{"id":61606,"date":"2024-11-19T15:31:07","date_gmt":"2024-11-19T07:31:07","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61606"},"modified":"2024-11-19T15:31:07","modified_gmt":"2024-11-19T07:31:07","slug":"%e5%8d%95%e7%bb%86%e8%83%9e%e4%b9%8b%e6%89%be%e5%af%bb%e5%8f%97%e5%bd%b1%e5%93%8d%e6%9c%80%e5%a4%a7%e7%9a%84%e7%bb%86%e8%83%9e%e7%b1%bb%e5%9e%8b%ef%bc%8caugur%e6%9d%a5%e5%b8%ae%e5%bf%99%e5%95%a6","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61606","title":{"rendered":"\u5355\u7ec6\u80de\u4e4b\u627e\u5bfb\u53d7\u5f71\u54cd\u6700\u5927\u7684\u7ec6\u80de\u7c7b\u578b\uff0cAugur\u6765\u5e2e\u5fd9\u5566~"},"content":{"rendered":"\n

\u5404\u4f4d\u505a\u5355\u7ec6\u80de\u7684\u5c0f\u4f19\u4f34\u4eec\u6709\u6ca1\u6709\u8fd9\u6837\u7684\u70e6\u607c\u5440\uff1f\u6700\u8fd1\u5c0f\u679c\u603b\u662f\u53d1\u73b0\u6bcf\u6b21\u90fd\u627e\u4e0d\u5230\u60f3\u8981\u5f80\u4e0b\u5206\u6790\u7684\u70b9\uff0c\u6839\u636e\u73b0\u5728\u56fa\u6709\u7684\u4e00\u4e9b\u5355\u7ec6\u80de\u6d41\u7a0b\u505a\u5230\u7ec6\u80de\u5206\u7c7b\u4e4b\u540e\u5c31\u5f88\u96be\u518d\u6293\u5230\u5f80\u4e0b\u5206\u6790\u7684\u7ec6\u80de\u7c7b\u578b\u4e86\uff0c\u600e\u4e48\u786e\u5b9a\u54ea\u4e2a\u7ec6\u80de\u7c7b\u578b\u662f\u5bf9\u6211\u4eec\u7684\u8bfe\u9898\u6709\u610f\u4e49\u7684\u5462\uff1f\u9664\u4e86\u4f20\u7edf\u7684\u6839\u636e\u7ec6\u80de\u6bd4\u4f8b\u8fd8\u786e\u5b9a\u4e4b\u5916\uff0c R\u5305Augur\u4e5f\u6765\u5e2e\u5fd9\u5566\uff01\u8fd9\u4e48\u4e00\u4e2a\u7b80\u5355\u53c8\u597d\u7528\u7684\u5305\uff0c\u545c\u545c\uff0c\u7b80\u76f4\u592a\u79f0\u5c0f\u679c\u7684\u5fc3\u610f\u4e86\uff0c\u7b80\u5355\u597d\u7528\uff0c\u7279\u522b\u597d\u4e0a\u624b\uff0c\u5373\u4fbf\u662f\u5c0f\u767d\u4e5f\u5206\u5206\u949f\u5b66\u5f97\u4f1a\u54e6\uff01\uff01\uff01<\/p>\n\n\n\n

\u540c\u65f6\u8fd9\u91cc\u5c0f\u679c\u8981\u63d0\u9192\u5927\u5bb6\u5566\uff0c\u5982\u679c\u9047\u5230\u64cd\u4f5c\u5360\u7528\u5185\u5b58\u6bd4\u8f83\u5927\u7684\u65f6\u5019\uff0c\u5c0f\u679c\u5efa\u8bae\u5927\u5bb6\u7528\u670d\u52a1\u5668\u54e6\uff0c\u4e0d\u7136\u81ea\u5df1\u7684\u7535\u8111\u8fd0\u884c\u4e0d\u5f00\uff0c\u5982\u679c\u6ca1\u6709\u81ea\u5df1\u7684\u670d\u52a1\u5668\u6b22\u8fce\u8054\u7cfb\u5c0f\u679c\u79df\u8d41\u670d\u52a1\u5668\u54e6~<\/p>\n\n\n\n

R\u5305Augur\u5c31\u662f\u7528\u6765\u8bc6\u522b\u5bf9\u5355\u7ec6\u80de\u6570\u636e\u4e2d\u7684\u751f\u7269\u6270\u52a8\u6700\u654f\u611f\u7684\u7ec6\u80de\u7c7b\u578b\uff0c\u7b80\u5355\u70b9\u6765\u8bf4\u5c31\u662f\u6bd4\u5982\u5c0f\u679c\u7814\u7a76\u7684\u662f\u8870\u8001\u7684\u8bfe\u9898\uff0c\u5047\u5982\u8be5\u6570\u636e\u7684\u6837\u672c\u7ec4\u6210\u662f8\u4e2a\u8870\u8001\u548c\u5e74\u8f7b\u7684\u67d0\u7ec4\u7ec7\u7684\u6570\u636e\uff0c\u90a3\u5bf9\u4e8e\u8fd9\u4e2a\u8bfe\u9898\u6765\u8bf4\uff0c\u8870\u8001\u53ef\u6613\u7b80\u5355\u7406\u89e3\u4e3a\u8fd9\u4e2a\u751f\u7269\u6270\u52a8\uff0c\u53c8\u6bd4\u5982\u5404\u79cd\u764c\u75c7\u6570\u636e\u7b49\u7b49\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8fd9\u4e2a\u5305\u8ba1\u7b97\u51fa\u53d7\u5230\u8870\u8001\u5f71\u54cd\u6700\u654f\u611f\u7684\u4e00\u4e2a\u6216\u51e0\u4e2a\u7ec6\u80de\u7c7b\u578b\uff0c\u8fd9\u6837\u5bf9\u4e8e\u6211\u4eec\u540e\u7eed\u7684\u7814\u7a76\u5f88\u6709\u5e2e\u52a9\u54e6\uff01\u5f53\u7136\u8fd8\u6709\u4e00\u4e9b\u75be\u75c5\u6cbb\u7597\u7684\u6570\u636e\uff0c\u5c31\u53ef\u4ee5\u6e05\u695a\u7684\u770b\u5230\u6cbb\u7597\u540e\u6700\u5173\u952e\u7684\u7ec6\u80de\u7c7b\u578b\u4e86\uff01<\/p>\n\n\n\n

\u90a3\u8fd9\u4e2a\u5305\u5f53\u7136\u4e5f\u4e0d\u662f\u5c8c\u5c8c\u65e0\u540d\u7684\uff0c\u6709\u4e00\u4e9b\u6587\u7ae0\u5c31\u4f7f\u7528\u4e86\u8fd9\u4e2a\u5305\uff0c\u6bd4\u5982\u4e0b\u9762\u8fd9\u4e9b\u5c0f\u679c\u53d1\u73b0\u7684\u4e00\u4e9b\u4f7f\u7528\u8fd9\u4e2a\u5305\u7684\u4e00\u4e9b\u6848\u4f8b\uff0c\u5982\u4e0b\uff1a<\/p>\n\n\n\n

\u6848\u4f8b\u4e00<\/strong>\uff1a<\/p>\n\n\n\n

\u6765\u6e90\u4e8e2023\u5e747\u6708\u53d1\u8868\u5728Protein & Cell\u4e0a\u7684\u9898\u4e3aA single-nucleus transcriptomic atlas of primate liver aging uncovers the pro-senescence role of SREBP2 in hepatocytes\u4e2d\u63d0\u5230<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6587\u7ae0\u7684\u63cf\u8ff0\u5982\u4e0b\uff1awe identified that Hep was the cell type most responsive to aging in the single-nucleus data<\/p>\n\n\n\n

\u662f\u7528\u7684\u5355\u6838\u7ec6\u80de\u8870\u8001\u7684\u6570\u636e\uff0c\u8ba1\u7b97\u51fa\u4e86\u8870\u8001\u6700\u654f\u611f\u7684\u4e00\u4e2a\u7ec6\u80de\u7c7b\u578bHep\u3002<\/p>\n\n\n\n

\u6848\u4f8b\u4e8c<\/strong>\uff1a<\/p>\n\n\n\n

\u6765\u6e90\u4e8e2023\u5e744\u6708\u53d1\u8868\u5728Protein & Cell\u4e0a\u7684\u9898\u4e3aSingle-nucleus transcriptomics reveals a gatekeeper role for FOXP1 in primate cardiac aging\u7684\u6587\u7ae0\u4e2d\u63d0\u5230<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

CM was the cell type most affected by aging<\/p>\n\n\n\n

\u90a3\u63a5\u4e0b\u6765\u5c31\u8ddf\u5c0f\u679c\u6765\u5b66\u4e60\u8fd9\u4e2a\u5305\u7684\u5b89\u88c5\u548c\u4f7f\u7528\u5427~<\/p>\n\n\n\n

1<\/strong>.<\/strong>\u5b89\u88c5<\/strong><\/strong><\/p>\n\n\n\n

\u4f9d\u8d56\u5305\u5b89\u88c5<\/p>\n\n\n\n

dplyr (>= 0.8.0),<\/p>\n\n\n\n

purrr (>= 0.3.2),<\/p>\n\n\n\n

tibble (>= 2.1.3),<\/p>\n\n\n\n

magrittr (>= 1.5),<\/p>\n\n\n\n

tester (>= 0.1.7),<\/p>\n\n\n\n

Matrix (>= 1.2-14),<\/p>\n\n\n\n

sparseMatrixStats (>= 0.1.0),<\/p>\n\n\n\n

parsnip (>= 0.0.2),<\/p>\n\n\n\n

recipes (>= 0.1.4),<\/p>\n\n\n\n

rsample (>= 0.0.4),<\/p>\n\n\n\n

yardstick (>= 0.0.3),<\/p>\n\n\n\n

pbmcapply (>= 1.5.0),<\/p>\n\n\n\n

lmtest (>= 0.9-37),<\/p>\n\n\n\n

rlang (>= 0.4.0),<\/p>\n\n\n\n

glmnet (>= 2.0),<\/p>\n\n\n\n

randomForest (>= 4.6-14)<\/p>\n\n\n\n

\u9700\u8981\u63d0\u524d\u5148\u5b89\u88c5\u4e00\u4e0b<\/p>\n\n\n\n

install.packages(“devtools”)<\/p>\n\n\n\n

devtools::install_github(“Bioconductor\/MatrixGenerics”)<\/p>\n\n\n\n

devtools::install_github(“const-ae\/sparseMatrixStats”)<\/p>\n\n\n\n

devtools::install_github(“neurorestore\/Augur”)<\/p>\n\n\n\n

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

2<\/strong>.<\/strong>\u8fd0\u884c<\/strong><\/strong><\/p>\n\n\n\n

\u8fd9\u91cc\u4f7f\u7528\u7684\u5b9e\u9a8c\u6570\u636e\u662fAugur\u81ea\u5e26\u7684<\/p>\n\n\n\n

data(“sc_sim”)<\/p>\n\n\n\n

\u91cc\u9762\u7684\u6570\u636e\u7684\u5206\u7c7b\u4fe1\u606f\u5982\u4e0b\uff0c\u4e00\u5171\u6709\u4e24\u5217\uff0c\u7b2c\u4e00\u5217\u662f\u6cbb\u7597\u7ec4\u4e0econtrol\u7ec4\uff0c\u7b2c\u4e8c\u5217\u5c31\u662f\u7ec6\u80de\u7c7b\u578b\uff0c\u6211\u4eec\u8fd9\u91cc\u5c31\u770b\u770b\u54ea\u4e2a\u7ec6\u80de\u7c7b\u578b\u5bf9\u4e8e\u6cbb\u7597\u7ec4\u6765\u8bf4\u6700\u4e3a\u654f\u611f\u3002<\/p>\n\n\n\n

head(sc_sim@meta.data)<\/p>\n\n\n\n

      label cell_type<\/p>\n\n\n\n

1   control CellTypeA<\/p>\n\n\n\n

2 treatment CellTypeA<\/p>\n\n\n\n

3 treatment CellTypeA<\/p>\n\n\n\n

4   control CellTypeA<\/p>\n\n\n\n

5   control CellTypeA<\/p>\n\n\n\n

6 treatment CellTypeA<\/p>\n\n\n\n

\u63a5\u7740\u8fd0\u884ccalculate_auc\uff0c\u5e76\u68c0\u67e5AUC\u9879\u4e2d\u7684\u7ec6\u80de\u7c7b\u578b\u4f18\u5148\u7ea7<\/p>\n\n\n\n

augur = calculate_auc(sc_sim)<\/p>\n\n\n\n

using default assay: RNA …<\/p>\n\n\n\n

  |============================================================================================| 100%, Elapsed 01:48<\/p>\n\n\n\n

> augur$AUC<\/p>\n\n\n\n

# A tibble: 3 \u00d7 2<\/p>\n\n\n\n

  cell_type   auc<\/p>\n\n\n\n

  <chr>     <dbl><\/p>\n\n\n\n

1 CellTypeC 0.879<\/p>\n\n\n\n

2 CellTypeB 0.747<\/p>\n\n\n\n

3 CellTypeA 0.554<\/p>\n\n\n\n

><\/p>\n\n\n\n

\u53ef\u4ee5\u770b\u5230\u6211\u4eec\u8fd9\u91cc\u53ea\u6709\u4e09\u4e2a\u7ec6\u80de\u7c7b\u578bA\uff0cB\uff0cC\uff0c\u6839\u636e\u4f18\u5148\u7ea7\uff0c\u662fC>B>A\uff0c\u90a3\u53ef\u80fd\u7ec6\u80de\u7c7b\u578bC\u5c31\u662f\u6211\u4eec\u9700\u8981\u540e\u7eed\u4e3b\u8981\u5173\u6ce8\u7684\u4e00\u4e2a\u7ec6\u80de\u7c7b\u578b\u5566\uff01<\/p>\n\n\n\n

3<\/strong>.<\/strong>\u7ed8\u56fe<\/strong><\/strong><\/p>\n\n\n\n

data <- augur$AUC<\/p>\n\n\n\n

names(data)<\/p>\n\n\n\n

data$Category <-<\/p>\n\n\n\n

    paste0(data$cell_type,” – “,round(data$auc,2))<\/p>\n\n\n\n

data$Category<\/p>\n\n\n\n

[1] “CellTypeC – 0.88” “CellTypeB – 0.75” “CellTypeA – 0.55”<\/p>\n\n\n\n

ggplot(data = data, mapping = aes(x = cell_type, y = auc, fill = cell_type)) +<\/p>\n\n\n\n

    geom_bar(stat = ‘identity’, position = ‘dodge’) +<\/p>\n\n\n\n

    scale_fill_brewer(palette = ‘Accent’)+#\u67f1\u5f62\u7684\u989c\u8272<\/p>\n\n\n\n

    ylim(c(0, 1.3))+#y\u8f74\u7684\u8303\u56f4\uff0c\u5982\u679c\u4e0d\u8bbe\u7f6e\uff0c\u6574\u4e2a\u56fe\u5c31\u662f\u4e00\u4e2a\u5706\uff0c\u8fde\u8d77\u6765\u4e86\uff0cy\u503c\u8d8a\u5927\uff0c\u56fe\u5f62\u8d8a\u77ed<\/p>\n\n\n\n

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

    coord_polar(theta = “y”)+# \u6309\u7167Y\u8f74\u65cb\u8f6c<\/p>\n\n\n\n

    geom_text(data = data, hjust = 1, size = 3,<\/p>\n\n\n\n

              aes(x = Category, y = 0, label = Category)) +#\u52a0\u8f7dX\u8f74\u4e0a\u7684\u6807\u7b7e<\/p>\n\n\n\n

    theme_minimal() +# \u53bb\u6389\u7070\u8272\u80cc\u666f<\/p>\n\n\n\n

    theme(legend.position = “none”,<\/p>\n\n\n\n

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

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

          axis.line = element_blank(),<\/p>\n\n\n\n

          axis.text.y = element_blank(),<\/p>\n\n\n\n

          axis.text.x = element_blank(),<\/p>\n\n\n\n

          axis.ticks = element_blank())<\/p>\n\n\n\n

\u901a\u8fc7\u8fd0\u884c\u4e0a\u8ff0\u7684\u4ee3\u7801\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u753b\u51fa\u6765\u548c\u6587\u7ae0\u4e2d\u4e00\u6837\u7684\u56fe\u5f62\u5566~\u6700\u540e\u7684\u56fe\u5f62\u5c31\u662f\u4e0b\u9762\u8fd9\u6837\u5566\uff01\u56e0\u4e3a\u793a\u4f8b\u6570\u636e\u7684\u7ec6\u80de\u7c7b\u578b\u8f83\u5c11\uff0c\u6240\u4ee5\u505a\u51fa\u6765\u7684\u56fe\u5f62\u4e0d\u600e\u4e48\u597d\u770b\uff0c\u5927\u5bb6\u53ef\u4ee5\u6362\u81ea\u5df1\u7684\u6570\u636e\u770b\u770b\u5440\uff0c\u56e0\u4e3a\u4e00\u822c\u5355\u7ec6\u80de\u7684\u6570\u636e\u7ec6\u80de\u7c7b\u578b\u4e5f\u662f\u6bd4\u8f83\u591a\u7684\uff0c\u57fa\u672c\u4e0a\u90fd\u662f\u5341\u51e0\u4e2a\uff0c\u8fd9\u6837\u6211\u4eec\u7684\u56fe\u5f62\u5c31\u548c\u6587\u7ae0\u4e2d\u7684\u770b\u8d77\u6765\u5dee\u4e0d\u591a\u5566\uff01<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6b64\u5916\u8fd8\u6709\u4e00\u4e9b\u7ec6\u5c0f\u7684\u53c2\u6570\u5c0f\u679c\u5e26\u5927\u5bb6\u8fdb\u884c\u8c03\u6574\uff0c\u4e00\u4e2a\u662f\u5982\u679c\u5927\u5bb6\u7684\u7ec6\u80de\u6570\u6bd4\u8f83\u591a\u7684\u65f6\u5019\uff0c\u8fd0\u884c\u8fd9\u4e00\u6b65\u5c31\u6bd4\u8f83\u56f0\u96be\u4e86\uff0c\u53ef\u4ee5\u8c03\u6574n_threads\u53c2\u6570\uff0c\u9ed8\u8ba4\u4e0b\u8be5\u53c2\u6570\u7684\u503c\u662f4\uff0c\u6211\u4eec\u53ef\u4ee5\u8c03\u6574\u62108\uff0c\u8fd9\u6837\u8fd0\u884c\u901f\u5ea6\u5c31\u662f\u4e4b\u524d\u7684\u4e24\u500d\u4e86\u3002\u5728\u5bf9\u63a5seurat\u5bf9\u8c61\u65f6\u5019\uff0cmeta.data \u4e2d\u5177\u6709\u540d\u4e3a cell_type \u548c label \u7684\u5217\uff0c\u8fd9\u610f\u5473\u7740\u6211\u4eec\u53ef\u4ee5\u5c06\u5176\u76f4\u63a5\u4f5c\u4e3a Augur \u7684\u8f93\u5165\u63d0\u4f9b\uff0c\u8f93\u5165cell_type_col\u53c2\u6570\u548clabel_col\u53c2\u6570\u5373\u53ef\u3002<\/p>\n\n\n\n

\u5f53\u7136\u5566\uff0c\u6211\u4eec\u7528\u8fd9\u4e2a\u5305\u4e5f\u662f\u4e3a\u4e86\u8ba1\u7b97\u7ec6\u80de\u7c7b\u578b\u7684\u654f\u611f\u5ea6\uff0c\u81f3\u4e8e\u7b2c\u4e09\u90e8\u5206\u7ed8\u56fe\u7684\u5f62\u5f0f\uff0c\u5982\u679c\u5927\u5bb6\u6709\u5176\u4ed6\u66f4\u5408\u9002\u4e00\u70b9\u7684\u56fe\u5f62\u4e5f\u53ef\u4ee5\u6362\u5176\u4ed6\u7684\u54e6~\u8fd9\u91cc\u5c0f\u679c\u53ea\u662f\u63d0\u4f9b\u4e00\u79cd\u5c55\u793a\u65b9\u5f0f~\u5176\u5b9e\u5982\u679c\u7ec6\u80de\u7c7b\u578b\u50cf\u5c0f\u679c\u8fd9\u79cd\u5f88\u5c11\u7684\u8bdd\uff0c\u53ef\u4ee5\u76f4\u63a5\u753bbarplot\uff0c\u5c31\u50cf\u9047\u5230\u5206\u6790\u4e9a\u7c7b\u7684\u60c5\u51b5\u65f6\u3002<\/p>\n\n\n\n

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