{"id":25514,"date":"2024-01-22T16:56:06","date_gmt":"2024-01-22T08:56:06","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25514"},"modified":"2024-01-22T16:56:08","modified_gmt":"2024-01-22T08:56:08","slug":"%e4%ba%94%e5%88%86%e9%92%9f%e7%bb%98%e5%88%b6%e7%82%ab%e9%85%b7%e7%9a%84%e7%9b%b8%e5%85%b3%e6%80%a7%e5%88%86%e6%9e%90%e5%9b%be","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25514","title":{"rendered":"\u4e94\u5206\u949f\u7ed8\u5236\u70ab\u9177\u7684\u76f8\u5173\u6027\u5206\u6790\u56fe"},"content":{"rendered":"

\u8868\u8ff0\u751f\u7269\u5b66\u7684\u610f\u4e49\u7684\u8fc7\u7a0b\u4e2d\u6211\u4eec\u79bb\u4e0d\u5f00\u76f8\u5173\u6027\u56fe\u7684\u7ed8\u5236\uff0c\u76f8\u5173\u6027\u56fe\u7684\u7ed8\u5236\u975e\u5e38\u7684\u91cd\u8981\uff0c\u6563\u70b9\u56fe\u3001\u7ebf\u6027\u56fe\u8fd8\u4f1a\u6709\u70ab\u9177\u7684\u7c7b\u4f3c\u70ed\u56fe\u7684\u5f62\u5f0f\u5448\u73b0\u76f8\u5173\u6027\u5206\u6790\u662f\u5e38\u7528\u7684\u624b\u6bb5\u3002\u76f8\u5173\u6027\u8868\u793a\u4e24\u7ec4\u6570\u636e\u4e4b\u95f4\u7684\u5173\u7cfb\u662f\u5426\u201c\u76f8\u4f3c\u201d\u6216\u201c\u76f8\u8fd1\u201d\uff1b\u9047\u5230\u591a\u7ec4\u6570\u636e\u65f6\uff0c\u76f8\u5173\u6027\u56fe\u5c31\u53ef\u4ee5\u6e05\u6670\u7684\u8868\u660e\u591a\u7ec4\u6570\u636e\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u4eca\u5929\u5c0f\u679c\u5e26\u4f60\u628a\u76f8\u5173\u6027\u5206\u6790\u7684\u6570\u636e\u505a\u7684\u6f02\u4eae\u3002<\/p>\n

corrgram\u5305\u7ed8\u5236<\/h2>\n

#\u5b89\u88c5\u52a0\u8f7d\u5305<\/p>\n

install.packages(“corrgram”)<\/p>\n

library(corrgram)<\/p>\n

# \u67e5\u770b\u6570\u636e\u96c6<\/p>\n

head(iris)<\/p>\n

\"\u622a\u5c4f2023-07-12<\/p>\n

corrgram(iris)<\/p>\n

\"\u622a\u5c4f2023-07-12<\/p>\n

# \u52a0\u8f7d corrgram \u5305<\/p>\n

library(corrgram)<\/p>\n

# \u4f7f\u7528 mtcars \u6570\u636e\u96c6<\/p>\n

data(mtcars)<\/p>\n

head(mtcars)<\/p>\n

# \u7ed8\u5236\u76f8\u5173\u6027\u56fe<\/p>\n

corrgram(mtcars, order=TRUE, lower.panel=panel.shade,<\/p>\n

upper.panel=panel.ellipse, diag.panel=panel.minmax,<\/p>\n

main=”Correlation Matrix Plot of mtcars Data”)<\/p>\n

\"\u622a\u5c4f2023-07-13 lower.panel=panel.shade: \u8fd9\u4e2a\u9009\u9879\u7528\u4e8e\u6307\u5b9a\u76f8\u5173\u6027\u56fe\u7684\u4e0b\u5c42\u9762\u677f\u7ed8\u5236\u65b9\u5f0f\u3002panel.shade\u4f1a\u7ed8\u5236\u5e95\u5c42\u9762\u677f\uff0c\u901a\u8fc7\u989c\u8272\u6765\u4ee3\u8868\u76f8\u5173\u6027\u7684\u5f3a\u5f31\u3002\u989c\u8272\u8d8a\u6d45\u8868\u793a\u76f8\u5173\u6027\u8d8a\u5f3a\uff0c\u989c\u8272\u8d8a\u6df1\u8868\u793a\u76f8\u5173\u6027\u8d8a\u5f31\u3002<\/p>\n

upper.panel=panel.ellipse: \u8fd9\u4e2a\u9009\u9879\u7528\u4e8e\u6307\u5b9a\u76f8\u5173\u6027\u56fe\u7684\u4e0a\u5c42\u9762\u677f\u7ed8\u5236\u65b9\u5f0f\u3002panel.ellipse\u4f1a\u7ed8\u5236\u4e0a\u5c42\u9762\u677f\uff0c\u901a\u8fc7\u692d\u5706\u5f62\u6765\u5c55\u793a\u76f8\u5173\u6027\u7684\u6982\u7387\u5206\u5e03\u3002\u692d\u5706\u5f62\u7684\u5927\u5c0f\u548c\u5f62\u72b6\u8868\u793a\u76f8\u5173\u6027\u7684\u5f3a\u5ea6\u548c\u65b9\u5411\u3002<\/p>\n

diag.panel=panel.minmax: \u8fd9\u4e2a\u9009\u9879\u7528\u4e8e\u6307\u5b9a\u5bf9\u89d2\u7ebf\u4e0a\u7684\u9762\u677f\u7ed8\u5236\u65b9\u5f0f\u3002panel.minmax\u4f1a\u5728\u5bf9\u89d2\u7ebf\u7684\u9762\u677f\u4e2d\u5c55\u793a\u6bcf\u4e2a\u53d8\u91cf\u7684\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u3002<\/p>\n

\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u7ed8\u56fe\u65b9\u5f0f\u3002<\/p>\n

\u7528corrplot\u5305\u7ed8\u5236<\/strong><\/h2>\n

# \u5b89\u88c5\u5e76\u52a0\u8f7dcorrplot\u5305,\u63d0\u4f9b\u4e86\u4e24\u79cd\u5b89\u88c5\u8f6f\u4ef6\u5305\u7684\u65b9\u5f0f,BiocManager\u5b89\u88c5\u4f1a\u66f4\u597d\u7528\u4e00\u4e9b\uff0c\u9047\u5230\u5b89\u88c5\u4e2d\u8df3\u51fa\u201cDo you want to install from sources the package which needs compilation? (Yes\/no\/cancel) \u201d\u7684\u8bed\u53e5\u90fd\u8f93\u5165\u201cy\u201d\u5373\u53ef\u3002\u8fd9\u662f\u56e0\u4e3a\u4e00\u4e9b\u4f9d\u8d56\u7684\u5305\u9700\u8981\u5b89\u88c5<\/p>\n

install.packages(“corrplot”)<\/p>\n

BiocManager::install(“corrplot”)<\/p>\n

library(corrplot)<\/p>\n

# \u521b\u5efa\u4e00\u4e2a\u5305\u542b\u4e09\u4e2a\u53d8\u91cf\u7684\u76f8\u5173\u6027\u77e9\u9635<\/p>\n

corr_matrix <- matrix(c(1, 0.6, 0.3,<\/p>\n

0.6, 1, 0.8,<\/p>\n

0.3, 0.8, 1), nrow = 3)<\/p>\n

# \u7ed8\u5236\u76f8\u5173\u6027\u56fe<\/p>\n

corrplot(corr_matrix, method = “circle”)<\/p>\n

\"\u622a\u5c4f2023-07-13<\/p>\n

#\u7528R\u4e2d\u81ea\u5e26\u7684\u6570\u636e\u96c6mtcars\u518d\u5c1d\u8bd5\u4e00\u4e2a\u4f8b\u5b50<\/p>\n

head(mtcars)<\/p>\n

#\u8ba1\u7b97\u201cpearson\u201d\u6765\u8ba1\u7b97\u76f8\u5173\u6027<\/p>\n

M <- cor(mtcars,method = “pearson”)<\/p>\n

head(M)<\/p>\n

corrplot(M)<\/p>\n

\"\u622a\u5c4f2023-07-13<\/p>\n

\u4e0b\u9762\u8ddf\u7740\u5c0f\u679c\u4e00\u8d77\u8c03\u8282\u4e00\u4e9b\u53c2\u6570\u6765\u4fdd\u8bc1\u56fe\u7247\u7684\u7f8e\u89c2\u6027\u53ed\uff5e<\/p>\n

1\u3001\u6807\u7b7e\u6027\u8d28<\/p>\n

corrplot(M, method = “circle”,<\/p>\n

tl.col = “black”, tl.cex = 0.8, tl.srt = 30)<\/p>\n

#\u8c03\u6807\u9898\u989c\u8272\uff0ctl\u6807\u7b7e\u7684\u7684\u6027\u8d28tl.col = “black”,\u5927\u5c0f tl.cex = 1.2,\u89d2\u5ea6 tl.srt = 45<\/p>\n

\"\u622a\u5c4f2023-07-13<\/p>\n

2\u3001\u7ed8\u56fe\u7c7b\u578b\u548c\u4f4d\u7f6e<\/p>\n

corrplot(M, method = “pie”,<\/p>\n

type = “upper”,<\/p>\n

tl.col = “black”, tl.cex = 1.2, tl.srt = 45<\/p>\n

)<\/p>\n

#method\u548ctype\u8fd8\u6709\u5f88\u591a\u9009\u9879\u54e6\uff0c\u53ef\u4ee5\u90fd\u5c1d\u8bd5\u4e00\u4e0b<\/p>\n

#method = c(“circle”, “square”, “ellipse”, “number”, “shade”, “color”, “pie”)<\/p>\n

#type = c(“full”, “lower”, “upper”)<\/p>\n

\"\u622a\u5c4f2023-07-13<\/p>\n

    \n
  1. \u7ec4\u5408\u5f0f\u7ed8\u5236<\/li>\n<\/ol>\n

    corrplot(M, method = “ellipse”, type = “upper”,<\/p>\n

    tl.col = “black”, tl.cex = 0.8, tl.srt = 45,tl.pos = “lt”)<\/p>\n

    corrplot(M, method = “number”, type = “lower”,<\/p>\n

    tl.col = “n”, tl.cex = 0.8, tl.pos = “n”,<\/p>\n

    add = T)<\/p>\n

    #\u4e0a\u8fb9\u7ed8\u5236\u692d\u5706\u5f62\u7684\u76f8\u5173\u6027\uff0c\u4e0b\u8fb9\u6807\u51fa\u76f8\u5173\u6027\u7684\u6307\u6570\uff0c\u5176\u5b9e\u5c31\u5206\u522b\u753b\u4e24\u4e2a\u56fe\uff0c\u7b2c\u4e8c\u4e2a\u56fe\u52a0\u4e00\u4e2a\u547d\u4ee4add = T\uff0c\u4ee3\u8868\u53e0\u52a0\u3002<\/p>\n

    \"\u622a\u5c4f2023-07-13<\/p>\n

    4\u3001\u8c03\u4e00\u4e2a\u8272\u5f69\u76d8<\/p>\n

    library(RColorBrewer)<\/p>\n

    addcol <- RColorBrewer::brewer.pal(12,”Set3″)<\/p>\n

    corrplot(M, method = “pie”, type = “upper”,col = addcol,<\/p>\n

    tl.col = “black”, tl.cex = 0.8, tl.srt = 45,<\/p>\n

    tl.pos = “lt”)<\/p>\n

    corrplot(M, method = “number”, type = “lower”,col = addcol,<\/p>\n

    tl.col = “n”, tl.cex = 0.8, tl.pos = “n”,<\/p>\n

    add = T)<\/p>\n

    \"\u622a\u5c4f2023-07-13<\/p>\n

    \u5c0f\u679c\u7684\u5ba1\u7f8e\u4e0d\u662f\u5f88\u597d\uff0c\u8bfb\u8005\u4eec\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u559c\u597d\uff0c\u5bfb\u627e\u4e00\u4e9b\u989c\u8272\u4e0a\u7684\u7f8e\u611f\u5440\uff5e<\/p>\n

    #\u6dfb\u52a0\u7edf\u8ba1\u5b66\u610f\u4e49\u661f\u53f7<\/p>\n

    \u8fd9\u91cc\u6dfb\u52a0\u7684\u662f\u5e38\u7528\u7684P\u503c\uff0c\u6807\u6ce8\u663e\u8457\u6027<\/p>\n

    ddcol <- RColorBrewer::brewer.pal(12,”Paired”)<\/p>\n

    testRes = cor.mtest(M, menthod = “pearson”, conf.level = 0.95)<\/p>\n

    corrplot(M, method = “color”, col = addcol,<\/p>\n

    tl.col = “black”, tl.cex = 0.8, tl.srt = 45,tl.pos = “lt”,<\/p>\n

    p.mat = testRes$p, diag = T, type = ‘upper’,<\/p>\n

    sig.level = c(0.001, 0.01, 0.05), pch.cex = 1.2,<\/p>\n

    insig = ‘label_sig’, pch.col = ‘grey20’, order = ‘AOE’)<\/p>\n

    corrplot(M, method = “number”, type = “lower”,col = addcol,<\/p>\n

    tl.col = “n”, tl.cex = 0.8, tl.pos = “n”,order = ‘AOE’,<\/p>\n

    add = T)<\/p>\n

    \"\u622a\u5c4f2023-07-13<\/p>\n

    \u9644\uff1a<\/p>\n

    \u5c0f\u679c\u60f3\u628a\u8fd9\u4e2a\u51fd\u6570\u7684\u5177\u4f53\u53c2\u6570\u548c\u7528\u6cd5\u653e\u5728\u8fd9\u91cc<\/p>\n

    corrplot(corr,<\/p>\n

    method = c(“circle”, “square”, “ellipse”, “number”, “shade”, “color”, “pie”),<\/p>\n

    type = c(“full”, “lower”, “upper”), add = FALSE,<\/p>\n

    col = NULL, bg = “white”, title = “”, is.corr = TRUE,<\/p>\n

    diag = TRUE, outline = FALSE, mar = c(0,0,0,0),<\/p>\n

    addgrid.col = NULL, addCoef.col = NULL, addCoefasPercent = FALSE,<\/p>\n

    order = c(“original”, “AOE”, “FPC”, “hclust”, “alphabet”),<\/p>\n

    hclust.method = c(“complete”, “ward”, “single”, “average”,<\/p>\n

    “mcquitty”, “median”, “centroid”),<\/p>\n

    addrect = NULL, rect.col = “black”, rect.lwd = 2,<\/p>\n

    tl.pos = NULL, tl.cex = 1,<\/p>\n

    tl.col = “red”, tl.offset = 0.4, tl.srt = 90,<\/p>\n

    cl.pos = NULL, cl.lim = NULL,<\/p>\n

    cl.length = NULL, cl.cex = 0.8, cl.ratio = 0.15,<\/p>\n

    cl.align.text = “c”,cl.offset = 0.5,<\/p>\n

    addshade = c(“negative”, “positive”, “all”),<\/p>\n

    shade.lwd = 1, shade.col = “white”,<\/p>\n

    p.mat = NULL, sig.level = 0.05,<\/p>\n

    insig = c(“pch”,”p-value”,”blank”, “n”),<\/p>\n

    pch = 4, pch.col = “black”, pch.cex = 3,<\/p>\n

    plotCI = c(“n”,”square”, “circle”, “rect”),<\/p>\n

    lowCI.mat = NULL, uppCI.mat = NULL, …)<\/p>\n

    corr\uff1a\u9700\u8981\u53ef\u89c6\u5316\u7684\u76f8\u5173\u7cfb\u6570\u77e9\u9635<\/p>\n

    method\uff1a\u6307\u5b9a\u53ef\u89c6\u5316\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u662f\u5706\u5f62\u3001\u65b9\u5f62\u3001\u692d\u5706\u5f62\u3001\u6570\u503c\u3001\u9634\u5f71\u3001\u989c\u8272\u6216\u997c\u56fe\u5f62<\/p>\n

    type\uff1a\u6307\u5b9a\u5c55\u793a\u7684\u65b9\u5f0f\uff0c\u53ef\u4ee5\u662f\u5b8c\u5168\u7684\u3001\u4e0b\u4e09\u89d2\u6216\u4e0a\u4e09\u89d2\uff08\u201cfull\u201d\uff0c\u201cupper”\u548c”lower\u201d\uff09<\/p>\n

    col\uff1a\u6307\u5b9a\u56fe\u5f62\u5c55\u793a\u7684\u989c\u8272\uff0c\u9ed8\u8ba4\u4ee5\u5747\u5300\u7684\u989c\u8272\u5c55\u793a<\/p>\n

    bg\uff1a\u6307\u5b9a\u56fe\u7684\u80cc\u666f\u8272<\/p>\n

    title\uff1a\u4e3a\u56fe\u5f62\u6dfb\u52a0\u6807\u9898<\/p>\n

    is.corr\uff1a\u662f\u5426\u4e3a\u76f8\u5173\u7cfb\u6570\u7ed8\u56fe\uff0c\u9ed8\u8ba4\u4e3aTRUE\uff0c\u540c\u6837\u4e5f\u53ef\u4ee5\u5b9e\u73b0\u975e\u76f8\u5173\u7cfb\u6570\u7684\u53ef\u89c6\u5316\uff0c\u53ea\u9700\u4f7f\u8be5\u53c2\u6570\u8bbe\u4e3aFALSE\u5373\u53ef<\/p>\n

    diag\uff1a\u662f\u5426\u5c55\u793a\u5bf9\u89d2\u7ebf\u4e0a\u7684\u7ed3\u679c\uff0c\u9ed8\u8ba4\u4e3aTRUE<\/p>\n

    outline\uff1a\u662f\u5426\u7ed8\u5236\u5706\u5f62\u3001\u65b9\u5f62\u6216\u692d\u5706\u5f62\u7684\u8f6e\u5ed3\uff0c\u9ed8\u8ba4\u4e3aFALSE<\/p>\n

    mar\uff1a\u5177\u4f53\u8bbe\u7f6e\u56fe\u5f62\u7684\u56db\u8fb9\u95f4\u8ddd<\/p>\n

    addgrid.col\uff1a\u5f53\u9009\u62e9\u7684\u65b9\u6cd5\u4e3a\u989c\u8272\u6216\u9634\u5f71\u65f6\uff0c\u9ed8\u8ba4\u7684\u7f51\u683c\u7ebf\u989c\u8272\u4e3a\u767d\u8272\uff0c\u5426\u5219\u4e3a\u7070\u8272<\/p>\n

    addCoef.col\uff1a\u4e3a\u76f8\u5173\u7cfb\u6570\u6dfb\u52a0\u989c\u8272\uff0c\u9ed8\u8ba4\u4e0d\u6dfb\u52a0\u76f8\u5173\u7cfb\u6570\uff0c\u53ea\u6709\u65b9\u6cd5\u4e3anumber\u65f6\uff0c\u8be5\u53c2\u6570\u624d\u8d77\u4f5c\u7528<\/p>\n

    addCoefasPercent\uff1a\u4e3a\u8282\u7701\u7ed8\u56fe\u7a7a\u95f4\uff0c\u662f\u5426\u5c06\u76f8\u5173\u7cfb\u6570\u8f6c\u6362\u4e3a\u767e\u5206\u6bd4\u683c\u5f0f\uff0c\u9ed8\u8ba4\u4e3aFALSE<\/p>\n

    order\uff1a\u6307\u5b9a\u76f8\u5173\u7cfb\u6570\u6392\u5e8f\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u662f\u539f\u59cb\u987a\u5e8f(original)\u3001\u7279\u5f81\u5411\u91cf\u89d2\u5e8f(AOE)\u3001\u7b2c\u4e00\u4e3b\u6210\u5206\u987a\u5e8f(FPC)\u3001\u5c42\u6b21\u805a\u7c7b\u987a\u5e8f(hclust)\u548c\u5b57\u6bcd\u987a\u5e8f\uff0c\u4e00\u822c\u201dAOE\u201d\u6392\u5e8f\u7ed3\u679c\u90fd\u6bd4\u201dFPC\u201d\u8981\u597d<\/p>\n

    hclust.method\uff1a\u5f53order\u4e3ahclust\u65f6\uff0c\u8be5\u53c2\u6570\u53ef\u4ee5\u662f\u5c42\u6b21\u805a\u7c7b\u4e2dward\u6cd5\u3001\u6700\u5927\u8ddd\u79bb\u6cd5\u7b497\u79cd\u4e4b\u4e00<\/p>\n

    addrect\uff1a\u5f53order\u4e3ahclust\u65f6\uff0c\u53ef\u4ee5\u4e3a\u6dfb\u52a0\u76f8\u5173\u7cfb\u6570\u56fe\u6dfb\u52a0\u77e9\u5f62\u6846\uff0c\u9ed8\u8ba4\u4e0d\u6dfb\u52a0\u6846\uff0c\u5982\u679c\u60f3\u6dfb\u52a0\u6846\u65f6\uff0c\u53ea\u9700\u4e3a\u8be5\u53c2\u6570\u6307\u5b9a\u4e00\u4e2a\u6574\u6570\u5373\u53ef<\/p>\n

    rect.col\uff1a\u6307\u5b9a\u77e9\u5f62\u6846\u7684\u989c\u8272<\/p>\n

    rect.lwd\uff1a\u6307\u5b9a\u77e9\u5f62\u6846\u7684\u7ebf\u5bbd<\/p>\n

    tl.pos\uff1a\u6307\u5b9a\u6587\u672c\u6807\u7b7e(\u53d8\u91cf\u540d\u79f0)\u7684\u4f4d\u7f6e\uff0c\u5f53type=full\u65f6\uff0c\u9ed8\u8ba4\u6807\u7b7e\u4f4d\u7f6e\u5728\u5de6\u8fb9\u548c\u9876\u90e8(lt)\uff0c\u5f53type=lower\u65f6\uff0c\u9ed8\u8ba4\u6807\u7b7e\u5728\u5de6\u8fb9\u548c\u5bf9\u89d2\u7ebf(ld)\uff0c\u5f53type=upper\u65f6\uff0c\u9ed8\u8ba4\u6807\u7b7e\u5728\u9876\u90e8\u548c\u5bf9\u89d2\u7ebf\uff0cd\u8868\u793a\u5bf9\u89d2\u7ebf\uff0cn\u8868\u793a\u4e0d\u6dfb\u52a0\u6587\u672c\u6807\u7b7e<\/p>\n

    tl.cex\uff1a\u6307\u5b9a\u6587\u672c\u6807\u7b7e\u7684\u5927\u5c0f<\/p>\n

    tl.col\uff1a\u6307\u5b9a\u6587\u672c\u6807\u7b7e\u7684\u989c\u8272<\/p>\n

    cl.pos\uff1a\u56fe\u4f8b\uff08\u989c\u8272\uff09\u4f4d\u7f6e\uff0c\u5f53type=upper\u6216full\u65f6\uff0c\u56fe\u4f8b\u5728\u53f3\u8868\u00ae\uff0c\u5f53type=lower\u65f6\uff0c\u56fe\u4f8b\u5728\u5e95\u90e8\uff0c\u4e0d\u9700\u8981\u56fe\u4f8b\u65f6\uff0c\u53ea\u9700\u6307\u5b9a\u8be5\u53c2\u6570\u4e3an<\/p>\n

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    shade.lwd\uff1a\u6307\u5b9a\u9634\u5f71\u7684\u7ebf\u5bbd<\/p>\n

    shade.col\uff1a\u6307\u5b9a\u9634\u5f71\u7ebf\u7684\u989c\u8272<\/p>\n

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