{"id":26117,"date":"2024-01-24T17:25:58","date_gmt":"2024-01-24T09:25:58","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=26117"},"modified":"2024-01-24T17:26:00","modified_gmt":"2024-01-24T09:26:00","slug":"r%e8%af%ad%e8%a8%80%e5%8c%85genomicranges%e5%b8%a6%e4%bd%a0%e4%ba%94%e5%88%86%e9%92%9f%e5%bf%ab%e9%80%9f%e5%a4%84%e7%90%86%e5%9f%ba%e5%9b%a0%e7%bb%84%e5%8c%ba%e9%97%b4%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=26117","title":{"rendered":"R\u8bed\u8a00\u5305GenomicRanges\u5e26\u4f60\u4e94\u5206\u949f\u5feb\u901f\u5904\u7406\u57fa\u56e0\u7ec4\u533a\u95f4\u6570\u636e"},"content":{"rendered":"

GenomicRanges\u662fR\u8bed\u8a00\u4e2d\u4e00\u4e2a\u5f3a\u5927\u800c\u7075\u6d3b\u7684\u751f\u7269\u4fe1\u606f\u5b66\u5305\uff0c\u4e3b\u8981\u7528\u4e8e\u5904\u7406\u57fa\u56e0\u7ec4\u533a\u95f4\u6570\u636e\u3002\u5728\u751f\u7269\u4fe1\u606f\u5b66\u9886\u57df\uff0c\u57fa\u56e0\u7ec4\u533a\u95f4\u6570\u636e\u662f\u6307\u5177\u6709\u4f4d\u7f6e\u4fe1\u606f\u7684\u751f\u7269\u5b66\u5b9e\u4f53\uff0c\u4f8b\u5982\u57fa\u56e0\u3001\u67d3\u8272\u4f53\u533a\u57df\u3001\u53d8\u5f02\u4f4d\u70b9\u7b49\u3002\u8fd9\u4e9b\u533a\u95f4\u6570\u636e\u5728\u57fa\u56e0\u7ec4\u5b66\u3001\u8f6c\u5f55\u7ec4\u5b66\u548c\u8868\u89c2\u57fa\u56e0\u7ec4\u5b66\u7b49\u7814\u7a76\u4e2d\u8d77\u7740\u91cd\u8981\u4f5c\u7528\u3002GenomicRanges\u5305\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u51fd\u6570\u548c\u6570\u636e\u7ed3\u6784\uff0c\u7528\u4e8e\u5b58\u50a8\u3001\u64cd\u4f5c\u548c\u53ef\u89c6\u5316\u57fa\u56e0\u7ec4\u533a\u95f4\u6570\u636e\uff0c\u4f7f\u751f\u7269\u4fe1\u606f\u5b66\u5bb6\u548c\u57fa\u7840\u79d1\u7814\u4eba\u5458\u80fd\u591f\u66f4\u52a0\u9ad8\u6548\u5730\u8fdb\u884c\u57fa\u56e0\u7ec4\u6570\u636e\u7684\u5206\u6790\u548c\u89e3\u91ca\u3002<\/p>\n

GenomicRanges\u5305\u63d0\u4f9b\u4e86\u5e7f\u6cdb\u7684\u51fd\u6570\u548c\u65b9\u6cd5\uff0c\u7528\u4e8e\u5bf9`GRanges`\u5bf9\u8c61\u8fdb\u884c\u64cd\u4f5c\u548c\u5206\u6790\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528`range()`\u51fd\u6570\u6765\u8ba1\u7b97\u533a\u95f4\u7684\u957f\u5ea6\uff0c\u4f7f\u7528`overlaps()`\u51fd\u6570\u6765\u67e5\u627e\u91cd\u53e0\u7684\u533a\u95f4\uff0c\u4f7f\u7528`subset()`\u51fd\u6570\u6765\u9009\u62e9\u7279\u5b9a\u67d3\u8272\u4f53\u6216\u4f4d\u7f6e\u7684\u533a\u95f4\u7b49\u7b49\u3002\u8fd9\u4e9b\u51fd\u6570\u4f7f\u5f97\u7528\u6237\u80fd\u591f\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7b5b\u9009\u3001\u5408\u5e76\u548c\u8f6c\u6362\uff0c\u4ece\u800c\u66f4\u597d\u5730\u7406\u89e3\u57fa\u56e0\u7ec4\u6570\u636e\u7684\u7279\u5f81\u548c\u53d8\u5316\u3002<\/p>\n

\u8981\u4f7f\u7528GenomicRanges\u5305\uff0c\u53ef\u4ee5\u5728R\u4e2d\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\u548c\u52a0\u8f7d\uff1a<\/p>\n

> install.packages(“GenomicRanges”) #\u5b89\u88c5\u8bed\u8a00\u5305<\/p>\n

> library(GenomicRanges) #\u52a0\u8f7d\u8bed\u8a00\u5305<\/p>\n

\u4e00\u4e2a\u91cd\u8981\u7684\u7279\u6027\u662f\uff0cGenomicRanges\u5305\u8fd8\u652f\u6301\u5bf9\u57fa\u56e0\u7ec4\u533a\u95f4\u8fdb\u884c\u96c6\u5408\u8fd0\u7b97\u3002\u8fd9\u610f\u5473\u7740\u7528\u6237\u53ef\u4ee5\u5bf9\u591a\u4e2a`GRanges`\u5bf9\u8c61\u8fdb\u884c\u5e76\u96c6\u3001\u4ea4\u96c6\u3001\u5dee\u96c6\u7b49\u8fd0\u7b97\uff0c\u4ece\u800c\u8fdb\u884c\u66f4\u590d\u6742\u7684\u533a\u95f4\u64cd\u4f5c\u548c\u5206\u6790\u3002\u8fd9\u5bf9\u4e8e\u6574\u5408\u591a\u4e2a\u6570\u636e\u6e90\u3001\u6bd4\u8f83\u5b9e\u9a8c\u6761\u4ef6\u6216\u6837\u672c\u4e4b\u95f4\u7684\u5dee\u5f02\u975e\u5e38\u6709\u7528\uff0c\u6709\u52a9\u4e8e\u53d1\u73b0\u7279\u5b9a\u751f\u7269\u5b66\u4e8b\u4ef6\u7684\u5171\u540c\u6a21\u5f0f\u6216\u5dee\u5f02\u3002<\/p>\n

\u793a\u4f8b\uff1a<\/p>\n

\u5047\u8bbe\u6211\u4eec\u6709\u4e24\u4e2a\u57fa\u56e0\u7ec4\u533a\u95f4\u6570\u636e\u96c6\uff0c\u5206\u522b\u4ee3\u8868\u4e24\u4e2a\u4e0d\u540c\u5b9e\u9a8c\u6761\u4ef6\u4e0b\u7684\u57fa\u56e0\u7ed3\u5408\u4f4d\u70b9\u3002\u6211\u4eec\u5c06\u5b83\u4eec\u8868\u793a\u4e3a\u4e24\u4e2aGRanges\u5bf9\u8c61\uff1aset1\u548cset2\u3002\u73b0\u5728\u6211\u4eec\u60f3\u4e86\u89e3\u8fd9\u4e24\u4e2a\u6570\u636e\u96c6\u4e4b\u95f4\u7684\u91cd\u53e0\u548c\u5dee\u5f02\uff0c\u4ee5\u53ca\u5728\u4e24\u4e2a\u6570\u636e\u96c6\u4e2d\u90fd\u5b58\u5728\u7684\u5171\u540c\u57fa\u56e0\u7ed3\u5408\u4f4d\u70b9\u3002<\/p>\n

#\u52a0\u8f7d\u8bed\u8a00\u5305<\/p>\n

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

# \u521b\u5efa\u793a\u4f8b\u6570\u636e\u96c6<\/p>\n

> set1 <- GRanges(<\/p>\n

+ seqnames = c(“chr1”, “chr1”, “chr2”),<\/p>\n

+ ranges = IRanges(start = c(100, 200, 1500), end = c(300, 400, 1800))<\/p>\n

+ )<\/p>\n

> set2 <- GRanges(<\/p>\n

+ seqnames = c(“chr1”, “chr2”),<\/p>\n

+ ranges = IRanges(start = c(250, 1600), end = c(350, 1700))<\/p>\n

+ )<\/p>\n

# \u8fdb\u884c\u96c6\u5408\u8fd0\u7b97<\/p>\n

> union_set <- union(set1, set2) # \u5e76\u96c6<\/p>\n

> intersect_set <- intersect(set1, set2) # \u4ea4\u96c6<\/p>\n

> setdiff_set1 <- setdiff(set1, set2) # \u5dee\u96c6\uff1a\u5728set1\u4e2d\u800c\u4e0d\u5728set2\u4e2d\u7684\u533a\u95f4<\/p>\n

> setdiff_set2 <- setdiff(set2, set1) # \u5dee\u96c6\uff1a\u5728set2\u4e2d\u800c\u4e0d\u5728set1\u4e2d\u7684\u533a\u95f4<\/p>\n

# \u67e5\u770b\u7ed3\u679c<\/p>\n

> print(“\u5e76\u96c6\uff1a”)<\/p>\n

[1] “\u5e76\u96c6\uff1a”<\/p>\n

> print(union_set)<\/p>\n

GRanges object with 2 ranges and 0 metadata columns:<\/p>\n

seqnames ranges strand<\/p>\n

<Rle> <IRanges> <Rle><\/p>\n

[1] chr1 100-400 *<\/p>\n

[2] chr2 1500-1800 *<\/p>\n

——-<\/p>\n

seqinfo: 2 sequences from an unspecified genome; no seqlengths<\/p>\n

> print(“\u4ea4\u96c6\uff1a”)<\/p>\n

[1] “\u4ea4\u96c6\uff1a”<\/p>\n

> print(intersect_set)<\/p>\n

GRanges object with 2 ranges and 0 metadata columns:<\/p>\n

seqnames ranges strand<\/p>\n

<Rle> <IRanges> <Rle><\/p>\n

[1] chr1 250-350 *<\/p>\n

[2] chr2 1600-1700 *<\/p>\n

——-<\/p>\n

seqinfo: 2 sequences from an unspecified genome; no seqlengths<\/p>\n

> print(“\u5728set1\u4e2d\u800c\u4e0d\u5728set2\u4e2d\u7684\u533a\u95f4\uff1a”)<\/p>\n

[1] “\u5728set1\u4e2d\u800c\u4e0d\u5728set2\u4e2d\u7684\u533a\u95f4\uff1a”<\/p>\n

> print(setdiff_set1)<\/p>\n

GRanges object with 4 ranges and 0 metadata columns:<\/p>\n

seqnames ranges strand<\/p>\n

<Rle> <IRanges> <Rle><\/p>\n

[1] chr1 100-249 *<\/p>\n

[2] chr1 351-400 *<\/p>\n

[3] chr2 1500-1599 *<\/p>\n

[4] chr2 1701-1800 *<\/p>\n

——-<\/p>\n

seqinfo: 2 sequences from an unspecified genome; no seqlengths<\/p>\n

> print(“\u5728set2\u4e2d\u800c\u4e0d\u5728set1\u4e2d\u7684\u533a\u95f4\uff1a”)<\/p>\n

[1] “\u5728set2\u4e2d\u800c\u4e0d\u5728set1\u4e2d\u7684\u533a\u95f4\uff1a”<\/p>\n

> print(setdiff_set2)<\/p>\n

GRanges object with 0 ranges and 0 metadata columns:<\/p>\n

seqnames ranges strand<\/p>\n

<Rle> <IRanges> <Rle><\/p>\n

——-<\/p>\n

seqinfo: 2 sequences from an unspecified genome; no seqlengths<\/p>\n

\u4ee5\u4e0a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e24\u4e2a\u793a\u4f8b\u7684GRanges\u5bf9\u8c61 set1\u548c set2\uff0c\u5206\u522b\u4ee3\u8868\u4e24\u4e2a\u4e0d\u540c\u5b9e\u9a8c\u6761\u4ef6\u4e0b\u7684\u57fa\u56e0\u7ed3\u5408\u4f4d\u70b9\u3002\u7136\u540e\uff0c\u6211\u4eec\u4f7f\u7528union()\u51fd\u6570\u8ba1\u7b97\u4e86\u8fd9\u4e24\u4e2a\u6570\u636e\u96c6\u7684\u5e76\u96c6\uff0c\u4f7f\u7528intersect()\u51fd\u6570\u8ba1\u7b97\u4e86\u4ea4\u96c6\uff0c\u4ee5\u53ca\u4f7f\u7528setdiff()\u51fd\u6570\u5206\u522b\u8ba1\u7b97\u4e86\u5728set1\u4e2d\u800c\u4e0d\u5728set2\u4e2d\u4ee5\u53ca\u5728set2\u4e2d\u800c\u4e0d\u5728set1\u4e2d\u7684\u57fa\u56e0\u7ed3\u5408\u4f4d\u70b9\u3002<\/p>\n

\u8fd9\u6837\uff0c\u6211\u4eec\u5c31\u80fd\u591f\u5206\u6790\u548c\u7406\u89e3\u8fd9\u4e24\u4e2a\u6570\u636e\u96c6\u4e4b\u95f4\u7684\u91cd\u53e0\u60c5\u51b5\u548c\u5dee\u5f02\uff0c\u4ece\u800c\u66f4\u597d\u5730\u4e86\u89e3\u57fa\u56e0\u7ed3\u5408\u4f4d\u70b9\u5728\u4e0d\u540c\u5b9e\u9a8c\u6761\u4ef6\u4e0b\u7684\u8c03\u63a7\u60c5\u51b5\u3002\u8fd9\u5bf9\u4e8e\u7406\u89e3\u57fa\u56e0\u8c03\u63a7\u3001\u7814\u7a76\u8f6c\u5f55\u56e0\u5b50\u7ed3\u5408\u7b49\u751f\u7269\u5b66\u95ee\u9898\u975e\u5e38\u6709\u5e2e\u52a9\u3002<\/p>\n

\u9664\u4e86\u57fa\u672c\u7684\u6570\u636e\u64cd\u4f5c\u5916\uff0cGenomicRanges\u5305\u8fd8\u5177\u6709\u5f3a\u5927\u7684\u53ef\u89c6\u5316\u529f\u80fd\u3002\u7528\u6237\u53ef\u4ee5\u4f7f\u7528`plot()`\u51fd\u6570\u76f4\u63a5\u7ed8\u5236`GRanges`\u5bf9\u8c61\uff0c\u5c06\u57fa\u56e0\u7ec4\u533a\u95f4\u5728\u67d3\u8272\u4f53\u4e0a\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u6216\u8005\u901a\u8fc7\u5176\u4ed6\u56fe\u8868\u5c55\u793a\u57fa\u56e0\u7ec4\u6570\u636e\u7684\u7279\u5f81\u3002\u8fd9\u4f7f\u5f97\u7ed3\u679c\u7684\u89e3\u91ca\u66f4\u76f4\u89c2\uff0c\u6709\u52a9\u4e8e\u7814\u7a76\u4eba\u5458\u5feb\u901f\u89c2\u5bdf\u6570\u636e\u4e2d\u7684\u8d8b\u52bf\u548c\u5f02\u5e38\u60c5\u51b5\u3002<\/p>\n

\u793a\u4f8b\uff1a<\/p>\n

\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2aGRanges\u5bf9\u8c61\uff0c\u4ee3\u8868\u4e00\u7ec4\u57fa\u56e0\u7684\u8d77\u59cb\u548c\u7ec8\u6b62\u4f4d\u7f6e\u4fe1\u606f\uff0c\u4ee5\u53ca\u5b83\u4eec\u6240\u5728\u7684\u67d3\u8272\u4f53\u540d\u79f0\u3002\u6211\u4eec\u5c06\u4f7f\u7528plot()\u51fd\u6570\u5c06\u8fd9\u4e9b\u57fa\u56e0\u5728\u67d3\u8272\u4f53\u4e0a\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n

# \u5bfc\u5165GenomicRanges\u5305<\/p>\n

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

# \u5bfc\u5165ggbio\u5305<\/p>\n

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

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

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

# \u521b\u5efa\u793a\u4f8b\u6570\u636e\u96c6<\/p>\n

> genes <- GRanges(<\/p>\n

+ seqnames = c(“chr1”, “chr1”, “chr2”, “chr2”),<\/p>\n

+ ranges = IRanges(start = c(100, 200, 1500, 1700), end = c(300, 400, 1800, 2000))<\/p>\n

+ )<\/p>\n

> # \u5c06GRanges\u5bf9\u8c61\u8f6c\u6362\u4e3adata.frame<\/p>\n

> genes_df <- as.data.frame(genes)<\/p>\n

# \u7ed8\u5236\u57fa\u56e0\u7ec4\u533a\u95f4\u6570\u636e<\/p>\n

> p <- ggplot() +<\/p>\n

+ geom_rect(data = genes_df, aes(xmin = start, xmax = end, ymin = 0, ymax = 1), fill = “blue”) +<\/p>\n

+ facet_wrap(~ seqnames, scales = “free_x”, ncol = 1) +<\/p>\n

+ labs(x = “\u4f4d\u7f6e”, y = “\u67d3\u8272\u4f53”) +<\/p>\n

+ theme_bw()<\/p>\n

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

\"1eaabebb-2af0-4477-b3a5-1234ef5acc78\"<\/p>\n

\u7ed8\u56fe\u7ed3\u679c\u4f1a\u663e\u793a\u4e24\u6761\u67d3\u8272\u4f53\uff0c\u57fa\u56e0\u7528\u84dd\u8272\u77e9\u5f62\u8868\u793a\u3002\u6bcf\u4e2a\u77e9\u5f62\u7684\u6a2a\u8f74\u8868\u793a\u57fa\u56e0\u7684\u8d77\u59cb\u548c\u7ec8\u6b62\u4f4d\u7f6e\uff0c\u7eb5\u8f74\u8868\u793a\u67d3\u8272\u4f53\u3002\u7531\u4e8e\u6211\u4eec\u4f7f\u7528\u4e86facet_wrap()\u51fd\u6570\uff0c\u5c06\u56fe\u5f62\u6309\u7167\u67d3\u8272\u4f53\u8fdb\u884c\u4e86\u5206\u7ec4\uff0c\u6240\u4ee5\u6bcf\u4e2a\u67d3\u8272\u4f53\u7684\u57fa\u56e0\u7ec4\u533a\u95f4\u90fd\u5728\u4e00\u4e2a\u5355\u72ec\u7684\u5b50\u56fe\u4e2d\u5c55\u793a\u3002<\/p>\n

\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0cGenomicRanges\u5305\u5728\u8bb8\u591a\u751f\u7269\u4fe1\u606f\u5b66\u4efb\u52a1\u4e2d\u53d1\u6325\u7740\u91cd\u8981\u4f5c\u7528\u3002\u4f8b\u5982\uff0c\u5b83\u5e38\u7528\u4e8e\u5904\u7406\u8f6c\u5f55\u7ec4\u6d4b\u5e8f\uff08RNA-seq\uff09\u6570\u636e\uff0c\u901a\u8fc7\u5c06\u57fa\u56e0\u7ec4\u533a\u95f4\u4e0e\u57fa\u56e0\u8868\u8fbe\u91cf\u5173\u8054\uff0c\u8fdb\u884c\u5dee\u5f02\u8868\u8fbe\u5206\u6790\u548c\u529f\u80fd\u6ce8\u91ca\u3002\u6b64\u5916\uff0c\u57fa\u56e0\u7ec4\u533a\u95f4\u6570\u636e\u4e5f\u7ecf\u5e38\u7528\u4e8e\u5bfb\u627e\u86cb\u767d\u8d28\u7ed3\u5408\u4f4d\u70b9\u3001\u68c0\u6d4b\u67d3\u8272\u4f53\u53d8\u5f02\u3001\u7814\u7a76\u8868\u89c2\u9057\u4f20\u5b66\u4fee\u9970\u7b49\u7814\u7a76\u4e2d\u3002<\/p>\n

\"KHDif\"<\/p>\n

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References:<\/p>\n

    \n
  1. https:\/\/www.google.com\/url?sa=i&url=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F42400257%2Fgenomicranges-add-coverage&psig=AOvVaw2FfQcFlH2_kq7jcOeM2ruw&ust=1690346754304000&source=images&cd=vfe&opi=89978449&ved=0CBAQjRxqFwoTCNCu97-GqYADFQAAAAAdAAAAABAQ<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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