\/span><\/code><code>#> 4.635784 4.053539 <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## plot <\/span><\/span><\/code><code>ggplot(ARID1A_rna_expr, aes(x = Group, y = ARID1A_8289)) + <\/span><\/span><\/code><code> geom_boxplot()<\/span><\/span><\/code><\/pre>\n<\/section>\n<img decoding=\"async\" loading=\"lazy\" class=\"rich_pages wxw-img\" data-imgfileid=\"100014278\" data-ratio=\"0.7148148148148148\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/5_mUbL57XEoELxhj4lvADBMwfHRz0Gxw.png?resize=560%2C400\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAwaOtnqQlN0kicqNFL8Q1tVST9AujhNfgtxla0bhibJGqbI1amUbL57XEoELxhj4lvADBMwfHRz0Gxw\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" height=\"400 .17999267578125\" width=\"560 .010009765625\" data-recalc-dims=\"1\" \/><\/span> <\/span><o:page><\/o:page><\/p>\n\u8fdb\u884c\u5168\u57fa\u56e0\u7ec4in silico\u9057\u4f20\u7b5b\u9009<\/strong><\/span><\/p>\n\u5728\u786e\u5b9a<\/span>ARID1A_HomDel\u7ec4\u4e2d\u7684\u7ec6\u80de\u7cfb\u4e0e<\/span>Control\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\u7528<\/span>screen_results()\u6267\u884c<\/span>in silico<\/span>\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 <\/span>\u81f4\u6b7b\u6982\u7387\uff08Lethality probabilities\uff09<\/span>\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<\/span>Meyers, R., et al., 2017<\/span>\u3002 <\/span>screen_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<\/span><\/span><\/p>\n\u2022GeneName_ID\uff1aHugo symbol\u5e26\u6709NCBI\u57fa\u56e0ID<\/span><\/p>\n\u2022GeneNames\uff1aHugo symbol<\/span><\/p>\n\u2022<\/span>median\uff0c<\/span>mean\uff0c<\/span>sd\uff0c<\/span>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<\/span><\/span><\/p>\n\u2022Pval\uff1a\u63a7\u5236\u548c\u7a81\u53d8\u7ec4\u4e4b\u95f4\u7684Mann-Whitney U\u68c0\u9a8c\u7684P\u503c\u3002<\/span><\/p>\n\u2022Adj_pval\uff1a\u7ecf\u8fc7BH\u6821\u6b63\u7684P\u503c\u3002<\/span><\/p>\n\u2022log2FC_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<\/span><\/p>\n\u2022log2FC_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<\/span><\/p>\n\u2022CliffDelta\uff1a\u7a81\u53d8\u548c\u5bf9\u7167\u4f9d\u8d56\u6982\u7387\u4e4b\u95f4\u7684Cliff’s delta\u975e\u53c2\u6570\u6548\u5e94\u5927\u5c0f\u3002\u8303\u56f4\u4ecb\u4e8e-1\u52301\u4e4b\u95f4\u3002<\/span><\/p>\n\u2022dip_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<\/span> <\/span><o:page><\/o:page><\/p>\n\u2022Interaction_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<\/span><\/p>\n\u6ce8\u610f<\/span> \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\u679c\u4e0b\u9762\u7684\u4f8b\u5b50 <\/span>GI_screen() \u82b1\u4e86\u5927\u7ea62\u4e2a\u5c0f\u65f6\u6765\u5904\u7406\u3002\u4e3a\u4e86\u8282\u7701\u65f6\u95f4\uff0c\u5c0f\u679c\u5df2\u7ecf\u4e3a\u60a8\u9884\u5148\u5904\u7406\u4e86\u8fd9\u4e00\u6b65\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"bash\"><code>ARID1A_mutant_id <- ARID1A_groups %>% filter(Group %in% c(\"ARID1A_HomDel\")) %>% pull(DepMap_ID) <\/span><\/span><\/code><code>ARID1A_control_id <- ARID1A_groups %>% filter(Group %in% c(\"Control\")) %>% pull(DepMap_ID) <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## See warning above. <\/span><\/span><\/code><code>## This can take several hours depending on number of lines\/cores used. <\/span><\/span><\/code><code>screen_results <- GI_screen( <\/span><\/span><\/code><code> control_id = ARID1A_control_id, <\/span><\/span><\/code><code> mutant_id = ARID1A_mutant_id, <\/span><\/span><\/code><code> core_num = 5, # depends on how many cores you have <\/span><\/span><\/code><code> output_dir = gretta_output_dir, # Will save your results here as well as in the variable <\/span><\/span><\/code><code> data_dir = gretta_data_dir, <\/span><\/span><\/code><code>test = FALSE) # use TRUE to run a short test to make sure all will run overnight.<\/span><\/span><\/code><code>## Load prepared ARID1A screen result <\/span><\/span><\/code><code>load(paste0(gretta_data_dir,\"\/sample_22Q2_ARID1A_KO_screen.rda\"), envir = environment())<\/span><\/span><\/code><\/pre>\n<\/section>\n\u6211\u4eec\u53ef\u4ee5\u5feb\u901f\u786e\u5b9a<\/span>GRETTA\u662f\u5426\u9884\u6d4b\u4e86\u4efb\u4f55\u81f4\u6b7b\u6027\u9057\u4f20\u76f8\u4e92\u4f5c\u7528\u3002\u6211\u4eec\u4f7f\u7528<\/span>Pval\u622a\u65ad\u503c\u4e3a0.05\uff0c\u5e76\u6839\u636e<\/span>Interaction_score\u8fdb\u884c\u6392\u540d\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"css\"><code>screen_results %>% <\/span><\/span><\/code><code>filter(Pval < 0.05) %>% <\/span><\/span><\/code><code>arrange(-Interaction_score) %>% <\/span><\/span><\/code><code>select(GeneNames:Mutant_median, Pval, Interaction_score) %>% head<\/span><\/span><\/code><code>#> # A tibble: 6 \u00d7 5 <\/span><\/span><\/code><code>#> GeneNames Control_median Mutant_median Pval Interaction_score<\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> 1 ARID1B 0.0579 0.515 6.84e-10 9.16<\/span><\/span><\/code><code>#> 2 CCDC110 0.0165 0.0303 3.54e- 4 3.45<\/span><\/span><\/code><code>#> 3 APOO 0.0168 0.0283 9.61e- 4 3.02<\/span><\/span><\/code><code>#> 4 NHS 0.0352 0.0539 9.69e- 4 3.01<\/span><\/span><\/code><code>#> 5 SLC66A2 0.00793 0.0134 1.06e- 3 2.98<\/span><\/span><\/code><code>#> 6 ATXN7L1 0.0138 0.0259 1.78e- 3 2.75<\/span><\/span><\/code><\/pre>\n<\/section>\n<p style=\"mso-style-name: 'Source Code';margin-top: 0.0pt;margin-bottom: 10.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;word-break: break-all;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\"><span style=\"font-size: 15px;font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;letter-spacing: 0.034em;text-align: justify;\">\u5c0f\u679c\u53ef\u4ee5\u53d1\u73b0\uff0c<\/span>ARID1B<\/span>\uff0c\u4e00\u79cd\u5df2\u77e5\u7684\u4e0e<\/span>ARID1A<\/span>\u7684\u5408\u6210\u81f4\u6b7b\u76f8\u4e92\u4f5c\u7528\uff0c\u662f\u6b64\u5217\u8868\u7684\u9876\u90e8\u3002<\/span> <\/span><\/p>\n<o:page><\/o:page><\/p>\n\u53ef\u9009\u6b65\u9aa4\uff1a\u8fdb\u884c\u5c0f\u89c4\u6a21\u7b5b\u9009<\/strong><\/span><\/span><\/p>\n\u8981\u8fdb\u884c\u5c0f\u578b\u4f53\u5185\u7b5b\u9009\uff0c\u53ef\u4ee5\u5728 <\/span>gene_list = \u53c2\u6570\u4e2d\u63d0\u4f9b\u57fa\u56e0\u5217\u8868\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"nginx\"><code>small_screen_results <- GI_screen( <\/span><\/span><\/code><code> control_id = ARID1A_control_id, <\/span><\/span><\/code><code> mutant_id = ARID1A_mutant_id, <\/span><\/span><\/code><code> gene_list = c(\"ARID1A\", \"ARID1B\", \"SMARCA2\", \"GAPDH\", \"SMARCC2\"), <\/span><\/span><\/code><code> core_num = 5, # depends on how many cores you have <\/span><\/span><\/code><code> output_dir = gretta_output_dir, # Will save your results here as well as in the variable <\/span><\/span><\/code><code> data_dir = gretta_data_dir)<\/span><\/span><\/code><\/pre>\n<\/section>\n\u53ef\u89c6\u5316\u7b5b\u9009\u7ed3\u679c<\/strong><\/span><\/p>\n\u6700\u540e\uff0c\u4e00\u65e6\u5b8c\u6210<\/span>\u8ba1\u7b97\u673a\u7b5b\u9009<\/span>\uff0c\u5c31\u53ef\u4ee5\u4f7f\u7528<\/span>plot_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\u679c\u786e\u5b9a\u4e86<\/span>ARID1B<\/span>\u662f<\/span>ARID1A<\/span>\u7684\u5408\u6210\u81f4\u6b7b\u76f8\u4e92\u4f5c\u7528\u7269\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"shell\"><code>## Visualize results, turn on gene labels, <\/span><\/span><\/code><code>## and label three genes each that are predicted to have <\/span><\/span><\/code><code>## lethal and alleviating genetic interactions, respectively <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>plot_screen(result_df = screen_results, <\/span><\/span><\/code><code> label_genes = TRUE, <\/span><\/span><\/code><code> label_n = 3) <\/span><\/span><\/code><code>#> Warning: Removed 7 rows containing missing values (`geom_point()`).<\/span><\/span><\/code><\/pre>\n<\/section>\n<p style=\"mso-style-name: 'Source Code';margin-top: 0.0pt;margin-bottom: 10.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;word-break: break-all;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\"><o:page><\/o:page><\/p>\n<img decoding=\"async\" loading=\"lazy\" class=\"rich_pages wxw-img\" data-imgfileid=\"100014279\" data-ratio=\"0.7148148148148148\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/6_CrlwIibzr30hN5VpLqZXDzibJCrURg.png?resize=560%2C400\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAwaOtnqQlN0kicqNFL8Q1tVSuxYTV7uoW5QibI6ORJEraxybiaCACrlwIibzr30hN5VpLqZXDzibJCrURg\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" height=\"400 .5\" width=\"560 .010009765625\" data-recalc-dims=\"1\" \/><\/span><\/p>\n\u793a\u4f8b2\uff1a\u9274\u5b9aARID1A\u534f\u540c\u5fc5\u9700\uff08co-essential\uff09\u57fa\u56e0<\/strong><\/span><\/p>\n\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<\/span>Wainberg et al. 2021<\/span>; <\/span>Pan et al. 2018<\/span>\uff09\u3002 \u8003\u8651\u5230ARID1A\u662f\u54fa\u4e73\u52a8\u7269SWI\/SNF\u590d\u5408\u7269\u5df2\u77e5\u7684\u4e9a\u57fa(<\/span>Mashtalir\u7b492018<\/span>)\uff0c\u5c0f\u679c\u9884\u8ba1SWI\/SNF\u590d\u5408\u7269\u7684\u6210\u5458\u5c06\u4e0e<\/span>ARID1A<\/span>\u5171\u4eab\u5171\u540c\u5fc5\u9700\u6027\u3002\u672c\u793a\u4f8b\u5c06\u5c55\u793a\u5982\u4f55\u4f7f\u7528<\/span>GRETTA\u7ed8\u5236<\/span>ARID1A<\/span>\u7684\u5171\u5fc5\u9700\u57fa\u56e0\u7f51\u7edc\u3002<\/span><\/span><\/p>\n\u9274\u5b9a\u5177\u6709\u6700\u9ad8\u76f8\u5173\u6027\u7cfb\u6570\u7684\u57fa\u56e0<\/strong><\/span><\/p>\n\u4e3a\u4e86\u786e\u5b9a\u5171\u5fc5\u9700\u57fa\u56e0\uff0c\u6211\u4eec\u5c06\u5bf9<\/span>ARID1A<\/span>\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<\/span>SMARCE1<\/span>\u548c<\/span>SMARCB1<\/span>\u662f\u524d\u4e24\u4e2a\u5171\u5fc5\u9700\u57fa\u56e0\u3002<\/span> <\/span><o:page><\/o:page><\/p>\n\u6ce8\u610f<\/span> \u6b64\u8fc7\u7a0b\u53ef\u80fd\u9700\u8981\u51e0\u5206\u949f\u7684\u65f6\u95f4\u3002\u5c0f\u679c\u4e0b\u9762\u7684\u793a\u4f8b <\/span>coessential_map() + <\/span>get_inflection_points() \u5904\u7406\u5927\u7ea6\u9700\u898117\u5206\u949f\u7684\u65f6\u95f4\u3002\u4e3a\u4e86\u8282\u7701\u65f6\u95f4\uff0c\u5c0f\u679c\u5df2\u7ecf\u4e3a\u60a8\u9884\u5148\u5904\u7406\u4e86\u8fd9\u4e00\u6b65\u9aa4\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"properties\"><code>## Map co-essential genes <\/span><\/span><\/code><code>coess_df <- coessential_map( <\/span><\/span><\/code><code>input_gene = \"ARID1A\", <\/span><\/span><\/code><code>data_dir = gretta_data_dir, <\/span><\/span><\/code><code>output_dir = gretta_output_dir) <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>## Calculate inflection points of positive and negative curve using co-essential gene results. <\/span><\/span><\/code><code>coess_inflection_df <- get_inflection_points(coess_df)<\/span><\/span><\/code><code>\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<\/span><\/span><\/code><\/pre>\n<\/section>\n<p style=\"mso-style-name: 'Source Code';margin-top: 0.0pt;margin-bottom: 10.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;word-break: break-all;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\">\u5316\u3002<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"properties\"><code>## Combine and annotate data frame containing co-essential genes <\/span><\/span><\/code><code>coess_annotated_df <- annotate_coess(coess_df, coess_inflection_df) <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>plot_coess( <\/span><\/span><\/code><code>result_df = coess_annotated_df, <\/span><\/span><\/code><code>inflection_df = coess_inflection_df, <\/span><\/span><\/code><code>label_genes = TRUE, # Should gene names be labeled? <\/span><\/span><\/code><code>label_n = 3) # Number of genes to display from each end<\/span><\/span><\/code><\/pre>\n<\/section>\n<p style=\"mso-style-name: 'Source Code';margin-top: 0.0pt;margin-bottom: 10.0pt;text-align: left;margin-left: 0.0pt;text-indent: 0.0pt;line-height: normal;mso-pagination: widow-orphan;word-break: break-all;font-size: 12.0pt;font-family: Cambria;mso-fareast-font-family: 'Cambria';mso-fareast-language: EN-US;font-weight: normal;mso-bidi-font-weight: normal;\"> <\/p>\n<img decoding=\"async\" loading=\"lazy\" class=\"rich_pages wxw-img\" data-imgfileid=\"100014280\" data-ratio=\"0.7148148148148148\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/7_B1knaL7IS7c4fI6kMkwTA7K4Ydicug.png?resize=560%2C400\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAwaOtnqQlN0kicqNFL8Q1tVS8QRqXN2DZgeicaG9ia7OdEHzyecB1knaL7IS7c4fI6kMkwTA7K4Ydicug\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" height=\"400 .07000732421875\" width=\"560 .010009765625\" data-recalc-dims=\"1\" \/> \u5c0f\u679c\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<\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"css\"><code>## Show top 10 co-essential genes. <\/span><\/span><\/code><code>coess_annotated_df %>% arrange(Rank) %>% head(10) <\/span><\/span><\/code><code>#> # A tibble: 10 \u00d7 9 <\/span><\/span><\/code><code>#> GeneNameID_A GeneNameID_B estimate statistic p.value parameter Rank<\/span><\/span><\/code><code>#> <\/span><\/span><\/code><code>#> 1 ARID1A_8289 ARID1A_8289 1 Inf 0 1086 1 <\/span><\/span><\/code><code>#> 2 ARID1A_8289 SMARCB1_6598 0.477 17.9 7.45e-59 1086 2 <\/span><\/span><\/code><code>#> 3 ARID1A_8289 SMARCE1_6605 0.399 14.3 4.30e-39 1086 3 <\/span><\/span><\/code><code>#> 4 ARID1A_8289 SMARCC1_6599 0.369 13.1 9.35e-33 1086 4 <\/span><\/span><\/code><code>#> 5 ARID1A_8289 SS18_6760 0.332 11.6 4.85e-26 1086 5 <\/span><\/span><\/code><code>#> 6 ARID1A_8289 DPF2_5977 0.330 11.5 1.15e-25 1086 6 <\/span><\/span><\/code><code>#> 7 ARID1A_8289 SMARCD2_6603 0.270 9.22 1.10e-16 1086 7 <\/span><\/span><\/code><code>#> 8 ARID1A_8289 SMARCC2_6601 0.242 8.22 2.34e-13 1086 8 <\/span><\/span><\/code><code>#> 9 ARID1A_8289 BCL2_596 0.231 7.82 4.05e-12 1086 9 <\/span><\/span><\/code><code>#> 10 ARID1A_8289 CBFB_865 0.224 7.58 2.07e-11 1086 10 <\/span><\/span><\/code><code>#> # \u2139 2 more variables: Padj_BH, Candidate_gene<\/span><\/span><\/code><\/pre>\n<\/section>\n\u7279\u5b9a\u764c\u75c7\u7c7b\u578b\u7684\u53ef\u9009\u8fc7\u6ee4\u5668<\/strong><\/span><\/p>\n\u7528\u6237\u8fd8\u53ef\u4ee5\u4f7f\u7528<\/span>input_disease = “”\u9009\u9879\u6309\u75be\u75c5\u7c7b\u578b\u8fdb\u884c\u5b50\u96c6\u5212\u5206\uff0c\u6216\u4f7f\u7528<\/span>input_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\u679c\u63d0\u4f9b\u4e86\u4e00\u4e2aARID1A\u5fc5\u9700\u57fa\u56e0\u6620\u5c04\u7528\u4e8e\u80f0\u817a\u764c\u7684\u793a\u4f8b\u3002<\/span><\/span><\/p>\n\u6ce8\u610f<\/span> \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<\/span><\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"properties\"><code>## Map co-essential genes in pancreatic cancers only <\/span><\/span><\/code><code>coess_df <- coessential_map( <\/span><\/span><\/code><code>input_gene = \"ARID1A\", <\/span><\/span><\/code><code>input_disease = \"Pancreatic Cancer\", <\/span><\/span><\/code><code>core_num = 5, ## Depending on how many cores you have access to, increase this value to shorten processing time. <\/span><\/span><\/code><code>data_dir = gretta_data_dir, <\/span><\/span><\/code><code>output_dir = gretta_output_dir, <\/span><\/span><\/code><code>test = FALSE)<\/span><\/span><\/code><\/pre>\n<\/section>\n\u9488\u5bf9\u81ea\u5b9a\u4e49\u7ec6\u80de\u7cfb\u7684\u53ef\u9009\u8fc7\u6ee4\u5668<\/strong><\/span><\/p>\n\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528 <\/span>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<\/span><\/span><\/p>\n\u6ce8\u610f<\/span> \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<\/span> <\/span><\/p>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"properties\"><code>custom_lines <- c(\"ACH-000001\", \"ACH-000002\", \"ACH-000003\",...) <\/span><\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code> <\/span><\/code><code>coess_df <- coessential_map( <\/span><\/span><\/code><code>input_gene = \"ARID1A\", <\/span><\/span><\/code><code>input_cell_lines = custom_lines, <\/span><\/span><\/code><code>core_num = 5, ## Depending on how many cores you have access to, increase this value to shorten processing time. <\/span><\/span><\/code><code>data_dir = gretta_data_dir, <\/span><\/span><\/code><code>output_dir = gretta_output_dir, <\/span><\/span><\/code><code>test = FALSE)<\/span><\/span><\/code><\/pre>\n<\/section>\n\u7ed3\u8bed<\/strong><\/span><\/p>\n\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<\/span><\/p>\n\u53c2\u8003\u6587\u732e<\/strong><\/span><\/p>\n[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.<\/span><\/p>\n<section powered-by=\"xiumi.us\" style=\"margin-bottom: 0px;\">\n<section>\n<section powered-by=\"xiumi.us\">\n<section>\n<section powered-by=\"xiumi.us\">\n<section>\n<section powered-by=\"xiumi.us\">\n<section>\n<section>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-imgfileid=\"100014392\" data-ratio=\"0.5555555555555556\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/8_VmRkv7tSNKliafDnsuFR7BoeDgBrww-5.jpg?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_jpg\/zcicibWZessAxPqeNQsFjm2HibIuSld9Nh9GicQSWhJwHjia5tEevQvDIebLErVmRkv7tSNKliafDnsuFR7BoeDgBrww\/640?wx_fmt=jpeg&from=appmsg'\" data-type=\"jpeg\" data-w=\"900\" style=\"font-size: var(--articleFontsize);letter-spacing: 0.034em;\" data-recalc-dims=\"1\"> <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<p style=\"margin-top: 0pt;margin-bottom: 0pt;margin-left: 0pt;letter-spacing: 0.578px;text-indent: 0pt;text-wrap: wrap;text-align: justify;line-height: normal;font-size: 12pt;font-family: helvetica;color: rgb(0, 0, 0);break-after: auto;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-imgfileid=\"100014393\" data-ratio=\"0.38981481481481484\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/9_FbEg9fibwQicL8l2ExKOZZTmAHXabQ-6.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAx3HetBkB7aOGibFWKqibqIATyC4zFibzCgdrxFwRep6cDMLIbA0FbEg9fibwQicL8l2ExKOZZTmAHXabQ\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" style=\"font-size: var(--articleFontsize);letter-spacing: 0.034em;caret-color: rgba(0, 0, 0, 0);\" data-recalc-dims=\"1\"><\/p>\n<section 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textvalue=\"1.\u642d\u5efa\u751f\u4fe1\u5206\u6790\u6d41\u6c34\u7ebf\uff0c\u5982\u5de5\u5382\u4e00\u683724\u5c0f\u65f6\u8fd0\u8f6cSnakemake\u2014\u2014\u8fdb\u9636\u547d\u4ee4\" linktype=\"text\" imgurl=\"\" imgdata=\"null\" data-itemshowtype=\"0\" tab=\"innerlink\" data-linktype=\"2\" hasload=\"1\" style=\"color: var(--weui-LINK);outline: 0px;cursor: pointer;font-size: 15px;\" rel=\"noopener\">1.\u642d\u5efa\u751f\u4fe1\u5206\u6790\u6d41\u6c34\u7ebf\uff0c\u5982\u5de5\u5382\u4e00\u683724\u5c0f\u65f6\u8fd0\u8f6cSnakemake\u2014\u2014\u8fdb\u9636\u547d\u4ee4<\/span><\/a> <\/section>\n<section style=\"outline: 0px;letter-spacing: 0.578px;line-height: 1.75em;\"><a target=\"_blank\" href=\"http:\/\/mp.weixin.qq.com\/s?__biz=Mzg3OTg3MjQyNg==&mid=2247491705&idx=1&sn=9ff9b6da28b3a256a99c6697d589be66&chksm=cf7f70bcf808f9aafd1019a0bba3dd3a55e2867dbb9a09203f13c2683adf867094637c405ed1&scene=21#wechat_redirect\" 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