最早在China Unix上写博客，后来转战新浪。在azelea的说服下，果断换成wordpress，写技术博客贴点代码什么的，新浪必须是太渣。azelea是我开始独立博客的领路人，为我提供了空间和域名。再后来自己买了空间和ygc.name的域名，用了好几年。本来一直有想法要换成github+jekll，恰巧Wei Shen强力推荐HUGO。于是再一次换博客。

首先面临着wordpress迁移的问题。

导入wordpress posts为markdown文件

试用了多个插件或脚本，都是各种出错。最后发现了神器potter-wordpress，通过npm安装：

npm install -g pottercms-wordpress

然后一个语句:

potter-wordpress -h WORDPRESS_SITE_URL -u USER_NAME -p PASSWORD --dir PATH_TO_STORE_MD_FILES

自动从线上把posts抓下来，并转化为markdown。在之前各种搞不定之后，看到下面的输出有种莫名的快感。

Fetching articles…

   Got : [Sun Nov 29 2015] R kernel in Jupyter notebook 3
   Got : [Mon Nov 09 2015] phylomoji with ggtree
   Got : [Tue Nov 17 2015] 合体中的操作系统
   Got : [Mon Nov 02 2015] Comparison of clusterProfiler and GSEA-P
   Got : [Thu Oct 01 2015] ChIPseq data mining with ChIPseeker
   Got : [Tue Oct 06 2015] TRIE
   Got : [Thu Oct 15 2015] [BioC 3.2] NEWS of my BioC packages
   Got : [Wed Oct 21 2015] use simplify to remove redundancy of enriched GO terms
   
   ...
   
   Got : [Mon May 14 2007] Edit partition table in NetBSD/macppc
   Got : [Mon May 14 2007] 我的emacs配置文件
   Got : [Mon May 14 2007] pkgsrc tips
   Got : [Thu May 03 2007] scilab三维平面绘图
   Got : [Sat Jun 24 2006] qvwm窗口管理器的windows2000主题
   Got : [Wed Jun 14 2006] 安装os9和netbsd双系统
   Got : [Sat May 28 2005] 在emacs里使用maxima
   Got : [Mon Mar 14 2005] scau校园网认证
   Got : [Fri Jan 14 2005] Powerbook G3上安装gentoo

Done.

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I followed the post, Installing an R kernel for IPython/jupyter notebook 3 on OSX, to install jupyter with python3 and R kernels in my iMac.

I have elementaryOS on my Macbook Pro and also want to have jupyter on it. The installation process is quite similar.

Install Jupyter

sudo apt-get install python3-pip
sudo pip3 install jupyter

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更新EI Capitan已经近一个月，第一感觉就是明显变慢了。这是去年买的27' iMac，必须不慢。 MS office程序可以打开，但打开文档直接闪退。升级安装总会有些问题，就像我去年在笔记本上升级yosemite一样，最后两分钟始终过不去，迫使我全新安装，当时我看后台记录就发现了是MacTex拷不过去， 后来MacTex的网站上也写了升级Yosemite有这个问题。升级有些小问题是可以预见的，但很少有像EI Capitan这么多的问题。

Macworld EI Capitan review文章中有人这样评论：

haha nice review ! did you tried the update ?? slow connection safari freezes on launch outlook crash printer does not work anymore great job from Apple . My confidence in Apple has been seriously dented!

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Thanks @mevers for raising the issue to me and his efforts in benchmarking clusterProfiler.

He pointed out two issues:

• outputs from gseGO and GSEA-P are poorly overlap.
• pvalues from gseGO are generally smaller and don’t show a lot of variation

For GSEA analysis, we have two inputs, a ranked gene list and gene set collections.

First of all, the gene set collections are very different. The GMT file used in his test is c5.cc.v5.0.symbols.gmt, which is a tiny subset of GO CC, while clusterProfiler used the whole GO CC corpus.

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To simplify enriched GO result, we can use slim version of GO and use enricher function to analyze.

Another strategy is to use GOSemSim to calculate similarity of GO terms and remove those highly similar terms by keeping one representative term. To make this feature available to clusterProfiler users, I develop a simplify method to reduce redundant GO terms from output of enrichGO function.

require(clusterProfiler)
data(geneList, package="DOSE")
de <- names(geneList)[abs(geneList) > 2]
bp <- enrichGO(de, ont="BP")
enrichMap(bp)

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In BioC 3.2 release, all my packages including GOSemSim, clusterProfiler, DOSE, ReactomePA, and ChIPseeker switch from Sweave to R Markdown for package vignettes.

GOSemSim

To make it consistent between GOSemSim and clusterProfiler, ‘worm’ was deprecated and instead we should use ‘celegans’. As usual, information content data was updated.

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ChIP-seq is rapidly becoming a common technique and there are a large number of dataset available in the public domain. Results from individual experiments provide a limited understanding of chromatin interactions, as there is many factors cooperate to regulate transcription. Unlike other tools that designed for single dataset, ChIPseeker is designed for comparing profiles of ChIP-seq datasets at different levels.

We provide functions to compare profiles of peaks binding to TSS regions, annotation, and enriched functional profiles. More importantly, ChIPseeker incorporates statistical testing of co-occurrence of different ChIP-seq datasets and can be used to identify co-factors.

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