{"width":"100%","published":"2015-04-21 18:34:05","version":"1.0","blog_url":"https://ryamada22.hatenablog.jp/","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fryamada22.hatenablog.jp%2Fentry%2F20150421%2F1429608845\" title=\"SeqGSEA - ryamada\u306e\u907a\u4f1d\u5b66\u30fb\u907a\u4f1d\u7d71\u8a08\u5b66\u30e1\u30e2\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","author_name":"ryamada22","image_url":null,"height":"190","type":"rich","blog_title":"ryamada\u306e\u907a\u4f1d\u5b66\u30fb\u907a\u4f1d\u7d71\u8a08\u5b66\u30e1\u30e2","url":"https://ryamada22.hatenablog.jp/entry/20150421/1429608845","title":"SeqGSEA","categories":["RNAseq","SeqGSEA","Bioconductor","R"],"author_url":"https://blog.hatena.ne.jp/ryamada22/","description":"\u30d1\u30c3\u30b1\u30fc\u30b8\u306b\u95a2\u3059\u308b\u8a18\u4e8b\u30b5\u30a4\u30c8 \u305d\u3053\u304b\u3089\u306e\u5f15\u7528 The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological var\u2026","provider_url":"https://hatena.blog","provider_name":"Hatena Blog"}