{"id":29233,"date":"2024-02-22T17:50:32","date_gmt":"2024-02-22T09:50:32","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=29233"},"modified":"2024-02-22T17:50:33","modified_gmt":"2024-02-22T09:50:33","slug":"%e5%8d%95%e7%bb%86%e8%83%9e%e6%95%b0%e6%8d%aemarker%e5%9f%ba%e5%9b%a0%e5%b0%8f%e6%8f%90%e7%90%b4%e5%9b%be%e7%bb%98%e5%88%b6","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=29233","title":{"rendered":"\u5355\u7ec6\u80de\u6570\u636emarker\u57fa\u56e0\u5c0f\u63d0\u7434\u56fe\u7ed8\u5236"},"content":{"rendered":"

\u5355\u7ec6\u80de\u6570\u636emarker\u57fa\u56e0\u5c0f\u63d0\u7434\u56fe\u7ed8\u5236<\/p>\n

\u4eca\u5929\u5c0f\u679c\u60f3\u901a\u8fc7\u5355\u7ec6\u80de\u6570\u636emarker\u57fa\u56e0\u8868\u8fbe\u91cf\u6570\u636e\u6765\u7ed8\u5236\u5206\u9762\u5c0f\u63d0\u7434\u56fe\uff0c\u901a\u8fc7\u5c0f\u63d0\u7434\u56fe\u6765\u5c55\u793amarker\u57fa\u56e0\u5728\u4e0d\u540c\u7ec6\u80de\u7fa4\u4e2d\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u5e76\u505a\u4e86\u663e\u8457\u6027\u5206\u6790\uff0c\u8bdd\u4e0d\u591a\u8bf4\u5f00\u59cb\u4eca\u5929\u7684\u5206\u4eab\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n

    \n
  1. \n \u5b89\u88c5\u9700\u8981\u7684R\u5305<\/li>\n<\/ol>\n

    install.packages(\u201cggplot2\u201d)<\/p>\n

    install.packages(\u201cggsci\u201d)<\/p>\n

    install.packages(\u201cggpubr\u201d)<\/p>\n

    install.packages(\u201crstatix\u201d)<\/p>\n

    install.packafes(\u201ctidyverse\u201d)<\/p>\n

      \n
    1. \n \u8f7d\u5165\u9700\u8981\u7684R\u5305<\/li>\n<\/ol>\n

      library(ggplot2)<\/p>\n

      library(ggsci)<\/p>\n

      library(ggpubr)<\/p>\n

      library(rstatix)<\/p>\n

      library(tidyverse)<\/p>\n

        \n
      1. \n \u793a\u4f8b\u6570\u636e<\/li>\n<\/ol>\n

        df <- read.csv(“scviolin.csv”, header = T)<\/p>\n

        # \u6309\u81ea\u5df1\u7684\u8981\u6c42\u6392\u5e8f\uff0c\u8be5\u987a\u5e8f\u5bf9\u5e94\u56fe\u4e2d\u7684\u987a\u5e8f<\/p>\n

        df$aging <- factor(df$aging, levels = c(“Y”, “O”))<\/p>\n

        df$cluster_short <- factor(df$cluster_short, levels = c(“OO”,”GC”, “SC”, “SMC”, “EC”, “NKT”, “M”))<\/p>\n

        df$gene <- factor(df$gene, levels = c(“DDX4”, “AMH”, “COL1A2”, “ACTG2”, “CDH5”, “KLRB1”, “CD163”))<\/p>\n

        \"Dingtalk_20230309134908\"<\/p>\n

          \n
        1. \n \u7ed8\u56fe\u4ee3\u7801<\/li>\n<\/ol>\n

          #\u663e\u8457\u6027\u5206\u6790<\/p>\n

          stat.test <- df %>%<\/p>\n

          group_by(cluster_short, gene) %>%<\/p>\n

          t_test(expression ~ aging) %>%<\/p>\n

          adjust_pvalue(method = “bonferroni”) %>%<\/p>\n

          add_significance()<\/p>\n

          stat.test <- stat.test %>% add_xy_position(x = “aging”)<\/p>\n

          #\u5f00\u59cb\u7ed8\u56fe<\/p>\n

          p1 <- ggviolin(df, x = “aging”, y = “expression”,<\/p>\n

          fill = “aging”,<\/p>\n

          facet = c(“gene”,”cluster_short”)) + #\u5206\u9762<\/p>\n

          scale_fill_npg() +<\/p>\n

          xlab(“”) +<\/p>\n

          ylab(“Expression”) +<\/p>\n

          theme(panel.grid = element_blank(),<\/p>\n

          strip.text.y = element_text(angle = 0),<\/p>\n

          strip.background.x = element_rect(fill = “grey95”), # \u9876\u90e8\u586b\u5145\u6d45\u7070\u8272<\/p>\n

          strip.background.y = element_blank())<\/p>\n

          p1 +<\/p>\n

          stat_pvalue_manual(stat.test,<\/p>\n

          bracket.nudge.y = 1,<\/p>\n

          hide.ns = TRUE, #\u4e0d\u663e\u793a\u4e0d\u663e\u8457\u7684<\/p>\n

          label = “{p.adj}{p.adj.signif}”, # pvalue\u548c\u661f\u6807<\/p>\n

          label.size = 3, # pvalue\u5b57\u7684\u5927\u5c0f<\/p>\n

          vjust = -0.5) + # pvalue\u7684\u4e0a\uff08\u8d1f\uff09\u4e0b\u4f4d\u7f6e<\/p>\n

          scale_y_continuous(expand = expansion(mult = c(0, 0.1))) #\u7ed9pvalue\u6587\u5b57\u7559\u8db3\u7a7a\u95f4<\/p>\n

          ggsave(“scViolin.pdf”, width = 9, height = 9)<\/p>\n

          \"Dingtalk_20230309140700\"<\/p>\n

          \u5c0f\u679c\u6700\u7ec8\u7ed8\u5236\u51fa\u4e86\u8fd9\u5f20\u6f02\u4eae\u7684\u5c0f\u63d0\u7434\u56fe\uff0c\u770b\u8d77\u6765\u6548\u679c\u8fd8\u4e0d\u9519\u5965\uff0c\u5728\u8fd9\u91cc\u5b89\u5229\u4e00\u4e0b\u5965\uff0c\u672c\u516c\u53f8\u670d\u52a1\u5668\u5df2\u7ecf\u5b89\u88c5\u597d\u76f8\u5173\u5355\u7ec6\u80de\u6570\u636e\u5206\u6790\u7684R\u5305\u548c\u8f6f\u4ef6\uff0c\u6709\u9700\u8981\u7684\u53ef\u4ee5\u8054\u7cfb\u5c0f\u679c\uff0c\u672c\u671f\u7684\u5206\u4eab\u5c31\u5230\u8fd9\u91cc\u4e0b\u671f\u5728\u89c1\u5965\u3002<\/p>\n

          \n \u5355\u7ec6\u80de\u6570\u636emarker\u57fa\u56e0\u5c0f\u63d0\u7434\u56fe\u7ed8\u5236\n<\/p>\n

          \n \u4eca\u5929\u5c0f\u679c\u60f3\u901a\u8fc7\u5355\u7ec6\u80de\u6570\u636emarker\u57fa\u56e0\u8868\u8fbe\u91cf\u6570\u636e\u6765\u7ed8\u5236\u5206\u9762\u5c0f\u63d0\u7434\u56fe\uff0c\u901a\u8fc7\u5c0f\u63d0\u7434\u56fe\u6765\u5c55\u793amarker\u57fa\u56e0\u5728\u4e0d\u540c\u7ec6\u80de\u7fa4\u4e2d\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u5e76\u505a\u4e86\u663e\u8457\u6027\u5206\u6790\uff0c\u8bdd\u4e0d\u591a\u8bf4\u5f00\u59cb\u4eca\u5929\u7684\u5206\u4eab\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a\n<\/p>\n

            \n
          1. \n \u5b89\u88c5\u9700\u8981\u7684R\u5305\n<\/li>\n<\/ol>\n

            \n install.packages(\u201cggplot2\u201d)\n<\/p>\n

            \n install.packages(\u201cggsci\u201d)\n<\/p>\n

            \n install.packages(\u201cggpubr\u201d)\n<\/p>\n

            \n install.packages(\u201crstatix\u201d)\n<\/p>\n

            \n install.packafes(\u201ctidyverse\u201d)\n<\/p>\n

              \n
            1. \n \u8f7d\u5165\u9700\u8981\u7684R\u5305\n<\/li>\n<\/ol>\n

              \n library(ggplot2)\n<\/p>\n

              \n library(ggsci)\n<\/p>\n

              \n library(ggpubr)\n<\/p>\n

              \n library(rstatix)\n<\/p>\n

              \n library(tidyverse)\n<\/p>\n

                \n
              1. \n \u793a\u4f8b\u6570\u636e\n<\/li>\n<\/ol>\n

                \n df <- read.csv(“scviolin.csv”, header = T)\n<\/p>\n

                \n # \u6309\u81ea\u5df1\u7684\u8981\u6c42\u6392\u5e8f\uff0c\u8be5\u987a\u5e8f\u5bf9\u5e94\u56fe\u4e2d\u7684\u987a\u5e8f\n<\/p>\n

                \n df$aging <- factor(df$aging, levels = c(“Y”, “O”))\n<\/p>\n

                \n df$cluster_short <- factor(df$cluster_short, levels = c(“OO”,”GC”, “SC”, “SMC”, “EC”, “NKT”, “M”))\n<\/p>\n

                \n df$gene <- factor(df$gene, levels = c(“DDX4”, “AMH”, “COL1A2”, “ACTG2”, “CDH5”, “KLRB1”, “CD163”))\n<\/p>\n

                \n \"Dingtalk_20230309134908\"\n<\/p>\n

                  \n
                1. \n \u7ed8\u56fe\u4ee3\u7801\n<\/li>\n<\/ol>\n

                  \n #\u663e\u8457\u6027\u5206\u6790\n<\/p>\n

                  \n stat.test <- df %>%\n<\/p>\n

                  \n group_by(cluster_short, gene) %>%\n<\/p>\n

                  \n t_test(expression ~ aging) %>%\n<\/p>\n

                  \n adjust_pvalue(method = “bonferroni”) %>%\n<\/p>\n

                  \n add_significance()\n<\/p>\n

                  \n stat.test <- stat.test %>% add_xy_position(x = “aging”)\n<\/p>\n

                  \n #\u5f00\u59cb\u7ed8\u56fe\n<\/p>\n

                  \n p1 <- ggviolin(df, x = “aging”, y = “expression”,\n<\/p>\n

                  \n fill = “aging”,\n<\/p>\n

                  \n facet = c(“gene”,”cluster_short”)) + #\u5206\u9762\n<\/p>\n

                  \n scale_fill_npg() +\n<\/p>\n

                  \n xlab(“”) +\n<\/p>\n

                  \n ylab(“Expression”) +\n<\/p>\n

                  \n theme(panel.grid = element_blank(),\n<\/p>\n

                  \n strip.text.y = element_text(angle = 0),\n<\/p>\n

                  \n strip.background.x = element_rect(fill = “grey95”), # \u9876\u90e8\u586b\u5145\u6d45\u7070\u8272\n<\/p>\n

                  \n strip.background.y = element_blank())\n<\/p>\n

                  \n p1 +\n<\/p>\n

                  \n stat_pvalue_manual(stat.test,\n<\/p>\n

                  \n bracket.nudge.y = 1,\n<\/p>\n

                  \n hide.ns = TRUE, #\u4e0d\u663e\u793a\u4e0d\u663e\u8457\u7684\n<\/p>\n

                  \n label = “{p.adj}{p.adj.signif}”, # pvalue\u548c\u661f\u6807\n<\/p>\n

                  \n label.size = 3, # pvalue\u5b57\u7684\u5927\u5c0f\n<\/p>\n

                  \n vjust = -0.5) + # pvalue\u7684\u4e0a\uff08\u8d1f\uff09\u4e0b\u4f4d\u7f6e\n<\/p>\n

                  \n scale_y_continuous(expand = expansion(mult = c(0, 0.1))) #\u7ed9pvalue\u6587\u5b57\u7559\u8db3\u7a7a\u95f4\n<\/p>\n

                  \n ggsave(“scViolin.pdf”, width = 9, height = 9)\n<\/p>\n

                  \n \"Dingtalk_20230309140700\"\n<\/p>\n

                  \n \u5c0f\u679c\u6700\u7ec8\u7ed8\u5236\u51fa\u4e86\u8fd9\u5f20\u6f02\u4eae\u7684\u5c0f\u63d0\u7434\u56fe\uff0c\u770b\u8d77\u6765\u6548\u679c\u8fd8\u4e0d\u9519\u5965\uff0c\u5728\u8fd9\u91cc\u5b89\u5229\u4e00\u4e0b\u5965\uff0c\u672c\u516c\u53f8\u670d\u52a1\u5668\u5df2\u7ecf\u5b89\u88c5\u597d\u76f8\u5173\u5355\u7ec6\u80de\u6570\u636e\u5206\u6790\u7684R\u5305\u548c\u8f6f\u4ef6\uff0c\u6709\u9700\u8981\u7684\u53ef\u4ee5\u8054\u7cfb\u5c0f\u679c\uff0c\u672c\u671f\u7684\u5206\u4eab\u5c31\u5230\u8fd9\u91cc\u4e0b\u671f\u5728\u89c1\u5965\u3002\n<\/p>\n

                  \n \u5355\u7ec6\u80de\u6570\u636emarker\u57fa\u56e0\u5c0f\u63d0\u7434\u56fe\u7ed8\u5236\n<\/p>\n

                  \n \u4eca\u5929\u5c0f\u679c\u60f3\u901a\u8fc7\u5355\u7ec6\u80de\u6570\u636emarker\u57fa\u56e0\u8868\u8fbe\u91cf\u6570\u636e\u6765\u7ed8\u5236\u5206\u9762\u5c0f\u63d0\u7434\u56fe\uff0c\u901a\u8fc7\u5c0f\u63d0\u7434\u56fe\u6765\u5c55\u793amarker\u57fa\u56e0\u5728\u4e0d\u540c\u7ec6\u80de\u7fa4\u4e2d\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u5e76\u505a\u4e86\u663e\u8457\u6027\u5206\u6790\uff0c\u8bdd\u4e0d\u591a\u8bf4\u5f00\u59cb\u4eca\u5929\u7684\u5206\u4eab\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a\n<\/p>\n

                    \n
                  1. \n \u5b89\u88c5\u9700\u8981\u7684R\u5305\n<\/li>\n<\/ol>\n

                    \n install.packages(\u201cggplot2\u201d)\n<\/p>\n

                    \n install.packages(\u201cggsci\u201d)\n<\/p>\n

                    \n install.packages(\u201cggpubr\u201d)\n<\/p>\n

                    \n install.packages(\u201crstatix\u201d)\n<\/p>\n

                    \n install.packafes(\u201ctidyverse\u201d)\n<\/p>\n

                      \n
                    1. \n \u8f7d\u5165\u9700\u8981\u7684R\u5305\n<\/li>\n<\/ol>\n

                      \n library(ggplot2)\n<\/p>\n

                      \n library(ggsci)\n<\/p>\n

                      \n library(ggpubr)\n<\/p>\n

                      \n library(rstatix)\n<\/p>\n

                      \n library(tidyverse)\n<\/p>\n

                        \n
                      1. \n \u793a\u4f8b\u6570\u636e\n<\/li>\n<\/ol>\n

                        \n df <- read.csv(“scviolin.csv”, header = T)\n<\/p>\n

                        \n # \u6309\u81ea\u5df1\u7684\u8981\u6c42\u6392\u5e8f\uff0c\u8be5\u987a\u5e8f\u5bf9\u5e94\u56fe\u4e2d\u7684\u987a\u5e8f\n<\/p>\n

                        \n df$aging <- factor(df$aging, levels = c(“Y”, “O”))\n<\/p>\n

                        \n df$cluster_short <- factor(df$cluster_short, levels = c(“OO”,”GC”, “SC”, “SMC”, “EC”, “NKT”, “M”))\n<\/p>\n

                        \n df$gene <- factor(df$gene, levels = c(“DDX4”, “AMH”, “COL1A2”, “ACTG2”, “CDH5”, “KLRB1”, “CD163”))\n<\/p>\n

                        \n \"Dingtalk_20230309134908\"\n<\/p>\n

                          \n
                        1. \n \u7ed8\u56fe\u4ee3\u7801\n<\/li>\n<\/ol>\n

                          \n #\u663e\u8457\u6027\u5206\u6790\n<\/p>\n

                          \n stat.test <- df %>%\n<\/p>\n

                          \n group_by(cluster_short, gene) %>%\n<\/p>\n

                          \n t_test(expression ~ aging) %>%\n<\/p>\n

                          \n adjust_pvalue(method = “bonferroni”) %>%\n<\/p>\n

                          \n add_significance()\n<\/p>\n

                          \n stat.test <- stat.test %>% add_xy_position(x = “aging”)\n<\/p>\n

                          \n #\u5f00\u59cb\u7ed8\u56fe\n<\/p>\n

                          \n p1 <- ggviolin(df, x = “aging”, y = “expression”,\n<\/p>\n

                          \n fill = “aging”,\n<\/p>\n

                          \n facet = c(“gene”,”cluster_short”)) + #\u5206\u9762\n<\/p>\n

                          \n scale_fill_npg() +\n<\/p>\n

                          \n xlab(“”) +\n<\/p>\n

                          \n ylab(“Expression”) +\n<\/p>\n

                          \n theme(panel.grid = element_blank(),\n<\/p>\n

                          \n strip.text.y = element_text(angle = 0),\n<\/p>\n

                          \n strip.background.x = element_rect(fill = “grey95”), # \u9876\u90e8\u586b\u5145\u6d45\u7070\u8272\n<\/p>\n

                          \n strip.background.y = element_blank())\n<\/p>\n

                          \n p1 +\n<\/p>\n

                          \n stat_pvalue_manual(stat.test,\n<\/p>\n

                          \n bracket.nudge.y = 1,\n<\/p>\n

                          \n hide.ns = TRUE, #\u4e0d\u663e\u793a\u4e0d\u663e\u8457\u7684\n<\/p>\n

                          \n label = “{p.adj}{p.adj.signif}”, # pvalue\u548c\u661f\u6807\n<\/p>\n

                          \n label.size = 3, # pvalue\u5b57\u7684\u5927\u5c0f\n<\/p>\n

                          \n vjust = -0.5) + # pvalue\u7684\u4e0a\uff08\u8d1f\uff09\u4e0b\u4f4d\u7f6e\n<\/p>\n

                          \n scale_y_continuous(expand = expansion(mult = c(0, 0.1))) #\u7ed9pvalue\u6587\u5b57\u7559\u8db3\u7a7a\u95f4\n<\/p>\n

                          \n ggsave(“scViolin.pdf”, width = 9, height = 9)\n<\/p>\n

                          \n \"Dingtalk_20230309140700\"\n<\/p>\n

                          \n \u5c0f\u679c\u6700\u7ec8\u7ed8\u5236\u51fa\u4e86\u8fd9\u5f20\u6f02\u4eae\u7684\u5c0f\u63d0\u7434\u56fe\uff0c\u770b\u8d77\u6765\u6548\u679c\u8fd8\u4e0d\u9519\u5965\uff0c\u5728\u8fd9\u91cc\u5b89\u5229\u4e00\u4e0b\u5965\uff0c\u672c\u516c\u53f8\u670d\u52a1\u5668\u5df2\u7ecf\u5b89\u88c5\u597d\u76f8\u5173\u5355\u7ec6\u80de\u6570\u636e\u5206\u6790\u7684R\u5305\u548c\u8f6f\u4ef6\uff0c\u6709\u9700\u8981\u7684\u53ef\u4ee5\u8054\u7cfb\u5c0f\u679c\uff0c\u672c\u671f\u7684\u5206\u4eab\u5c31\u5230\u8fd9\u91cc\u4e0b\u671f\u5728\u89c1\u5965\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

                          \u5355\u7ec6\u80de\u6570\u636emarker\u57fa\u56e0\u5c0f\u63d0\u7434\u56fe\u7ed8\u5236 \u4eca\u5929\u5c0f\u679c\u60f3\u901a\u8fc7\u5355\u7ec6\u80de\u6570\u636emarker\u57fa\u56e0\u8868\u8fbe\u91cf\u6570\u636e\u6765\u7ed8\u5236\u5206\u9762\u5c0f\u63d0\u7434\u56fe\uff0c […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":""},"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/29233"}],"collection":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=29233"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/29233\/revisions"}],"predecessor-version":[{"id":29382,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/29233\/revisions\/29382"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29233"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=29233"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=29233"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}