![\"2f0b5332-9884-433a-9708-2ffd6b4a8120\"](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/01\/2f0b5332-9884-433a-9708-2ffd6b4a8120.png?resize=640%2C551\")
<\/p>\n\u5728\u751f\u7269\u5b66\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u7ecf\u5e38\u9700\u8981\u5904\u7406\u5927\u89c4\u6a21\u7684\u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff0c\u8fd9\u4e9b\u6570\u636e\u53ef\u80fd\u6765\u81ea\u4e0d\u540c\u6761\u4ef6\u4e0b\u7684\u5b9e\u9a8c\u6216\u75be\u75c5\u6837\u672c\u3002genefilter\u63d0\u4f9b\u4e86\u4e00\u5957\u529f\u80fd\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u7528\u4e8e\u5904\u7406\u8fd9\u4e9b\u590d\u6742\u7684\u6570\u636e\uff0c\u5e76\u4ece\u4e2d\u8bc6\u522b\u51fa\u4e0e\u7814\u7a76\u95ee\u9898\u76f8\u5173\u7684\u57fa\u56e0\u3002<\/p>\n
\u8981\u4f7f\u7528genefilter\u5305\uff0c\u53ef\u4ee5\u5728R\u4e2d\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\u548c\u52a0\u8f7d\uff1a<\/p>\n
> install.packages(“genefilter”)<\/p>\n
> library(genefilter)<\/p>\n
genefilter\u7684\u4e00\u9879\u4e3b\u8981\u529f\u80fd\u662f\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u7684\u9884\u5904\u7406\u3002\u9884\u5904\u7406\u662f\u6570\u636e\u5206\u6790\u7684\u5173\u952e\u6b65\u9aa4\u4e4b\u4e00\uff0c\u5b83\u53ef\u4ee5\u6d88\u9664\u6570\u636e\u4e2d\u7684\u566a\u58f0\u3001\u5904\u7406\u7f3a\u5931\u503c\uff0c\u5e76\u5bf9\u6570\u636e\u8fdb\u884c\u5f52\u4e00\u5316\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027\u3002\u8fd9\u5bf9\u4e8e\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u5c24\u4e3a\u91cd\u8981\uff0c\u56e0\u4e3a\u5728\u5b9e\u9a8c\u4e2d\uff0c\u6570\u636e\u53ef\u80fd\u4f1a\u53d7\u5230\u591a\u79cd\u56e0\u7d20\u7684\u5f71\u54cd\uff0c\u4f8b\u5982\u6280\u672f\u5e73\u53f0\u7684\u5dee\u5f02\u3001\u5b9e\u9a8c\u6761\u4ef6\u7684\u53d8\u5316\u7b49\u3002genefilter\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5b8c\u6210\u8fd9\u4e9b\u4efb\u52a1\uff0c\u5e2e\u52a9\u7814\u7a76\u4eba\u5458\u83b7\u5f97\u9ad8\u8d28\u91cf\u7684\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u3002<\/p>\n
\u793a\u4f8b\uff1a<\/p>\n
\u5f53\u4f7f\u7528R\u4e2d\u7684genefilter\u5305\u8fdb\u884c\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u7684\u9884\u5904\u7406\u65f6\uff0c\u6211\u4eec\u901a\u5e38\u4f1a\u6d89\u53ca\u6570\u636e\u7684\u8bfb\u53d6\u3001\u6570\u636e\u6e05\u6d17\u3001\u5f52\u4e00\u5316\u548c\u53ef\u89c6\u5316\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u9884\u5904\u7406\u793a\u4f8b\uff0c\u4f7f\u7528R\u4e2d\u81ea\u5e26\u7684iris\u6570\u636e\u96c6\u3002<\/p>\n
# \u5b89\u88c5\u548c\u52a0\u8f7d\u6240\u9700\u7684\u5305<\/p>\n
> install.packages(“genefilter”)<\/p>\n
> install.packages(“Biobase”) # \u5982\u679c\u672a\u5b89\u88c5Biobase\u5305\u7684\u8bdd<\/p>\n
> library(genefilter)<\/p>\n
> library(Biobase)<\/p>\n
# \u4f7f\u7528iris\u6570\u636e\u96c6\u4f5c\u4e3a\u793a\u4f8b\u6570\u636e<\/p>\n
> data(iris)<\/p>\n
# \u5c06iris\u6570\u636e\u96c6\u7684\u524d\u56db\u5217\u4f5c\u4e3a\u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff08\u793a\u4f8b\u4e2d\u5e76\u4e0d\u662f\u771f\u5b9e\u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff09<\/p>\n
> gene_expr <- iris[, 1:4]<\/p>\n
# \u67e5\u770b\u539f\u59cb\u6570\u636e\u7684\u524d\u51e0\u884c<\/p>\n
> head(gene_expr)<\/p>\n
Sepal.Length Sepal.Width Petal.Length Petal.Width<\/p>\n
1 5.1 3.5 1.4 0.2<\/p>\n
2 4.9 3.0 1.4 0.2<\/p>\n
3 4.7 3.2 1.3 0.2<\/p>\n
4 4.6 3.1 1.5 0.2<\/p>\n
5 5.0 3.6 1.4 0.2<\/p>\n
6 5.4 3.9 1.7 0.4<\/p>\n
# \u5f52\u4e00\u5316<\/p>\n
# \u5728\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u4e2d\uff0c\u5f52\u4e00\u5316\u662f\u4e3a\u4e86\u4f7f\u4e0d\u540c\u57fa\u56e0\u7684\u8868\u8fbe\u503c\u5728\u6570\u91cf\u7ea7\u4e0a\u4fdd\u6301\u4e00\u81f4\uff0c\u4fbf\u4e8e\u6bd4\u8f83\u3002<\/p>\n
> normalized_data <- scale(gene_expr)<\/p>\n
# \u67e5\u770b\u5f52\u4e00\u5316\u540e\u7684\u6570\u636e\u524d\u51e0\u884c<\/p>\n
> head(normalized_data)<\/p>\n
Sepal.Length Sepal.Width Petal.Length Petal.Width<\/p>\n
[1,] -0.8976739 1.01560199 -1.335752 -1.311052<\/p>\n
[2,] -1.1392005 -0.13153881 -1.335752 -1.311052<\/p>\n
[3,] -1.3807271 0.32731751 -1.392399 -1.311052<\/p>\n
[4,] -1.5014904 0.09788935 -1.279104 -1.311052<\/p>\n
[5,] -1.0184372 1.24503015 -1.335752 -1.311052<\/p>\n
[6,] -0.5353840 1.93331463 -1.165809 -1.048667<\/p>\n
# Boxplot<\/p>\n
> boxplot(normalized_data, col = “lightblue”, main = “Normalized Gene Expression”,<\/p>\n
+ xlab = “Gene”, ylab = “Normalized Expression”)<\/p>\n
<\/p>\n
# Scatter plot<\/p>\n
> pairs(normalized_data, pch = 19, cex = 0.8)<\/p>\n
![\"ce948466-713d-416d-9da6-119d111efba4\"](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/01\/ce948466-713d-416d-9da6-119d111efba4.png?resize=640%2C551\")
<\/p>\n
# \u5229\u7528\u53ef\u89c6\u5316\u56fe\u8868\uff0c\u6211\u4eec\u53ef\u4ee5\u521d\u6b65\u89c2\u5bdf\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u548c\u57fa\u56e0\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u3002<\/p>\n
# \u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u7814\u7a76\u95ee\u9898\u548c\u9700\u8981\uff0c\u8fdb\u4e00\u6b65\u4f7f\u7528genefilter\u5305\u63d0\u4f9b\u7684\u529f\u80fd\uff0c\u7b5b\u9009\u51fa\u611f\u5174\u8da3\u7684\u57fa\u56e0\u5e76\u8fdb\u884c\u6df1\u5165\u5206\u6790\u3002<\/p>\n
\u9664\u4e86\u6570\u636e\u9884\u5904\u7406\u5916\uff0cgenefilter\u8fd8\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7b5b\u9009\u65b9\u6cd5\uff0c\u7528\u4e8e\u4ece\u5927\u89c4\u6a21\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u4e2d\u6311\u9009\u51fa\u4e0e\u7814\u7a76\u95ee\u9898\u6700\u76f8\u5173\u7684\u57fa\u56e0\u3002\u8fd9\u4e9b\u7b5b\u9009\u65b9\u6cd5\u53ef\u4ee5\u6839\u636e\u57fa\u56e0\u7684\u8868\u8fbe\u6a21\u5f0f\u3001\u53d8\u5f02\u7a0b\u5ea6\u3001\u76f8\u5173\u6027\u7b49\u6307\u6807\u8fdb\u884c\u7b5b\u9009\uff0c\u5e76\u4e14\u53ef\u4ee5\u6839\u636e\u7528\u6237\u7684\u9700\u6c42\u8fdb\u884c\u5b9a\u5236\u3002\u901a\u8fc7\u8fd9\u4e9b\u7b5b\u9009\u65b9\u6cd5\uff0c\u7814\u7a76\u4eba\u5458\u53ef\u4ee5\u5feb\u901f\u5730\u4ece\u6d77\u91cf\u7684\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u4e2d\u627e\u5230\u90a3\u4e9b\u5bf9\u4e8e\u7814\u7a76\u95ee\u9898\u6700\u6709\u610f\u4e49\u7684\u57fa\u56e0\uff0c\u4ece\u800c\u7f29\u5c0f\u7814\u7a76\u8303\u56f4\uff0c\u63d0\u9ad8\u7814\u7a76\u6548\u7387\u3002<\/p>\n
\u793a\u4f8b\uff1a<\/p>\n
airway\u6570\u636e\u96c6\u5305\u542b\u4e86\u6765\u81ea\u4eba\u7c7b\u6c14\u9053\u4e0a\u76ae\u7ec6\u80de\u7684RNA-seq\u6570\u636e\uff0c\u7528\u4e8e\u7814\u7a76\u54ee\u5598\u75c5\u7684\u57fa\u56e0\u8868\u8fbe\u53d8\u5316\u3002\u6211\u4eec\u5c06\u4f7f\u7528genefilter\u5305\u4e2d\u7684\u7b5b\u9009\u65b9\u6cd5\u6765\u6839\u636e\u57fa\u56e0\u7684\u8868\u8fbe\u6a21\u5f0f\u548c\u53d8\u5f02\u7a0b\u5ea6\u8fdb\u884c\u7b5b\u9009\uff0c\u5e76\u901a\u8fc7\u53ef\u89c6\u5316\u56fe\u8868\u6765\u5c55\u793a\u7ed3\u679c\u3002<\/p>\n
# \u5b89\u88c5\u548c\u52a0\u8f7d\u6240\u9700\u7684\u5305<\/p>\n
> install.packages(“genefilter”)<\/p>\n
> install.packages(“airway”) # \u5982\u679c\u672a\u5b89\u88c5airway\u5305\u7684\u8bdd<\/p>\n
> library(genefilter)<\/p>\n
> library(airway)<\/p>\n
# \u52a0\u8f7dairway\u6570\u636e\u96c6<\/p>\n
> data(airway)<\/p>\n
# \u63d0\u53d6\u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff0c\u5047\u8bbe\u6570\u636e\u662f\u77e9\u9635\u5f62\u5f0f\uff0c\u884c\u4e3a\u57fa\u56e0\uff0c\u5217\u4e3a\u6837\u672c<\/p>\n
> gene_expr <- assay(airway)<\/p>\n
# \u67e5\u770b\u539f\u59cb\u6570\u636e\u7684\u524d\u51e0\u884c<\/p>\n
> head(gene_expr)<\/p>\n
SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 SRR1039520 SRR1039521<\/p>\n
ENSG00000000003 679 448 873 408 1138 1047 770 572<\/p>\n
ENSG00000000005 0 0 0 0 0 0 0 0<\/p>\n
ENSG00000000419 467 515 621 365 587 799 417 508<\/p>\n
ENSG00000000457 260 211 263 164 245 331 233 229<\/p>\n
ENSG00000000460 60 55 40 35 78 63 76 60<\/p>\n
ENSG00000000938 0 0 2 0 1 0 0 0<\/p>\n
# \u7b5b\u9009\u65b9\u6cd5\u4e00\uff1a\u6839\u636e\u57fa\u56e0\u7684\u8868\u8fbe\u6a21\u5f0f\u8fdb\u884c\u7b5b\u9009<\/p>\n
# \u4f7f\u7528genefilter\u5305\u4e2d\u7684`rowSds`\u51fd\u6570\u8ba1\u7b97\u6bcf\u4e2a\u57fa\u56e0\u7684\u6807\u51c6\u5dee\uff08\u8868\u8fbe\u6a21\u5f0f\uff09\uff0c<\/p>\n
# \u7136\u540e\u9009\u62e9\u6807\u51c6\u5dee\u5927\u4e8e\u6307\u5b9a\u9608\u503c\u7684\u57fa\u56e0\u4f5c\u4e3a\u7b5b\u9009\u7ed3\u679c\u3002<\/p>\n
> threshold_sd <- 1 # \u8bbe\u7f6e\u6807\u51c6\u5dee\u7684\u9608\u503c<\/p>\n
> selected_genes_by_sd <- gene_expr[rowSds(gene_expr) > threshold_sd, ]<\/p>\n
# \u53ef\u89c6\u5316\uff1a\u7bb1\u7ebf\u56fe\u6bd4\u8f83\u7b5b\u9009\u524d\u540e\u7684\u6570\u636e<\/p>\n
> par(mfrow = c(1, 2))<\/p>\n
> boxplot(gene_expr, col = “lightblue”, main = “Before Filtering”, xlab = “Gene”, ylab = “Expression”)<\/p>\n
> boxplot(selected_genes_by_sd, col = “lightgreen”, main = “After Filtering by SD”, xlab = “Gene”, ylab = “Expression”)<\/p>\n
![\"4f29875a-59bf-4587-be9d-7f3ada80c686\"](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/01\/4f29875a-59bf-4587-be9d-7f3ada80c686.png?resize=640%2C551\")
<\/p>\n
# \u7b5b\u9009\u65b9\u6cd5\u4e8c\uff1a\u6839\u636e\u57fa\u56e0\u7684\u53d8\u5f02\u7a0b\u5ea6\u8fdb\u884c\u7b5b\u9009<\/p>\n
# \u4f7f\u7528genefilter\u5305\u4e2d\u7684`rowVars`\u51fd\u6570\u8ba1\u7b97\u6bcf\u4e2a\u57fa\u56e0\u7684\u65b9\u5dee\uff08\u53d8\u5f02\u7a0b\u5ea6\uff09\uff0c<\/p>\n
# \u7136\u540e\u9009\u62e9\u65b9\u5dee\u5927\u4e8e\u6307\u5b9a\u9608\u503c\u7684\u57fa\u56e0\u4f5c\u4e3a\u7b5b\u9009\u7ed3\u679c\u3002<\/p>\n
> threshold_var <- 0.1 # \u8bbe\u7f6e\u65b9\u5dee\u7684\u9608\u503c<\/p>\n
> selected_genes_by_var <- gene_expr[rowVars(gene_expr) > threshold_var, ]<\/p>\n
# \u53ef\u89c6\u5316\uff1a\u7bb1\u7ebf\u56fe\u6bd4\u8f83\u7b5b\u9009\u524d\u540e\u7684\u6570\u636e<\/p>\n
> par(mfrow = c(1, 2))<\/p>\n
> boxplot(gene_expr, col = “lightblue”, main = “Before Filtering”, xlab = “Gene”, ylab = “Expression”)<\/p>\n
> boxplot(selected_genes_by_var, col = “lightgreen”, main = “After Filtering by Variance”, xlab = “Gene”, ylab = “Expression”)<\/p>\n
![\"adf0732e-a46f-4782-aa5a-444066a99153\"](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/01\/adf0732e-a46f-4782-aa5a-444066a99153.png?resize=640%2C551\")
<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86\u4e24\u79cd\u7b5b\u9009\u65b9\u6cd5\uff1a\u57fa\u4e8e\u6807\u51c6\u5dee\uff08\u8868\u8fbe\u6a21\u5f0f\uff09\u548c\u57fa\u4e8e\u65b9\u5dee\uff08\u53d8\u5f02\u7a0b\u5ea6\uff09\u3002\u901a\u8fc7\u53ef\u89c6\u5316\u7bb1\u7ebf\u56fe\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u89c2\u5730\u6bd4\u8f83\u7b5b\u9009\u524d\u540e\u7684\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u5206\u5e03\u3002\u901a\u8fc7\u8c03\u6574\u9608\u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u7075\u6d3b\u5730\u9009\u62e9\u9002\u5408\u7279\u5b9a\u7814\u7a76\u95ee\u9898\u7684\u57fa\u56e0\u3002<\/p>\n
genefilter\u8fd8\u652f\u6301\u5e76\u884c\u8ba1\u7b97\uff0c\u8fd9\u4f7f\u5f97\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u53d8\u5f97\u66f4\u52a0\u9ad8\u6548\u3002\u5728\u5904\u7406\u5927\u89c4\u6a21\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u65f6\uff0c\u8ba1\u7b97\u590d\u6742\u5ea6\u53ef\u80fd\u975e\u5e38\u9ad8\uff0c\u4f7f\u7528\u5e76\u884c\u8ba1\u7b97\u53ef\u4ee5\u5145\u5206\u5229\u7528\u8ba1\u7b97\u673a\u8d44\u6e90\uff0c\u52a0\u901f\u6570\u636e\u5904\u7406\u8fc7\u7a0b\u3002<\/p>\n
\u4ee5\u4e0a\u5c31\u662f\u5bf9R\u8bed\u8a00\u5305genefilter\u7684\u7b80\u5355\u4ecb\u7ecd\u5566\uff0c\u5b83\u63d0\u4f9b\u4e86\u6570\u636e\u9884\u5904\u7406\u548c\u7b5b\u9009\u529f\u80fd\uff0c\u5e2e\u52a9\u7814\u7a76\u4eba\u5458\u4ece\u6d77\u91cf\u7684\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u4e2d\u6316\u6398\u51fa\u6709\u610f\u4e49\u7684\u4fe1\u606f\uff0c\u4e3a\u751f\u7269\u5b66\u7814\u7a76\u63d0\u4f9b\u6709\u529b\u7684\u652f\u6301\u3002\u65e0\u8bba\u662f\u5728\u751f\u7269\u533b\u5b66\u7814\u7a76\u3001\u751f\u7269\u4fe1\u606f\u5b66\u5206\u6790\u8fd8\u662f\u57fa\u56e0\u7ec4\u5b66\u7814\u7a76\u9886\u57df\uff0cgenefilter\u90fd\u662f\u4e00\u4e2a\u975e\u5e38\u6709\u4ef7\u503c\u7684\u5de5\u5177\uff0c\u6709\u52a9\u4e8e\u52a0\u5feb\u7814\u7a76\u8fdb\u5c55\uff0c\u4fc3\u8fdb\u751f\u7269\u5b66\u9886\u57df\u7684\u53d1\u5c55\u3002<\/p>\n
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