├── .DS_Store ├── README.md ├── docs ├── .DS_Store ├── CONFIG │ ├── README.md │ ├── config_1.md │ ├── config_2.md │ └── config_3.md ├── MOFA │ ├── README.md │ ├── mofa_1.md │ ├── mofa_2.md │ ├── mofa_3.assets │ │ ├── do3.png │ │ ├── do4-20221019030752121.png │ │ ├── do4-6120066.png │ │ ├── do4.png │ │ ├── do5-6120069.png │ │ ├── do5.png │ │ └── do6.png │ ├── mofa_3.md │ ├── mofa_4.assets │ │ ├── do10.png │ │ ├── do11-6120091.png │ │ ├── do11.png │ │ ├── do12-6120093.png │ │ ├── do12.png │ │ ├── do13.png │ │ ├── do14.png │ │ ├── do15.png │ │ ├── do16-1636004475065.png │ │ ├── do16.png │ │ ├── do7.png │ │ ├── do8.png │ │ ├── do9-6120084.png │ │ └── do9.png │ ├── mofa_4.md │ ├── mofa_5.assets │ │ └── image-20211104134822671.png │ ├── mofa_5.md │ ├── mofa_6.md │ ├── mofa_7.assets │ │ ├── 下载 (3).png │ │ ├── 下载 (4).png │ │ ├── 下载 (5).png │ │ ├── 下载 (6).png │ │ ├── 下载 (7).png │ │ └── 下载 (8).png │ └── mofa_7.md ├── MultiOMIC-ipynb │ ├── cellanno-1.ipynb │ ├── cellanno-2.ipynb │ ├── figure1.ipynb │ ├── figure2.ipynb │ ├── glue-0.ipynb │ ├── glue-1.ipynb │ └── glue-2.ipynb ├── MultiOMIC │ ├── .DS_Store │ ├── README.md │ ├── book-1.md │ ├── book-2.md │ ├── book-3.assets │ │ ├── image-20221019004245874-6119913.png │ │ ├── image-20221019004245874-6119960.png │ │ ├── image-20221019004245874.png │ │ ├── image-20221019004442469-6119965.png │ │ ├── image-20221019004442469.png │ │ ├── image-20221019004556158-6119968.png │ │ ├── image-20221019004556158.png │ │ ├── image-20221019004807328-6119971.png │ │ ├── image-20221019004807328.png │ │ ├── image-20221019004930306-6119974.png │ │ ├── image-20221019004930306.png │ │ ├── image-20221019005115998-6119977.png │ │ ├── image-20221019005115998.png │ │ ├── image-20221019005254018-6119981.png │ │ ├── image-20221019005254018.png │ │ ├── image-20221019005328419-6119984.png │ │ ├── image-20221019005328419.png │ │ ├── image-20221019005554337-6119988.png │ │ ├── image-20221019005554337.png │ │ ├── image-20221019005740026-6119990.png │ │ └── image-20221019005740026.png │ ├── book-3.md │ ├── book-4.assets │ │ ├── image-20221019012328268-6120000.png │ │ ├── image-20221019012328268.png │ │ ├── image-20221019012404062-6120003.png │ │ ├── image-20221019012404062.png │ │ ├── image-20221019012503861-6120007.png │ │ └── image-20221019012503861.png │ ├── book-4.md │ ├── book-5.assets │ │ ├── image-20221019014049480-6120013.png │ │ ├── image-20221019014049480.png │ │ ├── image-20221019014313452-6120017.png │ │ ├── image-20221019014313452.png │ │ ├── image-20221019014538577-6120020.png │ │ ├── image-20221019014538577.png │ │ ├── image-20221019014718599-6120022.png │ │ ├── image-20221019014718599.png │ │ ├── image-20221019014912052-6120025.png │ │ └── image-20221019014912052.png │ ├── book-5.md │ ├── book-6.assets │ │ ├── image-20221019020116601-6120031.png │ │ ├── image-20221019020116601.png │ │ ├── image-20221019020147975-6120034.png │ │ ├── image-20221019020147975.png │ │ ├── image-20221019020412203-6120038.png │ │ └── image-20221019020412203.png │ ├── book-6.md │ ├── book-7.assets │ │ ├── image-20221019021043508-6120043.png │ │ ├── image-20221019021043508.png │ │ ├── image-20221019021239322-6120046.png │ │ ├── image-20221019021239322.png │ │ ├── image-20221019021513334-6120051.png │ │ └── image-20221019021513334.png │ └── book-7.md ├── PLOT │ ├── .DS_Store │ ├── README.md │ ├── data │ │ ├── .DS_Store │ │ ├── data_exp.csv │ │ ├── data_heatmap.csv │ │ ├── data_kegg.txt │ │ └── data_vol.csv │ ├── plot_1.assets │ │ ├── output_11_1.png │ │ ├── output_14_1.png │ │ ├── output_16_1.png │ │ ├── output_18_0.png │ │ ├── output_1_1.png │ │ ├── output_20_0.png │ │ ├── output_5_0.png │ │ ├── output_7_1.png │ │ └── output_9_1.png │ ├── plot_1.md │ ├── plot_2.assets │ │ ├── output_11_0.png │ │ ├── output_13_0.png │ │ ├── output_15_0.png │ │ ├── output_17_0.png │ │ ├── output_19_1.png │ │ ├── output_1_1.png │ │ ├── output_21_1.png │ │ └── output_23_0.png │ ├── plot_2.md │ ├── plot_3.assets │ │ ├── output_11_0.png │ │ ├── output_13_1.png │ │ ├── output_15_2.png │ │ ├── output_1_1.png │ │ └── output_9_0.png │ └── plot_3.md ├── README.md ├── RNASEQ │ ├── README.md │ ├── rnaseq_1.md │ ├── rnaseq_2.assets │ │ ├── NHDF_heatmap.png │ │ ├── NHDF_volcano-1635819681870.png │ │ ├── NHDF_volcano.png │ │ ├── untitled.png │ │ └── untitled1.png │ ├── rnaseq_2.md │ ├── rnaseq_3.assets │ │ ├── NOD-like receptor signaling pathway.prerank.png │ │ └── untitled1.png │ └── rnaseq_3.md ├── RNASEQUP │ ├── README.md │ ├── rnasequp_1.md │ └── rnasequp_2.md ├── SCGLUE │ ├── README.md │ ├── scglue_1.md │ ├── scglue_2.md │ ├── scglue_3.assets │ │ ├── do.png │ │ ├── do10.png │ │ ├── do11.png │ │ ├── do12.png │ │ ├── do13.png │ │ ├── do14.png │ │ ├── do15.png │ │ ├── do16.png │ │ ├── do2.png │ │ ├── do3.png │ │ ├── do4.png │ │ ├── do5.png │ │ ├── do6.png │ │ ├── do7.png │ │ ├── do8.png │ │ └── do9.png │ ├── scglue_3.md │ ├── scglue_4.md │ └── scglue_5.md ├── SUMMARY.md ├── TMT │ ├── .DS_Store │ ├── README.md │ ├── tmt_1.assets │ │ ├── image-20220126012940259-6120196.png │ │ └── image-20220126012940259.png │ ├── tmt_1.md │ ├── tmt_2.assets │ │ ├── image-20220126013900416-6120205.png │ │ ├── image-20220126013900416.png │ │ ├── image-20220126014204530.png │ │ ├── image-20220126014423239.png │ │ ├── image-20220126014658979.png │ │ ├── image-20220126014844199.png │ │ ├── image-20220126014944222.png │ │ ├── image-20220126015312481.png │ │ ├── image-20220126015354523.png │ │ └── image-20220126015849066.png │ ├── tmt_2.md │ ├── tmt_3.assets │ │ ├── image-20220126021021714.png │ │ ├── image-20220126022018085.png │ │ ├── image-20220126022234665.png │ │ ├── image-20220126022304695.png │ │ └── image-20220126022354890.png │ └── tmt_3.md ├── index.md └── overrides │ └── main.html ├── mkdocs.yml └── site ├── .DS_Store ├── 404.html ├── CONFIG ├── config_1 │ └── index.html ├── config_2 │ └── index.html ├── config_3 │ └── index.html └── index.html ├── MOFA ├── index.html ├── mofa_1 │ └── index.html ├── mofa_2 │ └── index.html ├── mofa_3.assets │ ├── do3.png │ ├── do4-20221019030752121.png │ ├── do4-6120066.png │ ├── do4.png │ ├── do5-6120069.png │ ├── do5.png │ └── do6.png ├── mofa_3 │ └── index.html ├── mofa_4.assets │ ├── do10.png │ ├── do11-6120091.png │ ├── do11.png │ ├── do12-6120093.png │ ├── do12.png │ ├── do13.png │ ├── do14.png │ ├── do15.png │ ├── do16-1636004475065.png │ ├── do16.png │ ├── do7.png │ ├── do8.png │ ├── do9-6120084.png │ └── do9.png ├── mofa_4 │ └── index.html ├── mofa_5.assets │ └── image-20211104134822671.png ├── mofa_5 │ └── index.html ├── mofa_6 │ └── index.html ├── mofa_7.assets │ ├── 下载 (3).png │ ├── 下载 (4).png │ ├── 下载 (5).png │ ├── 下载 (6).png │ ├── 下载 (7).png │ └── 下载 (8).png └── mofa_7 │ └── index.html ├── MultiOMIC-ipynb ├── cellanno-1 │ └── index.html ├── cellanno-2 │ └── index.html ├── figure1 │ └── index.html ├── figure2 │ └── index.html ├── glue-0 │ └── index.html ├── glue-1 │ └── index.html └── glue-2 │ └── index.html ├── MultiOMIC ├── book-1 │ └── index.html ├── book-2 │ └── index.html ├── book-3.assets │ ├── image-20221019004245874-6119913.png │ ├── image-20221019004245874-6119960.png │ ├── image-20221019004245874.png │ ├── image-20221019004442469-6119965.png │ ├── image-20221019004442469.png │ ├── image-20221019004556158-6119968.png │ ├── image-20221019004556158.png │ ├── image-20221019004807328-6119971.png │ ├── image-20221019004807328.png │ ├── image-20221019004930306-6119974.png │ ├── image-20221019004930306.png │ ├── image-20221019005115998-6119977.png │ ├── image-20221019005115998.png │ ├── image-20221019005254018-6119981.png │ ├── image-20221019005254018.png │ ├── image-20221019005328419-6119984.png │ ├── image-20221019005328419.png │ ├── image-20221019005554337-6119988.png │ ├── image-20221019005554337.png │ ├── image-20221019005740026-6119990.png │ └── image-20221019005740026.png ├── book-3 │ └── index.html ├── book-4.assets │ ├── image-20221019012328268-6120000.png │ ├── image-20221019012328268.png │ ├── image-20221019012404062-6120003.png │ ├── image-20221019012404062.png │ ├── image-20221019012503861-6120007.png │ └── image-20221019012503861.png ├── book-4 │ └── index.html ├── book-5.assets │ ├── image-20221019014049480-6120013.png │ ├── image-20221019014049480.png │ ├── image-20221019014313452-6120017.png │ ├── image-20221019014313452.png │ ├── image-20221019014538577-6120020.png │ ├── image-20221019014538577.png │ ├── image-20221019014718599-6120022.png │ ├── image-20221019014718599.png │ ├── image-20221019014912052-6120025.png │ └── image-20221019014912052.png ├── book-5 │ └── index.html ├── book-6.assets │ ├── image-20221019020116601-6120031.png │ ├── image-20221019020116601.png │ ├── image-20221019020147975-6120034.png │ ├── image-20221019020147975.png │ ├── image-20221019020412203-6120038.png │ └── image-20221019020412203.png ├── book-6 │ └── index.html ├── book-7.assets │ ├── image-20221019021043508-6120043.png │ ├── image-20221019021043508.png │ ├── image-20221019021239322-6120046.png │ ├── image-20221019021239322.png │ ├── image-20221019021513334-6120051.png │ └── image-20221019021513334.png ├── book-7 │ └── index.html └── index.html ├── PLOT ├── data │ ├── data_exp.csv │ ├── data_heatmap.csv │ ├── data_kegg.txt │ └── data_vol.csv ├── index.html ├── plot_1.assets │ ├── output_11_1.png │ ├── output_14_1.png │ ├── output_16_1.png │ ├── output_18_0.png │ ├── output_1_1.png │ ├── output_20_0.png │ ├── output_5_0.png │ ├── output_7_1.png │ └── output_9_1.png ├── plot_1 │ └── index.html ├── plot_2.assets │ ├── output_11_0.png │ ├── output_13_0.png │ ├── output_15_0.png │ ├── output_17_0.png │ ├── output_19_1.png │ ├── output_1_1.png │ ├── output_21_1.png │ └── output_23_0.png ├── plot_2 │ └── index.html ├── plot_3.assets │ ├── output_11_0.png │ ├── output_13_1.png │ ├── output_15_2.png │ ├── output_1_1.png │ └── output_9_0.png └── plot_3 │ └── index.html ├── RNASEQ ├── index.html ├── rnaseq_1 │ └── index.html ├── rnaseq_2.assets │ ├── NHDF_heatmap.png │ ├── NHDF_volcano-1635819681870.png │ ├── NHDF_volcano.png │ ├── untitled.png │ └── untitled1.png ├── rnaseq_2 │ └── index.html ├── rnaseq_3.assets │ ├── NOD-like receptor signaling pathway.prerank.png │ └── untitled1.png └── rnaseq_3 │ └── index.html ├── RNASEQUP ├── index.html ├── rnasequp_1 │ └── index.html └── rnasequp_2 │ └── index.html ├── SCGLUE ├── index.html ├── scglue_1 │ └── index.html ├── scglue_2 │ └── index.html ├── scglue_3.assets │ ├── do.png │ ├── do10.png │ ├── do11.png │ ├── do12.png │ ├── do13.png │ ├── do14.png │ ├── do15.png │ ├── do16.png │ ├── do2.png │ ├── do3.png │ ├── do4.png │ ├── do5.png │ ├── do6.png │ ├── do7.png │ ├── do8.png │ └── do9.png ├── scglue_3 │ └── index.html ├── scglue_4 │ └── index.html └── scglue_5 │ └── index.html ├── SUMMARY └── index.html ├── TMT ├── index.html ├── tmt_1.assets │ ├── image-20220126012940259-6120196.png │ └── image-20220126012940259.png ├── tmt_1 │ └── index.html ├── tmt_2.assets │ ├── image-20220126013900416-6120205.png │ ├── image-20220126013900416.png │ ├── image-20220126014204530.png │ ├── image-20220126014423239.png │ ├── image-20220126014658979.png │ ├── image-20220126014844199.png │ ├── image-20220126014944222.png │ ├── image-20220126015312481.png │ ├── image-20220126015354523.png │ └── image-20220126015849066.png ├── tmt_2 │ └── index.html ├── tmt_3.assets │ ├── image-20220126021021714.png │ ├── image-20220126022018085.png │ ├── image-20220126022234665.png │ ├── image-20220126022304695.png │ └── image-20220126022354890.png └── tmt_3 │ └── index.html ├── assets ├── images │ └── favicon.png ├── javascripts │ ├── bundle.fc8c2696.min.js │ ├── bundle.fc8c2696.min.js.map │ ├── lunr │ │ ├── min │ │ │ ├── lunr.ar.min.js │ │ │ ├── lunr.da.min.js │ │ │ ├── lunr.de.min.js │ │ │ ├── lunr.du.min.js │ │ │ ├── lunr.es.min.js │ │ │ ├── lunr.fi.min.js │ │ │ ├── lunr.fr.min.js │ │ │ ├── lunr.hi.min.js │ │ │ ├── lunr.hu.min.js │ │ │ ├── lunr.it.min.js │ │ │ ├── lunr.ja.min.js │ │ │ ├── lunr.jp.min.js │ │ │ ├── lunr.ko.min.js │ │ │ ├── lunr.multi.min.js │ │ │ ├── lunr.nl.min.js │ │ │ ├── lunr.no.min.js │ │ │ ├── lunr.pt.min.js │ │ │ ├── lunr.ro.min.js │ │ │ ├── lunr.ru.min.js │ │ │ ├── lunr.stemmer.support.min.js │ │ │ ├── lunr.sv.min.js │ │ │ ├── lunr.ta.min.js │ │ │ ├── lunr.th.min.js │ │ │ ├── lunr.tr.min.js │ │ │ ├── lunr.vi.min.js │ │ │ └── lunr.zh.min.js │ │ ├── tinyseg.js │ │ └── wordcut.js │ └── workers │ │ ├── search.208ed371.min.js │ │ └── search.208ed371.min.js.map └── stylesheets │ ├── main.7bf56d0a.min.css │ ├── main.7bf56d0a.min.css.map │ ├── palette.a0c5b2b5.min.css │ └── palette.a0c5b2b5.min.css.map ├── index.html ├── overrides └── main.html ├── sitemap.xml └── sitemap.xml.gz /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/.DS_Store -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Bioinformatics_tutorial 2 | 3 | - 作者：starlitnightly 4 | - 日期：2021.11.02 5 | 6 | ## 楔子 7 | 8 | 撰写这个笔记，目的在于提供一个生物信息学相关的教程吧 9 | 10 | ——2021.11.02，星夜 11 | 12 | ## 目录 13 | 14 | - 【配置】：https://starlitnightly.github.io/bioinformatic_tutorial/CONFIG/ 15 | - 【分析1: RNA-seq上游】：https://starlitnightly.github.io/bioinformatic_tutorial/RNASEQUP/ 16 | - 【分析2: RNA-seq下游】：https://starlitnightly.github.io/bioinformatic_tutorial/RNASEQ/ 17 | - 【分析3: 单细胞样本对齐】：https://starlitnightly.github.io/bioinformatic_tutorial/SCGLUE/ 18 | - 【分析4: MOFA单细胞多组学因子分析】：https://starlitnightly.github.io/bioinformatic_tutorial/MOFA/ 19 | - 【绘图：Python数据可视化-生物信息学专栏】：https://starlitnightly.github.io/bioinformatic_tutorial/PLOT/ 20 | 21 | ## 数据 22 | 23 | 【绘图：Python数据可视化-生物信息学专栏】：https://github.com/Starlitnightly/bioinformatic_tutorial/tree/main/PLOT/data 24 | 25 | ## License 26 | 27 | 知识共享许可协议 28 | 29 | 本作品采用知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议进行许可。 -------------------------------------------------------------------------------- /docs/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/.DS_Store -------------------------------------------------------------------------------- /docs/CONFIG/README.md: -------------------------------------------------------------------------------- 1 | # 配置：基础环境配置 2 | 3 | -------------------------------------------------------------------------------- /docs/CONFIG/config_1.md: -------------------------------------------------------------------------------- 1 | # 配置1：Python环境 2 | 3 | 对于生物信息学相关的分析，这里分为Python，R语言与linux三部分 4 | 5 | ## 1. 前言 6 | 7 | 在python部分，我们主要使用到numpy，pandas，seaborn，matplotlib，scipy，statsmodel这几个包，值得庆幸的是，只需要通过Anaconda的安装，这几个包就可以一并被安装上了，避免各种各样报错的烦恼 8 | 9 | ## 2. Anaconda下载 10 | 11 | 如果你没有科学上网的话，建议从清华大学镜像网下载Anaconda3的安装包进行安装，省事儿 12 | 13 | 下载地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-5.3.1-Windows-x86_64.exe 14 | 15 | ## 3. Anaconda安装（转自知乎BG大龍） 16 | 17 | ### 3.1 安装过程 18 | 19 | ![img](https://pic1.zhimg.com/80/v2-1a95c6756d90ce6dd74a9f08f6dd50a8_720w.jpg) 20 | 21 | ![img](https://pic2.zhimg.com/80/v2-29778a46617e491adb554ac5fa5823b1_720w.jpg) 22 | 23 | 接着就是路径，提醒小白，安装到C盘真的可以避免后续的很多小问题，但是尽管这样我也没有尝试过把它装入C盘。 24 | 25 | > 我选择了E盘，单独创建一个文件夹命名为“Anaconda”. 26 | > 注意路径要简单，我的是 E:\Anaconda\ 27 | > 28 | > ——不要有空格！！！ 29 | > 30 | > ——不要有中文字符！！！ 31 | 32 | ![img](https://pic2.zhimg.com/80/v2-64590b21362f65132f54d1a597f0c809_720w.jpg) 33 | 34 | 大家注意到这一幅图跟上一幅图不一样，那是因为，我建议两个都打勾，第一个不打勾对初学者其实挺不友好的，打勾了能省很多事儿 35 | 36 | ![img](https://pic2.zhimg.com/80/v2-8c295d70c79db8487180a10a3b1798a1_720w.jpg) 37 | 38 | 点击install，等待不太漫长的进度条…… 39 | 40 | ![img](https://pic1.zhimg.com/80/v2-76ba09f793eb5ed5044e63a9a07bb4c0_720w.jpg) 41 | 42 | 提示安装成功…… 43 | 44 | ![img](https://pic4.zhimg.com/80/v2-fde78f1bb363c59f31ed653e872e788b_720w.jpg) 45 | 46 | 提示安装VScode，选择点击“skip” 47 | 48 | ![img](https://pic1.zhimg.com/80/v2-ec381c321d4a6abc833acc39e2ab1698_720w.jpg) 49 | 50 | 两个“learn”，都取消打勾 51 | 52 | ![img](https://pic2.zhimg.com/80/v2-6ed983e8cedf48dc0c0870d3de2c620d_720w.jpg) 53 | 54 | ### 3.2 检验安装 55 | 56 | 我们在cmd中输入python，检查是否有Python环境 57 | 58 | > Q：cmd怎么打开 59 | > 60 | > A：“cmd命令的打开方法：1、在电脑桌面中使用“WIN＋R”组合键，打开的“运行”窗口，输入“cmd”命令并回车即可打开；2、打开“开始”菜单，在搜索框中输入“cmd”，点击“cmd.exe”即可打开。” 61 | 62 | ![img](https://pic3.zhimg.com/80/v2-bad7f81e99c68cce4359359f40bcaa0a_720w.jpg) 63 | 64 | 在cmd中输入： conda info，——查看是否有？ (检验安装成功的标志) 65 | 66 | ![img](https://pic3.zhimg.com/80/v2-26f33b7c61cc29739f172bb1a1392d66_720w.jpg) 67 | 68 | ### 3.3 检验其他是否安装成功。尤其是 Anaconda Navifator 69 | 70 | > 点击，看是否能够进入界面，若成功，大功告成。 71 | 72 | ![img](https://pic1.zhimg.com/80/v2-724a6e563be81c17c9cd060ddc8e8d50_720w.jpg) 73 | 74 | ![img](https://pic1.zhimg.com/80/v2-4b2b25ef579b8dfdd55e5f4950de18cc_720w.jpg) 75 | 76 | 77 | 78 | 到这里，你已经完成了Python环境的基础配置，至于更高阶的配置，那么我们等到需要进行对应的分析的时候，再进行安装。 -------------------------------------------------------------------------------- /docs/CONFIG/config_2.md: -------------------------------------------------------------------------------- 1 | # 配置2：Linux环境 2 | 3 | Linux环境对于生物信息学而言，是一个必备的环境，所以我们得确保自己的电脑有一个可以用的linux环境。在这里，我推荐wsl，这是微软官方发布的一个专门用于linux分析用的环境，可以说，这避免了大量的vmware的bug出现，对于小白也是相当友好的。 4 | 5 | 为了简单起见，我这里放上微软的官方教程：https://docs.microsoft.com/zh-cn/windows/wsl/install 6 | 7 | -------------------------------------------------------------------------------- /docs/MOFA/README.md: -------------------------------------------------------------------------------- 1 | # 分析2：MOFA单细胞多组学因子分析 2 | 3 | -------------------------------------------------------------------------------- /docs/MOFA/mofa_1.md: -------------------------------------------------------------------------------- 1 | # MOFA分析： 环境配置 2 | 3 | mofa的整个分析，也是分为Python与R两部分，可以说，复杂的生物信息学分析，都与Python跟R密切相关，而对于Mofa分析而言，或许更为复杂。 4 | 5 | ## 1. Python部分 6 | 7 | 在Python部分，主要有以下几个包需要被安装：scglue，scanpy，mofapy2以及episcanpy 8 | 9 | ### 1.1 conda环境 10 | 11 | 在Python部分需要安装的包，可能与过往需要的包会起到冲突，所以我们新建一个conda环境 12 | 13 | ```python 14 | conda create -n rna python=3.6 15 | conda activate rna 16 | ``` 17 | 18 | 通过上述两行代码，我们现在进入了一个叫rna的python虚拟环境，这个环境是非常干净的，没有什么多余的包，所以我们在下一步中将依次安装需要的依赖 19 | 20 | ### 1.2 Jupyterlab安装 21 | 22 | 由于这是一个新的python环境，所以我们需要重新装一下jupyter 23 | 24 | ``` 25 | conda install -c conda-forge jupyterlab 26 | ``` 27 | 28 | ### 1.3 scanpy安装 29 | 30 | 接下来进入正题，我们安装单细胞处理所必需的包-scanpy 31 | 32 | ```python 33 | conda install seaborn scikit-learn statsmodels numba pytables 34 | conda install -c conda-forge python-igraph leidenalg 35 | pip install scanpy 36 | ``` 37 | 38 | ### 1.4 scglue安装 39 | 40 | 安装完scanpy后，我们安装一下用于单细胞配对的包scglue 41 | 42 | ```shell 43 | conda install -c defaults -c pytorch -c bioconda -c conda-forge -c scglue scglue --yes 44 | ``` 45 | 46 | ### 1.5 mofapy2安装 47 | 48 | 我们接下来安装用于多组学因子训练的模型mofa 49 | 50 | ``` 51 | pip install mofapy2 52 | ``` 53 | 54 | ### 1.6 episcanpy安装 55 | 56 | 最后，是表观基因组的单细胞处理的包episcanpy的安装 57 | 58 | ```shell 59 | pip install git+https://github.com/colomemaria/epiScanpy 60 | ``` 61 | 62 | 以上，就是Python环境所需要的全部依赖了 63 | 64 | ## 2. R环境 65 | 66 | ### 2.1 安装MOFA2 67 | 68 | ```R 69 | if (!requireNamespace("BiocManager", quietly = TRUE)) 70 | install.packages("BiocManager") 71 | 72 | BiocManager::install("MOFA2") 73 | ``` 74 | 75 | ### 2.2 安装data.table 76 | 77 | ```R 78 | install.packages('data.table',type='source')#源码的形式安装 79 | ``` 80 | 81 | ### 2.3 安装scater 82 | 83 | ```R 84 | BiocManager::install("scater",type='source') 85 | ``` 86 | 87 | ### 2.4 安装其他 88 | 89 | 上述三个包的安装比较特别，就单独列了出来，其他的就用下面的命令安装就好 90 | 91 | ```R 92 | install.packages(c('purrr', 'ggplot2', 'reticulate', 'argparse','RColorBrewer')) 93 | ``` 94 | 95 | 到这里，我们复现Nature所需要的R环境就基本配置好了 -------------------------------------------------------------------------------- /docs/MOFA/mofa_2.md: -------------------------------------------------------------------------------- 1 | # MOFA分析： 单细胞样本对齐 2 | 3 | 前面提到，我们在单细胞多组学中，会得到大量的细胞，每个细胞都有不同的状态。对于mofa而言，2018年的nature使用了scnmt技术，该技术可以同时测定一个细胞的ATAC与RNA情况，但在大部分情况下，我们只能从同一个样本中分别进行ATAC与RNA的测定，而不是对同一个细胞进行测定，但是从理论上来说，应该是有一些细胞是相像的，于是，如何把这些细胞对齐，成为了一个需要解决的问题。 4 | 5 | 本小节参照**分析3：单细胞样本对齐** 6 | 7 | -------------------------------------------------------------------------------- /docs/MOFA/mofa_3.assets/do3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/MOFA/mofa_3.assets/do3.png -------------------------------------------------------------------------------- /docs/MOFA/mofa_3.assets/do4-20221019030752121.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/MOFA/mofa_3.assets/do4-20221019030752121.png 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-------------------------------------------------------------------------------- 1 | # MOFA分析： scRNA-seq数据处理 2 | 3 | 由于单细胞原始矩阵较大，我们进行mofa分析时往往需要对scRNA-seq数据进行预处理 4 | 5 | ## 1. 数据准备 6 | 7 | ### 1.1 导入包 8 | 9 | ```python 10 | import anndata 11 | import networkx as nx 12 | import scanpy as sc 13 | from matplotlib import rcParams 14 | import pandas as pd 15 | import numpy as np 16 | ``` 17 | 18 | ### 1.2 导入数据 19 | 20 | ```python 21 | rna_pair = anndata.read_h5ad("rna_pair.h5ad") 22 | #atac_pair = anndata.read_h5ad("atac_pair.h5ad") 23 | new_pair=pd.read_csv('mofa_pre_pair.csv') 24 | ``` 25 | 26 | ### 1.3 配对细胞重命名 27 | 28 | ```python 29 | #new_cell=[] 30 | #for i in rna_pair.obs.index: 31 | # new_cell.append(new_pair[new_pair['scRNA']==i]['sample'].iloc[0]) 32 | #rna_pair.obs.index=new_cell 33 | #new_cell[:5] 34 | 35 | r1=rna_pair[new_pair['scRNA']] 36 | r1.obs.index=new_pair.index.values 37 | r1.write_h5ad('rna_mofa_pre1.h5ad',compression="gzip")#必须先保存 38 | rna_pair=anndata.read_h5ad("rna_mofa_pre1.h5ad") 39 | 40 | ``` 41 | 42 | ## 2. 单细胞数据质控 43 | 44 | ### 2.1 过滤细胞与基因 45 | 46 | ```python 47 | sc.pp.filter_cells(rna_pair, min_genes=2000) 48 | sc.pp.filter_genes(rna_pair, min_cells=150) 49 | ``` 50 | 51 | ### 2.2 过滤线粒体基因 52 | 53 | ```python 54 | rna_pair.var['mt'] = rna_pair.var_names.str.startswith('MT-') # annotate the group of mitochondrial genes as 'mt' 55 | sc.pp.calculate_qc_metrics(rna_pair, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True) 56 | sc.pl.violin(rna_pair, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'], 57 | jitter=0.4, multi_panel=True) 58 | ``` 59 | 60 | ![do3](mofa_3.assets/do4-6120066.png) 61 | 62 | ### 2.3 观察分布 63 | 64 | ```python 65 | sc.pl.scatter(rna_pair, x='total_counts', y='pct_counts_mt') 66 | sc.pl.scatter(rna_pair, x='total_counts', y='n_genes_by_counts') 67 | ``` 68 | 69 | ![do5](mofa_3.assets/do5-6120069.png)![do4](mofa_3.assets/do4-20221019030752121.png) 70 | 71 | ### 2.4 根据分布过滤细胞 72 | 73 | ```python 74 | rna_pair = rna_pair[rna_pair.obs.n_genes_by_counts < 3500, :] 75 | rna_pair = rna_pair[rna_pair.obs.pct_counts_mt < 1, :] 76 | ``` 77 | 78 | ### 2.5 高变基因计算 79 | 80 | ```python 81 | sc.pp.normalize_total(rna_pair, target_sum=1e4) 82 | sc.pp.log1p(rna_pair) 83 | sc.pp.highly_variable_genes(rna_pair, min_mean=0.0125, max_mean=3, min_disp=0.5) 84 | sc.pl.highly_variable_genes(rna_pair) 85 | rna_pair = rna_pair[:, rna_pair.var.highly_variable] 86 | ``` 87 | 88 | ![do6](mofa_3.assets/do6.png) 89 | 90 | ### 2.6 回归 91 | 92 | ```python 93 | sc.pp.regress_out(rna_pair, ['total_counts', 'pct_counts_mt']) 94 | sc.pp.scale(rna_pair, max_value=10) 95 | rna_pair.write_h5ad('rna_mofa.h5ad',compression='gzip') 96 | ``` 97 | 98 | -------------------------------------------------------------------------------- /docs/MOFA/mofa_4.assets/do10.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /docs/MOFA/mofa_4.md: -------------------------------------------------------------------------------- 1 | # MOFA分析：scATAC-seq数据处理 2 | 3 | 由于单细胞ATAC-seq数据依然较大，我们需要对其进行过滤 4 | 5 | ## 1. 数据准备 6 | 7 | ### 1.1 导入包 8 | 9 | ```python 10 | import anndata 11 | import networkx as nx 12 | import scanpy as sc 13 | from matplotlib import rcParams 14 | import pandas as pd 15 | import numpy as np 16 | import episcanpy.api as epi 17 | ``` 18 | 19 | ### 1.2 导入数据 20 | 21 | ```python 22 | atac_pair = anndata.read_h5ad("atac_pair.h5ad") 23 | new_pair=pd.read_csv('mofa_pre_pair.csv') 24 | ``` 25 | 26 | ### 1.3 配对细胞重命名 27 | 28 | ```python 29 | #new_cell=[] 30 | #for i in atac_pair.obs.index: 31 | # new_cell.append(new_pair[new_pair['scATAC']==i]['sample'].iloc[0]) 32 | #atac_pair.obs.index=new_cell 33 | #new_cell[:5] 34 | 35 | r1=atac_pair[new_pair['scATAC']] 36 | r1.obs.index=new_pair.index.values 37 | r1.write_h5ad('atac_mofa_pre1.h5ad',compression="gzip")#必须先保存 38 | atac_pair=anndata.read_h5ad("atac_mofa_pre1.h5ad") 39 | ``` 40 | 41 | ### 1.4 染色体位置重命名 42 | 43 | ```python 44 | lo=[] 45 | for i in atac_pair.var_names: 46 | lo.append(i.replace(':','_').replace('-','_')) 47 | atac_pair.var.index=lo 48 | ``` 49 | 50 | ### 1.5 染色体位置相关基因标注 51 | 52 | #### 1.5.1 Annotation下载 53 | 54 | ```shell 55 | wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_19/gencode.v19.annotation.gtf.gz -O gencode.v19.annotation.gtf.gz 56 | gunzip gencode.v19.annotation.gtf 57 | ``` 58 | 59 | #### 1.5.2 Annotation标注 60 | 61 | ```python 62 | epi.tl.find_genes(atac_pair, 63 | gtf_file='gencode.v19.annotation.gtf', 64 | key_added='transcript_annotation', 65 | upstream=2000, 66 | feature_type='transcript', 67 | annotation='HAVANA', 68 | raw=False) 69 | ``` 70 | 71 | ## 2. 数据处理 72 | 73 | ### 2.1 过滤空feature跟barcode 74 | 75 | ```python 76 | # remove any potential empty features or barcodes 77 | epi.pp.filter_cells(atac_pair, min_features=1) 78 | epi.pp.filter_features(atac_pair, min_cells=1) 79 | atac_pair 80 | ``` 81 | 82 | > AnnData object with n_obs × n_vars = 23770 × 214125 83 | 84 | ### 2.2 对数化 85 | 86 | ```python 87 | atac_pair.obs['log_nb_features'] = [np.log10(x) for x in atac_pair.obs['nb_features']] 88 | epi.pl.violin(atac_pair, ['nb_features']) 89 | epi.pl.violin(atac_pair, ['log_nb_features']) 90 | ``` 91 | 92 | ![do8](mofa_4.assets/do8.png)![do7](mofa_4.assets/do7.png) 93 | 94 | ### 2.3 设置每个细胞最小具有的feature数 95 | 96 | ```python 97 | # set a minimum number of cells to keep 98 | min_features = 400 99 | 100 | epi.pp.coverage_cells(atac_pair, binary=True, log=False, bins=50, 101 | threshold=min_features, save='Buenrostro_bulk_peaks_coverage_cells.png') 102 | epi.pp.coverage_cells(atac_pair, binary=True, log=10, bins=50, 103 | threshold=min_features, save='Buenrostro_bulk_peaks_coverage_cells_log10.png') 104 | ``` 105 | 106 | ![do9](mofa_4.assets/do9-6120084.png) 107 | 108 | ![do10](https://raw.githubusercontent.com/Starlitnightly/bioinformatic_galaxy/master/img/do10.png) 109 | 110 | ### 2.4 设置每个feature存在于最小的细胞数 111 | 112 | ```python 113 | # minimum number of cells sharing a feature 114 | min_cells = 80 115 | epi.pp.coverage_features(atac_pair, binary=True, log=False, 116 | threshold=min_cells, save='Buenrostro_bulk_peaks_coverage_peaks.png') 117 | epi.pp.coverage_features(atac_pair, binary=True, log=True, 118 | threshold=min_cells, save='Buenrostro_bulk_peaks_coverage_peaks_log10.png') 119 | ``` 120 | 121 | ![do11](mofa_4.assets/do11-6120091.png) 122 | 123 | ![do12](mofa_4.assets/do12-6120093.png) 124 | 125 | ### 2.5 过滤细胞与feature 126 | 127 | ```python 128 | min_features = 400 129 | epi.pp.filter_cells(atac_pair, min_features=min_features) 130 | min_cells = 80 131 | epi.pp.filter_features(atac_pair, min_cells=min_cells) 132 | ``` 133 | 134 | ### 2.6 计算高变feature 135 | 136 | ```python 137 | min_score_value = 0.515 138 | nb_feature_selected = 20000 139 | epi.pl.variability_features(atac_pair,log=None, 140 | min_score=min_score_value, nb_features=nb_feature_selected, 141 | save='variability_features_plot_bonemarrow_peakmatrix.png') 142 | 143 | epi.pl.variability_features(atac_pair,log='log10', 144 | min_score=min_score_value, nb_features=nb_feature_selected, 145 | save='variability_features_plot_bonemarrow_peakmatrix_log10.png') 146 | ``` 147 | 148 | ![do13](mofa_4.assets/do13.png) 149 | 150 | ![do14](mofa_4.assets/do14.png) 151 | 152 | ## 3. 过滤高变feature的cell 153 | 154 | ```python 155 | atac_pair.raw=atac_pair 156 | # create a new AnnData containing only the most variable features 157 | atac_pair = epi.pp.select_var_feature(atac_pair,nb_features=nb_feature_selected,show=False,copy=True) 158 | epi.pl.violin(atac_pair, ['nb_features']) 159 | epi.pl.violin(atac_pair, ['log_nb_features']) 160 | ``` 161 | ![do15](mofa_4.assets/do15.png) 162 | 163 | ![do16](mofa_4.assets/do16-1636004475065.png) 164 | 165 | ```python 166 | epi.pp.filter_cells(atac_pair, min_features=1000) 167 | epi.pp.filter_cells(atac_pair, max_features=4000) 168 | atac_pair.write_h5ad('atac_mofa.h5ad',compression="gzip") 169 | ``` 170 | 171 | -------------------------------------------------------------------------------- /docs/MOFA/mofa_5.assets/image-20211104134822671.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/MOFA/mofa_5.assets/image-20211104134822671.png -------------------------------------------------------------------------------- /docs/MOFA/mofa_5.md: -------------------------------------------------------------------------------- 1 | # MOFA分析：MOFA模型构建 2 | 3 | 到本小节，就正式构建MOFA模型了 4 | 5 | ## 1. 数据准备 6 | 7 | ### 1.1 导入包 8 | 9 | ```python 10 | from mofapy2.run.entry_point import entry_point 11 | import anndata 12 | import networkx as nx 13 | import scanpy as sc 14 | from matplotlib import rcParams 15 | import pandas as pd 16 | import numpy as np 17 | ``` 18 | 19 | ### 1.2 导入数据 20 | 21 | ```python 22 | rna=anndata.read_h5ad("rna_mofa.h5ad") 23 | atac=anndata.read_h5ad("atac_mofa.h5ad") 24 | ``` 25 | 26 | ### 1.3 计算多组学公共list 27 | 28 | ```python 29 | ret3= list(set(rna.obs.index).intersection(atac.obs.index)) 30 | ``` 31 | 32 | ### 1.4 观察细胞类型 33 | 34 | ```python 35 | for i in list(set(rna[ret3].obs['cell_type'])): 36 | print(i,len(rna[ret3].obs.loc[rna[ret3].obs['cell_type']==i])/len(rna[ret3].obs)) 37 | ``` 38 | 39 | > OPC 0.046986390149060274 40 | > 41 | > PER.END 0.0031756318859364873 42 | > 43 | > ASC 0.07465975372650681 44 | > 45 | > INH 0.08165910563836681 46 | > 47 | > MG 0.0390149060272197 48 | > 49 | > ODC 0.6180816591056384 50 | > 51 | > EX 0.13642255346727156 52 | 53 | ## 2. MOFA模型 54 | 55 | ### 2.1 MOFA参数设置 56 | 57 | ```python 58 | # initialise the entry point 59 | 60 | ent1 = entry_point() 61 | ent1.set_data_options( 62 | scale_groups = False, 63 | scale_views = False, 64 | center_groups=True, 65 | ) 66 | ``` 67 | 68 | ### 2.2 构建组学层 69 | 70 | ```python 71 | data_mat=[[None for g in range(1)] for m in range(2)] 72 | data_mat[0][0]=rna[ret3].X 73 | data_mat[1][0]=np.array(atac[ret3].X.todense()) 74 | ``` 75 | 76 | ### 2.3 MOFA数据导入 77 | 78 | ```python 79 | ent1.set_data_matrix(data_mat, likelihoods = ["gaussian","gaussian"], 80 | views_names=['rna','atac'], 81 | samples_names=[ret3], 82 | features_names=[rna[ret3].var_names,atac[ret3].var_names]) 83 | ``` 84 | 85 | ### 2.4 模型参数设置 86 | 87 | ```python 88 | ent1.set_model_options( 89 | factors = 20, 90 | spikeslab_weights = True, 91 | ard_factors = True, 92 | ard_weights = True 93 | ) 94 | ent1.set_train_options( 95 | iter = 3000, 96 | convergence_mode = "slow", 97 | startELBO = 1, 98 | freqELBO = 1, 99 | dropR2 = 0.001, 100 | gpu_mode = True, 101 | verbose = False, 102 | seed = 1 103 | ) 104 | ``` 105 | 106 | ### 2.5 模型运行并保存 107 | 108 | ```python 109 | 110 | ent1.build() 111 | 112 | ent1.run() 113 | 114 | # Save the output 115 | ent1.save(outfile='mofa_factor.hdf5') 116 | ``` 117 | 118 | ![image-20211104134822671](mofa_5.assets/image-20211104134822671.png) 119 | 120 | ### 2.6 meta数据导出 121 | 122 | ``` 123 | rna[ret3].obs.to_csv('mofa_meta.csv') 124 | ``` 125 | 126 | -------------------------------------------------------------------------------- /docs/MOFA/mofa_6.md: -------------------------------------------------------------------------------- 1 | # MOFA下游分析：R语言环节 2 | 3 | ## 1. MOFA数据处理 4 | 5 | ### 1.1 数据导入 6 | 7 | ```R 8 | #load library 9 | library(MOFA2) 10 | library(data.table) 11 | library(ggplot2) 12 | library(tidyverse) 13 | #install.packages('psych') 14 | #install.packages('ggpubr') 15 | 16 | #load data 17 | sample_metadata <- fread('mofa_meta.csv')#注意第一列细胞的标头要写为sample 18 | model <- load_model("mofa_factor.hdf5") 19 | samples_metadata(model) <- sample_metadata 20 | ``` 21 | 22 | ### 1.2 Var数据导出 23 | 24 | ```R 25 | #Variance decomposition by Factor 26 | plot_variance_explained(model, max_r2=15) 27 | variance_explained=model@cache[["variance_explained"]][["r2_per_factor"]][["group0"]] 28 | write.table(variance_explained,file='variance_explained.csv',sep=',',row.names =FALSE) 29 | 30 | ``` 31 | 32 | ### 1.3 Cor数据导出 33 | 34 | ```R 35 | #Association analysis 36 | p=correlate_factors_with_covariates(model, covariates = c("cell_type","Age","Sex"), plot="log_pval",return_data=TRUE) 37 | write.table(p,file='correlate_factors.csv',sep=',') 38 | ``` -------------------------------------------------------------------------------- /docs/MOFA/mofa_7.assets/下载 (3).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/MOFA/mofa_7.assets/下载 (3).png -------------------------------------------------------------------------------- /docs/MOFA/mofa_7.assets/下载 (4).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/MOFA/mofa_7.assets/下载 (4).png -------------------------------------------------------------------------------- /docs/MOFA/mofa_7.assets/下载 (5).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/MOFA/mofa_7.assets/下载 (5).png -------------------------------------------------------------------------------- /docs/MOFA/mofa_7.assets/下载 (6).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/MOFA/mofa_7.assets/下载 (6).png -------------------------------------------------------------------------------- /docs/MOFA/mofa_7.assets/下载 (7).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/MOFA/mofa_7.assets/下载 (7).png -------------------------------------------------------------------------------- /docs/MOFA/mofa_7.assets/下载 (8).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/MOFA/mofa_7.assets/下载 (8).png -------------------------------------------------------------------------------- /docs/MultiOMIC/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/MultiOMIC/.DS_Store -------------------------------------------------------------------------------- /docs/MultiOMIC/README.md: -------------------------------------------------------------------------------- 1 | # 多组学分析白皮书 2 | 3 | 在本章中，我们将根据Cell的单细胞转录组与单细胞染色质开放组的数据，构建出一套完整的pipeline用于下游的分析，目的在于使的一些测得很好的数据能被充分的挖掘出来 4 | 5 | - 第一章-数据预处理 6 | - 第二章-GLUE多组学整合 7 | - 第三章-整体细胞分析 8 | - 第四章-细胞亚群分析 9 | -------------------------------------------------------------------------------- /docs/MultiOMIC/book-1.md: -------------------------------------------------------------------------------- 1 | ## 1. 数据预处理 2 | 3 | 在本章中，我们将介绍如何对数据进行初步的处理，数据来源分别为`Cellranger`,`Cellranger-atac`,`velocyto`三个上游分析结果的数据，格式分别为： 4 | 5 | - cellranger：`filtered_feature_bc_matrix.h5`或`filtered_feature_bc_matrix`文件夹 6 | - cellranger-atac: `filtered_peak_bc_matrix`文件夹下的`matrix.mtx`,`barcodes.tsv`,`peaks.bed`三个文件以及`filtered_peak_bc_matrix.h5`文件（如果有的话最好） 7 | - velocyto: `possorted_genome_bam.loom`文件 8 | 9 | ### 1.1 RNA数据预处理 10 | 11 | 我们将`filtered_feature_bc_matrix.h5`与`possorted_genome_bam.loom`文件放在同一个目录下，然后使用scvelo的教程读取这两个文件，然后保存成h5ad格式 12 | 13 | ```python 14 | import scanpy as sc 15 | import scvelo as scv 16 | 17 | #读取矩阵文件 18 | adata=sc.read_10x_h5('filtered_feature_bc_matrix.h5') 19 | #使得obs跟var名唯一 20 | adata.var_names_make_unique() 21 | adata.obs_names_make_unique() 22 | #读取velocyto文件 23 | ldata = scv.read('possorted_genome_bam.loom', cache=True) 24 | #合并两文件 25 | adata = scv.utils.merge(adata, ldata) 26 | #保存文件 27 | adata.write_h5ad('rna_raw.h5ad',compression='gzip') 28 | ``` 29 | 30 | ### 1.2 ATAC数据预处理 31 | 32 | 我们找到`filtered_peak_bc_matrix`文件夹下的`matrix.mtx`,`barcodes.tsv`,`peaks.bed`三个文件，使用episcanpy读取后保存 33 | 34 | ```python 35 | import episcanpy 36 | adata=episcanpy.pp.read_ATAC_10x('matrix.mtx', \ 37 | cell_names='barcodes.tsv', \ 38 | var_names='peaks.bed') 39 | adata.write_h5ad('atac_raw.h5ad',compression="gzip") 40 | ``` 41 | 42 | ### 1.3 ATAC数据生成Gene-Activity矩阵 43 | 44 | 这里提供两种不同的方法达到此目的，一个较简单，一个较繁琐。区别在于，简单的方法你不一定有文件，但繁琐的方法你一定能实现 45 | #### 方法1: 使用`filtered_peak_bc_matrix.h5`文件 46 | 47 | 然后再运行下面的代码生成一个`gene_activity_gene_score.h5`文件 48 | ```shell 49 | MAESTRO scatac-genescore \ 50 | --format h5 \ 51 | --peakcount filtered_peak_bc_matrix.h5 \ 52 | --genedistance 10000 \ 53 | --species GRCh38 \ 54 | --model Enhanced \ 55 | -d /data/result \ 56 | --outprefix gene_activity 57 | ``` 58 | 59 | #### 方法2：使用前面生成的`atac_raw.h5ad`文件 60 | 61 | ```python 62 | import scanpy as sc 63 | atac=sc.read('../data/raw_data/atac.h5ad') 64 | atac.to_df().T.to_csv('../data/raw_data/brca_atac.tsv', sep='\t') 65 | ``` 66 | 67 | 然后再运行下面的代码生成一个`gene_activity_gene_score.h5`文件 68 | ```shell 69 | MAESTRO scatac-genescore \ 70 | --format plain \ 71 | --peakcount brca_atac.tsv \ 72 | --genedistance 10000 \ 73 | --species GRCh38 \ 74 | --model Enhanced \ 75 | -d /data/result \ 76 | --outprefix gene_activity 77 | ``` 78 | 79 | ### 1.4 Gene-Activity矩阵保存为h5ad 80 | 81 | 这一步骤稍微有一些繁琐，不过代码照着运行就好了 82 | 83 | ```python 84 | import os 85 | import collections 86 | import tables 87 | import h5py 88 | import scipy.io 89 | import csv 90 | import gzip 91 | import scipy.sparse as sp_sparse 92 | import argparse as ap 93 | import pandas as pd 94 | FeatureBCMatrix = collections.namedtuple('FeatureBCMatrix', ['ids', 'names', 'barcodes', 'matrix']) 95 | def read_10X_h5(filename): 96 | """Read 10X HDF5 files, support both gene expression and peaks.""" 97 | with tables.open_file(filename, 'r') as f: 98 | try: 99 | group = f.get_node(f.root, 'matrix') 100 | except tables.NoSuchNodeError: 101 | print("Matrix group does not exist in this file.") 102 | return None 103 | feature_group = getattr(group, 'features') 104 | ids = getattr(feature_group, 'id').read() 105 | names = getattr(feature_group, 'name').read() 106 | barcodes = getattr(group, 'barcodes').read() 107 | data = getattr(group, 'data').read() 108 | indices = getattr(group, 'indices').read() 109 | indptr = getattr(group, 'indptr').read() 110 | shape = getattr(group, 'shape').read() 111 | matrix = sp_sparse.csc_matrix((data, indices, indptr), shape=shape) 112 | return FeatureBCMatrix(ids, names, barcodes, matrix) 113 | 114 | ``` 115 | 116 | ```python 117 | import re 118 | scatac_count=read_10X_h5('gene_activity_gene_score.h5') 119 | peakmatrix = scatac_count.matrix 120 | features = scatac_count.names.tolist() 121 | features = [re.sub("\W", "_", feature.decode()) for feature in features] 122 | features = [feature.encode() for feature in features] 123 | barcodes = scatac_count.barcodes.tolist() 124 | adata=anndata.AnnData(peakmatrix.T,obs=barcodes,var=features) 125 | adata.obs.index=[i.decode('utf-8') for i in adata.obs[0]] 126 | adata.var.index=[i.decode('utf-8') for i in adata.var[0]] 127 | del adata.obs[0] 128 | del adata.var[0] 129 | adata.write_h5ad('gene_activity_gene_score.h5ad',compression='gzip') 130 | ``` 131 | 132 | 到此，我们数据分析所需要的所有基本文件就准备完成了 133 | -------------------------------------------------------------------------------- /docs/MultiOMIC/book-2.md: -------------------------------------------------------------------------------- 1 | ## 2. GLUE多组学整合 2 | 3 | 文字版教程解释参照GLUE官方文档:https://scglue.readthedocs.io/zh_CN/latest/tutorials.html 4 | 5 | 但具体的代码参照我的文件，我对教程以及我们的数据进行了配套处理，分别是`glue-0.ipynb`,`glue-1.ipynb`,`glue-2.ipynb`三个文件。 6 | 7 | - `glue-0.ipynb`: https://github.com/Starlitnightly/bioinformatic_tutorial/blob/main/MultiOMIC-ipynb/glue-0.ipynb 8 | - `glue-1.ipynb`: https://github.com/Starlitnightly/bioinformatic_tutorial/blob/main/MultiOMIC-ipynb/glue-1.ipynb 9 | - `glue-2.ipynb`: https://github.com/Starlitnightly/bioinformatic_tutorial/blob/main/MultiOMIC-ipynb/glue-2.ipynb 10 | 11 | 但值得注意的是，我们这里得到的细胞只是整合后，还没进行配对，就是使得一个细胞同时具有两个组学层，配对的方法如下，我们需要使用具有X_glue层的rna与atac文件 12 | 13 | ```python 14 | #读取数据 15 | rna=sc.read('../cellanno/rna_anno.h5ad') 16 | atac=sc.read('../cellanno/atac_anno.h5ad') 17 | 18 | #提取GLUE层结果 19 | rna_loc=pd.DataFrame(rna.obsm['X_glue'], index=rna.obs.index) 20 | atac_loc=pd.DataFrame(atac.obsm['X_glue'], index=atac.obs.index) 21 | 22 | #对GLUE层进行Pearson系数分析 23 | import numpy as np 24 | import gc 25 | len1=(len(rna_loc)//5000)+1 26 | p_pd=pd.DataFrame(columns=['rank_'+str(i) for i in range(50)]) 27 | n_pd=pd.DataFrame(columns=['rank_'+str(i) for i in range(50)]) 28 | for j in range(len1): 29 | c=pd.DataFrame() 30 | for i in range(len1): 31 | t1=rna_loc.iloc[5000*(i):5000*(i+1)] 32 | t2=atac_loc.iloc[5000*(j):5000*(j+1)] 33 | a=np.corrcoef(t1,t2)[len(t1):,0:len(t1)] 34 | b=pd.DataFrame(a,index=t2.index,columns=t1.index) 35 | 36 | c=pd.concat([c,b],axis=1) 37 | del t1 38 | del t2 39 | del a 40 | del b 41 | gc.collect() 42 | for i in range(len(c)): 43 | t_c=c.iloc[i] 44 | p_pd.loc[t_c.name]=c.iloc[i].sort_values(ascending=False)[:50].values 45 | n_pd.loc[t_c.name]=c.iloc[i].sort_values(ascending=False)[:50].index.tolist() 46 | print('Now epoch is {}, {}/{}'.format(j,j*5000+len(c),len(atac_loc))) 47 | del c 48 | gc.collect() 49 | 50 | #寻找最近的细胞，其中depth的灵活调整可以使得配对成功的细胞数变大，同时精度有所下降 51 | def find_neighbor_cell(p_pd,n_pd,depth=10): 52 | rubish_c=[] 53 | finish_c=[] 54 | for d in range(depth): 55 | p_pd=p_pd.loc[p_pd['rank_{}'.format(d)]>0.9] 56 | p_pd=p_pd.sort_values('rank_{}'.format(d),ascending=False) 57 | for i in p_pd.index: 58 | name=n_pd.loc[i,'rank_{}'.format(d)] 59 | if name not in rubish_c: 60 | finish_c.append(i) 61 | rubish_c.append(name) 62 | else: 63 | continue 64 | p_pd=p_pd.loc[~p_pd.index.isin(finish_c)] 65 | n_pd=n_pd.loc[~n_pd.index.isin(finish_c)] 66 | result=pd.DataFrame() 67 | result['omic_1']=rubish_c 68 | result['omic_2']=finish_c 69 | result.index=['cell_{}'.format(i) for i in range(len(result))] 70 | return result 71 | 72 | res_pair=find_neighbor_cell(p_pd,n_pd,depth=20) 73 | res_pair.head() 74 | ``` 75 | 76 | | omic_1 | omic_2 | | 77 | | -----: | -----------------: | ---------------------- | 78 | | cell_0 | AACTCAGCATGATCCA | neg-GCACGGTGTGCAAGAC-1 | 79 | | cell_1 | AAACGGGTCGGCGCAT | neg-TCTATTGAGAGGCAGG-1 | 80 | | cell_2 | GACGCGTAGACAAGCC | neg-GTGTCCTGTCATTGCA-1 | 81 | | cell_3 | AAACCTGTCCCAACGG-1 | neg-AGTGTACCATGTGGGA-1 | 82 | | cell_4 | CATATTCCAAACCCAT | neg-TCAGCTCCAACGGGTA-1 | 83 | 84 | ## 3. 整体细胞分析 85 | 86 | 文字版教程解释参照Pyomic官方文档：https://pyomic.readthedocs.io/en/latest/Tutorials/t_cellanno.html 87 | 88 | 但具体的代码依旧参照我的文件，我对相关配置进行了修改处理，分别是`cellanno-1.ipynb`,`cellanno-2.ipynb` 89 | 90 | - `cellanno-1.ipynb`: https://github.com/Starlitnightly/bioinformatic_tutorial/blob/main/MultiOMIC-ipynb/cellanno-1.ipynb 91 | - `cellanno-2.ipynb`: https://github.com/Starlitnightly/bioinformatic_tutorial/blob/main/MultiOMIC-ipynb/cellanno-2.ipynb 92 | 93 | 绘图代码见`figure1.ipynb`与`figure2.ipynb`文件 94 | 95 | - `figure1.ipynb`: https://github.com/Starlitnightly/bioinformatic_tutorial/blob/main/MultiOMIC-ipynb/figure1.ipynb 96 | - `figure2.ipynb`: https://github.com/Starlitnightly/bioinformatic_tutorial/blob/main/MultiOMIC-ipynb/figure2.ipynb 97 | 98 | -------------------------------------------------------------------------------- /docs/MultiOMIC/book-3.assets/image-20221019004245874-6119913.png: 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转录因子网络 2 | 3 | 恭喜你，来到了B细胞亚群的最后一小节分析，我们在前面的研究中已经比较过淋巴结转移的差异表达、细胞通讯以及动态调控的差异，更进一步的，我们需要挖掘背后调控的分子机制，到底是什么因素在控制着淋巴结转移，于是很直观的，我们将采取转录因子分析。 4 | 5 | 我们在GLUE的第三部分，已经得到了每个细胞的AUCell文件，该文件代表了每一个细胞所含有的转录因子类型以及活性，下面，我们就利用这个文件，进一步挖掘阳性淋巴结与阴性淋巴结背后的调控关系。 6 | 7 | 首先依然是导入包 8 | 9 | ```python 10 | #导入包 11 | import anndata 12 | print('anndata(Ver): ',anndata.__version__) 13 | import scanpy as sc 14 | print('scanpy(Ver): ',sc.__version__) 15 | import scltnn #非必需 16 | print('scltnn(Ver): ',scltnn.__version__) 17 | import matplotlib.pyplot as plt 18 | import matplotlib 19 | print('matplotlib(Ver): ',matplotlib.__version__) 20 | import seaborn as sns 21 | print('seaborn(Ver): ',sns.__version__) 22 | import numpy as np 23 | print('numpy(Ver): ',np.__version__) 24 | import pandas as pd 25 | print('pandas(Ver): ',pd.__version__) 26 | import scvelo as scv 27 | print('scvelo(Ver): ',scv.__version__) 28 | import Pyomic 29 | print('Pyomic(Ver): ',Pyomic.__version__) 30 | 31 | #绘图参数设置 32 | sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3) 33 | sc.settings.set_figure_params(dpi=80, facecolor='white') 34 | 35 | sc_color=['#7CBB5F','#368650','#A499CC','#5E4D9A','#78C2ED','#866017','#9F987F', '#E0DFED', '#EF7B77', '#279AD7', 36 | '#F0EEF0', '#1F577B', '#A56BA7', '#E0A7C8', '#E069A6', '#941456', '#FCBC10', '#EAEFC5', '#01A0A7', '#75C8CC', 37 | '#F0D7BC', '#D5B26C', '#D5DA48', '#B6B812','#9DC3C3', '#A89C92', '#FEE00C','#FEF2A1'] 38 | 39 | ``` 40 | 41 | 接着，我们导入了一些数据 42 | 43 | ```python 44 | #rna表达数据 45 | rna=sc.read('B_cell_anno_new.h5ad') 46 | #aucell数据 47 | rna_aucell=pd.read_csv('../glue_true/rna_aucell.tsv',sep ='\t') 48 | rna_aucell.set_index(rna_aucell.columns[0],inplace=True) 49 | #生成aucell的h5ad（B细胞） 50 | rna_auc_adata=anndata.AnnData(rna_aucell) 51 | rna_auc_adata=rna_auc_adata[rna.obs.index] 52 | rna_auc_adata.obs=rna.obs 53 | rna_auc_adata.uns=rna.uns 54 | 55 | ``` 56 | 57 | 我们绘制出阳性淋巴结与阴性淋巴结的平均转录因子水平 58 | 59 | ```python 60 | regulons=pd.DataFrame(columns=rna_auc_adata.var.index) 61 | for i in list(set(rna_auc_adata.obs['Type'])): 62 | regulons.loc[i]=rna_auc_adata[rna_auc_adata.obs['Type']==i].X.mean(axis=0) 63 | regulons.head() 64 | ``` 65 | 66 | 67 |
68 | ![Image title](book-7.assets/image-20221019021043508-6120043.png){ width="300" } 69 |
70 | 71 | 72 | 我们使用Pos减去Neg，得到阳性与阴性的转录因子离散度 73 | 74 | ```python 75 | regulons_d=regulons.loc['Pos']-regulons.loc['Neg'] 76 | regulons_d=regulons_d.sort_values() 77 | ``` 78 | 79 | 我们接着可视化离散情况，找出离散度最大的转录因子 80 | 81 | ```python 82 | from colormap import Colormap 83 | c = Colormap() 84 | rnacmap = c.cmap_linear( '#ffffff','#de0029','#a51616') 85 | nodecmap= c.cmap_linear( '#a51616','#ffffff',sc_color[11]) 86 | 87 | type_color={ 88 | 'Pos':'#9B7170', 89 | 'Neg':'#C65A50' 90 | } 91 | fig, ax = plt.subplots(figsize=(6,2)) 92 | 93 | plt.scatter(regulons_d.index,regulons_d.values,c=abs(regulons_d.values),cmap=rnacmap) 94 | regulons_d_num=(abs(regulons_d.min())>abs(regulons_d.max()) and abs(regulons_d.min()) or abs(regulons_d.max())) 95 | regulons_d_min=(0-round(regulons_d_num,2))-0.01 96 | regulons_d_max=round(regulons_d_num,2)+0.01 97 | plt.yticks(fontsize=8) 98 | plt.ylim(regulons_d_min,regulons_d_max) 99 | plt.grid(False) 100 | 101 | plt.xticks(fontsize=8,rotation=90) 102 | 103 | plt.gca().add_patch(plt.Rectangle(xy=(-2,0.02), 104 | width=len(regulons_d)+3, color=type_color['Pos'], 105 | height=15,alpha=.2, 106 | fill=True, linewidth=2)) 107 | 108 | plt.gca().add_patch(plt.Rectangle(xy=(-2,-0.02), 109 | width=len(regulons_d)+3, color=type_color['Neg'], 110 | height=-15,alpha=.2, 111 | fill=True, linewidth=2)) 112 | 113 | plt.text(len(regulons_d)+1,0.02,'Pos',fontsize=12,color=type_color['Pos'],fontweight='bold') 114 | plt.text(len(regulons_d)+1,-0.02,'Neg',fontsize=12,color=type_color['Neg'],ha='left',fontweight='bold') 115 | 116 | plt.ylabel('Regulon Activity',fontsize=12) 117 | plt.savefig("grn/pos_neg_T.png",dpi=300,bbox_inches = 'tight') 118 | ``` 119 | 120 | 121 |
122 | ![Image title](book-7.assets/image-20221019021239322-6120046.png){ width="300" } 123 |
124 | 125 | 进一步的，我们找到了ESR2与JUN两个转录因子，我们需要绘制其背后具体的调控关系，我们首先导入GLUE生成的调控网络 126 | 127 | ``` 128 | import scglue 129 | grn = scglue.genomics.read_ctx_grn("../glue_true/pruned_grn.csv") 130 | ``` 131 | 132 | 然后我们构建一个函数`plot_sub_nodes`，用于可视化其中特定的转录因子 133 | 134 | ```python 135 | def plot_sub_nodes(grn,goal_node): 136 | sub_grn=grn.subgraph([node for node in nx.dfs_preorder_nodes(grn,goal_node)]) 137 | edge_dict={} 138 | for i in sub_grn.edges(): 139 | edge_dict[i]={"weight":draft_grn.query("`TF` == '{}' and `target` == '{}'".format(i[0],i[1]))['importance'].values[0]} 140 | nx.set_edge_attributes(sub_grn,edge_dict) 141 | 142 | fig, ax = plt.subplots(figsize=(3,3)) 143 | pos = nx.spring_layout(sub_grn,k=3) 144 | 145 | p=dict(sub_grn.nodes) 146 | for i in p.keys(): 147 | if i ==goal_node: 148 | p[i]=0 149 | else: 150 | p[i]=1 151 | 152 | 153 | nx.draw_networkx_nodes(sub_grn, pos, nodelist=p,cmap=nodecmap, 154 | node_color=list(p.values())) 155 | 156 | edgewidth = [sub_grn.get_edge_data(u, v)['weight']/10 for u, v in sub_grn.edges()] 157 | nx.draw_networkx_edges(sub_grn, pos,width=edgewidth) 158 | 159 | 160 | label_options = {"ec": "white", "fc": "white", "alpha": 0.6} 161 | nx.draw_networkx_labels(sub_grn, pos, font_size=10, bbox=label_options) 162 | 163 | plt.grid(False) 164 | plt.axis("off") 165 | plt.xlim(-1.3,1.3) 166 | 167 | ``` 168 | 169 | 我们应用这个函数`plot_sub_nodes` 170 | 171 | ```python 172 | goal_node='JUN' 173 | plot_sub_nodes(grn,goal_node) 174 | plt.savefig("grn/sub_grn_{}.png".format(goal_node),dpi=300,bbox_inches = 'tight') 175 | ``` 176 | 177 |
178 | ![Image title](book-7.assets/image-20221019021513334-6120051.png){ width="300" } 179 |
180 | 到这里，本次B细胞亚群的分析流程就结束了，祝你学有所成 -------------------------------------------------------------------------------- /docs/PLOT/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/PLOT/.DS_Store -------------------------------------------------------------------------------- /docs/PLOT/README.md: -------------------------------------------------------------------------------- 1 | # 绘图：Python可视化 2 | 3 | 今天跟人闲聊，有一个人说R画的图比Python画的好看多了。这句话可把我气的，因为我是不喜欢R语言的，很多R语言的算法我都拿Python重写了。所以。 4 | 5 | 这个教程拖了很久，今天被这个人这句话，刺激到了，其实Python能画的图不比R语言差，就是看你会不会画而已，在本期教程中，我主要讲讲在生物信息学中，常见的一些图以及画法，并且提供相应的数据供参考。 6 | 7 | 数据下载地址：https://github.com/Starlitnightly/bioinformatic_tutorial/tree/main/PLOT/data 8 | 9 | ——by 星夜｜2022.11.22 10 | -------------------------------------------------------------------------------- /docs/PLOT/data/.DS_Store: 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RNA-seq上游】：https://starlitnightly.github.io/bioinformatic_tutorial/RNASEQUP/ 16 | - 【分析2: RNA-seq下游】：https://starlitnightly.github.io/bioinformatic_tutorial/RNASEQ/ 17 | - 【分析3: 单细胞样本对齐】：https://starlitnightly.github.io/bioinformatic_tutorial/SCGLUE/ 18 | - 【分析4: MOFA单细胞多组学因子分析】：https://starlitnightly.github.io/bioinformatic_tutorial/MOFA/ 19 | - 【绘图：Python数据可视化-生物信息学专栏】：https://starlitnightly.github.io/bioinformatic_tutorial/PLOT/ 20 | 21 | ## 数据 22 | 23 | 【绘图：Python数据可视化-生物信息学专栏】：https://github.com/Starlitnightly/bioinformatic_tutorial/tree/main/PLOT/data 24 | 25 | ## License 26 | 27 | 知识共享许可协议 28 | 29 | 本作品采用知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议进行许可。 -------------------------------------------------------------------------------- /docs/RNASEQ/README.md: -------------------------------------------------------------------------------- 1 | # 分析1：RNA-seq下游分析 2 | 3 | 写在前面，当今时代是多组学的时代，而其中，研究最多的应该是RNA-seq相关的分析，所以，作为一个生物信息学专业的学生，最应掌握的基础分析技能，应该是RNA-seq相关的分析，本教程将从RNA-seq分析入手，详细讲解RNA-seq分析相关的技术 -------------------------------------------------------------------------------- /docs/RNASEQ/rnaseq_1.md: -------------------------------------------------------------------------------- 1 | # RNA-seq分析: 环境配置 2 | 3 | 对于RNA-seq相关的分析，一共分为python跟R两部分。 4 | 5 | 我们这里处理的是counts矩阵，count矩阵简单来说，可以理解成横坐标为基因，纵坐标为样本的矩阵，如下表所示 6 | 7 | | geneid | sample1 | sample2 | 8 | | ------------------- | ------- | ------- | 9 | | **ENSG00000223972** | 0 | 0 | 10 | | **ENSG00000227232** | 82 | 63 | 11 | | **ENSG00000278267** | 11 | 2 | 12 | 13 | 我们的目的，就是挖掘这个矩阵中可能包含的信息，各种生物学意义的东西。 14 | 15 | ## 1. Python部分 16 | 17 | 对于python部分，在前面分析时所安装的Anaconda，理论上就足够完成全部分析了，但是我们仍有一些包需要安装，比如基因id转换，看到ENSG可能你也不知道这是一个什么东西吧。 18 | 19 | ```shell 20 | #安装mygene 21 | pip install mygene 22 | ``` 23 | 24 | 25 | 26 | ## 2. R语言部分 27 | 28 | 对于R语言部分，实际上仅DESeq2这个包是需要的，所以我们就安装这个包就好了 29 | 30 | ```R 31 | if (!requireNamespace("BiocManager", quietly = TRUE)) 32 | install.packages("BiocManager") 33 | 34 | BiocManager::install("DESeq2") 35 | ``` 36 | 37 | -------------------------------------------------------------------------------- /docs/RNASEQ/rnaseq_2.assets/NHDF_heatmap.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/RNASEQ/rnaseq_2.assets/NHDF_heatmap.png -------------------------------------------------------------------------------- /docs/RNASEQ/rnaseq_2.assets/NHDF_volcano-1635819681870.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/RNASEQ/rnaseq_2.assets/NHDF_volcano-1635819681870.png -------------------------------------------------------------------------------- /docs/RNASEQ/rnaseq_2.assets/NHDF_volcano.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/RNASEQ/rnaseq_2.assets/NHDF_volcano.png -------------------------------------------------------------------------------- /docs/RNASEQ/rnaseq_2.assets/untitled.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/RNASEQ/rnaseq_2.assets/untitled.png -------------------------------------------------------------------------------- /docs/RNASEQ/rnaseq_2.assets/untitled1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/RNASEQ/rnaseq_2.assets/untitled1.png -------------------------------------------------------------------------------- /docs/RNASEQ/rnaseq_3.assets/NOD-like receptor signaling pathway.prerank.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/RNASEQ/rnaseq_3.assets/NOD-like receptor signaling pathway.prerank.png -------------------------------------------------------------------------------- /docs/RNASEQ/rnaseq_3.assets/untitled1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/RNASEQ/rnaseq_3.assets/untitled1.png -------------------------------------------------------------------------------- /docs/RNASEQUP/README.md: -------------------------------------------------------------------------------- 1 | # 分析1：RNA-seq上游分析 2 | 3 | -------------------------------------------------------------------------------- /docs/RNASEQUP/rnasequp_1.md: -------------------------------------------------------------------------------- 1 | # RNA-seq. 环境配置 2 | 3 | 我们在前面的教程已经完成了Linux的安装，目前我们的linux可以理解成一个空壳，里面大概什么都没有，于是我们需要安装一些RNA-seq上游分析所必要的包。 4 | 5 | ## 1. 环境要求 6 | 7 | ### 1.1 Linux环境 8 | 9 | 对于Linux环境，我们安装的系统为Ubuntu18.04，满足此系统即可。 10 | 11 | ### 1.2 Window环境 12 | 13 | 对于Windows操作系统，我们首先安装WSL即可 14 | 15 | ## 2. 软件安装 16 | 17 | ### 2.1 miniconda安装 18 | 19 | conda是一个开源的软件包管理系统和环境管理系统，可用于安装多个版本的软件包及其依赖关系，并且可以任意切换（安装conda的目的是为了防止软件版本与包之间互相干扰。）（e.g. 有些软件只能在Python3.6上运行，再新的版本会出现bug，这时就需要用conda来解决这个问题） 20 | 21 | 我们在这里选择安装miniconda，这是一个轻量级的conda框架，相较于Anaconda的臃肿而言，更加轻便 22 | 23 | #### 2.1.1 下载miniconda 24 | 25 | ```python 26 | # 在linux在使用以下命令下载miniconda 27 | wget-c https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-latest-Linux-x86_64.sh 28 | ``` 29 | 30 | #### 2.1.2 安装miniconda 31 | 32 | ```python 33 | # 安装刚刚下载的Miniconda，bash就是运行.sh文件的意思 34 | bash Miniconda3-latest-Linux-x86_64.sh 35 | ``` 36 | 37 | #### 2.1.3 激活conda 38 | 39 | ```python 40 | #将conda命令添加到环境变量中 41 | source .bashrc 42 | ``` 43 | 44 | #### *2.1.4 国内用户选择清华镜像 45 | 46 | ```python 47 | # 添加镜像 48 | conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free 49 | conda config-- add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge 50 | conda config-- add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/biocondacondaconfig--setshow_channel_urls yes 51 | ``` 52 | 53 | ### 2.2 RNA-seq上游依赖包安装 54 | 55 | 在这一步中，我们将安装转录组上游分析所需要用到的各个包，我将在最后的一小节中简单介绍一下每个包的用途 56 | 57 | #### 2.2.1 创建虚拟环境 58 | 59 | 为了不干扰Linux系统下其他包的运行，我们将创建一个全新的虚拟环境用来管理RNA-seq分析将用到的包，我们将此虚拟环境命名为rna 60 | 61 | ```python 62 | #创建名为rna的软件安装环境 63 | conda create -n rna python=3 64 | #查看当前conda环境 65 | conda info --envs 66 | #激活conda的rna环境 67 | source activate rna 68 | ``` 69 | 70 | 在每一次退出Linux重进后，都不要忘记了输入source activate rna激活环境 71 | 72 | #### 2.2.2 安装RNA-seq上游依赖包 73 | 74 | ```python 75 | #以下一行命令即可安装完成 76 | conda install -y fastp fastqc multiqc subread bedtools cutadapt trim-galore sra-tools 77 | ``` 78 | 79 | #### *2.2.3 RNA-seq上游依赖包简介 80 | 81 | | Package | Description | 82 | | ----------- | ------------------------------------------------------------ | 83 | | fastp | fastq文件质控软件，极其智能 | 84 | | fastqc | 高通量测序数据的高级质控工具 | 85 | | multiqc | 对测序数据进行质量评估（将fastqc生成的多个报告整合成一个文件） | 86 | | subread | 将reads比对到参考基因组上（速度极快） | 87 | | bedtools | 涵盖各种基因组计算所需要的工具 | 88 | | cutadapt | 从高通量测序数据中发现并去除衔接子序列，引物，poly-A尾巴和其他类型的不需要的序列 | 89 | | trim-galore | 是对FastQC和Cutadapt的包装。适用于所有高通量测序，包括RRBS(Reduced Representation Bisulfite-Seq ), Illumina、Nextera 和smallRNA测序平台的双端和单端数据 | 90 | | sra-tools | 来自NCBI的SRA工具包和SDK是工具和库的集合，这些工具和库用于使用INSDC序列读取档案中的数据 | 91 | 92 | -------------------------------------------------------------------------------- /docs/RNASEQUP/rnasequp_2.md: -------------------------------------------------------------------------------- 1 | # RNA-seq. 上游分析全教程 2 | 3 | ## 1. 测序数据下载 4 | 5 | 在本节中，我们将介绍如何从ncbi上下载原始测序数据SRA文件进行分析 6 | 7 | ### 1.1 SRA文件下载 8 | 9 | #### 1.1.1 单SRA文件下载 10 | 11 | 如果你只想分析一个SRA文件，并且已经知道了SRA号，那么我们可以在linux交互环境输入以下命令进行下载 12 | 13 | ```python 14 | #单SRA下载prefetch 15 | SRR10502962 16 | ``` 17 | 18 | #### 1.1.2 多SRA文件下载 19 | 20 | 如果你需要批量下载SRA文件，那么你需要得到一个带有多个SRA号的txt文件 21 | 22 | e.g. SRR_Acc_List.txt文件内容 23 | 24 | ```python 25 | SRR5113012 26 | SRR5113013 27 | SRR5113014 28 | ``` 29 | 30 | ```python 31 | #多SRA下载 32 | prefetch --option-file SRR_Acc_List.txt 33 | ``` 34 | 35 | ### 1.2 sra格式转fastq格式 36 | 37 | sra格式的文件一般是经过压缩的测序文件，我们需要转换成原始测序数据fastq格式。 38 | 39 | #### 1.2.1 单端测序文件转换 40 | 41 | ```python 42 | #转换当前目录下全部以.sra结尾的文件 43 | fastq-dump *.sra 44 | ``` 45 | 46 | #### 1.2.2 双端测序文件转换 47 | 48 | ```python 49 | #--split-files参数可以将其分解为两个fastq文件。 50 | fastq-dump --split-files *.sra 51 | ``` 52 | 53 | ## 2. 测序数据质控 54 | 55 | 为了检测我们测序数据的质量，我们常常需要生成一份质控报告进行直观的观察 56 | 57 | #### 2.1 检测数据质量 58 | 59 | #### 2.1.1 单份报告生成 60 | 61 | 在本小节，我们使用fastqc生成质控报告，在fastq文件目录下输入 62 | 63 | ```python 64 | #批量生成所有fastq文件的质控报告 65 | fastqc *.fastq 66 | ``` 67 | 68 | 等待运行结束后，在同目录下有着*_fastqc.html和*_fastqc.zip两个文件，我们可以打开对应的html文件查看该fastq数据的质量。报告的解读见文章 69 | 70 | #### 2.1.2 多份报告整合 71 | 72 | 通过上面的步骤，我们得到了每个fastq文件的质控报告，为了整体进行评估，我们使用multiqc整合报告结果 73 | 74 | ```python 75 | #整合质控报告结果 76 | multiqc *.zip 77 | ``` 78 | 79 | ### 2.2 测序数据过滤 80 | 81 | 由于我们得到的数据可能包括接头序列，引物，poly-A尾巴和其他类型的不需要的序列，为避免影响下面的分析，我们需要去除这些无关的测序数据。 82 | 83 | 在这里，相较于其他过滤工具而言，我们选择trim-galore 84 | 85 | #### 2.2.1 单端测序 86 | 87 | ```shell 88 | #新建clean文件夹存放测序结果 89 | mkdir clean 90 | #单端测序数据质量过滤 91 | trim_galore -q 20 \ #设定Phred quality score阈值 92 | --phred33 \ #选择-phred33或者-phred64，表示测序平台使用的Phred qualityscore 93 | --stringency 3 \ #设定可以忍受的前后adapter重叠的碱基数，默认为1（非常苛刻）。可以适度放宽，因为后一个adapter几乎不可能被测序仪读到 94 | --length 20 \ #设定输出reads长度阈值，小于设定值会被抛弃。 95 | -e 0.1 \ #容错率 96 | -o /home/seq/clean #输出结果 97 | /home/seq/SRR10502962_1.fastq #待处理文件 98 | ``` 99 | 100 | #### 2.2.2 双端测序 101 | 102 | ```shell 103 | #新建clean文件夹存放测序结果 104 | mkdir clean 105 | #双端测序数据质量过滤 106 | trim_galore -q 25 \ 107 | --phred33 \ 108 | --stringency 3 \ 109 | --length 36 \ 110 | -e 0.1 \ 111 | --paired #双端测序 112 | -o /home/seq/clean 113 | /home/seq/SRR10502962_1.fastq /home/seq/SRR10502962_2.fastq 114 | ``` 115 | 116 | #### 2.2.3 多组测序数据同时过滤 117 | 118 | 为了同时完成多组测序数据的同时过滤，我们在这里编写.sh脚本来在Linux系统下批量运行 119 | 120 | **config** 121 | 122 | ```shell 123 | mkdir clean 124 | cd clean 125 | ls /home/seq/*_1.fastq >1 126 | ls /home/seq/*_2.fastq >2 127 | paste 1 2 > config 128 | ``` 129 | 130 | **qc.sh** 131 | 132 | ```shell 133 | bin_trim_galore=trim_galore 134 | dir='/home/seq/clean' 135 | cat $1 |while read id 136 | do 137 | arr(${id}) 138 | fq1=${arr[0]} 139 | fq2=${arr[1]} 140 | $bin_trim_galore -q 25 --phred33 --phred33 --length 36 --stringency 3 --paired -o $dir $fq1 $fq2 141 | done 142 | ``` 143 | 144 | 运行qc.sh 145 | 146 | ```shell 147 | #config是传递进去的参数 148 | bash qc.sh config 149 | ``` 150 | 151 | ## 3. 比对到参考基因组 152 | 153 | 由于测序仪机器读长的限制，在构建文库的过程中首先需要将DNA片段化，测序得到的序列只是基因组上的部分序列。为了确定测序reads在基因组上的位置，需要将reads比对回参考基因组上，这个步骤叫做mapping 154 | 155 | ### 3.1 参考基因组文件下载 156 | 157 | ```shell 158 | #参考基因组下载(hg38)(subread) 159 | wget -O hg38.fa.gz http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz 160 | #解压 161 | gunzip hg38.fa.gz 162 | ``` 163 | 164 | ### 3.2 subread比对 165 | 166 | #### 3.2.1 构建索引 167 | 168 | subread有着极其快速的比对效率，这与其构建索引的预处理是不可分开的，所以我们用以下代码来构建索引 169 | 170 | ```shell 171 | cd /home/seq/index/hg38 172 | subread-buildindex -o hg38 hg38 173 | ``` 174 | 175 | #### 3.2.2 比对 176 | 177 | ```shell 178 | subread-align -t 0 \ #0代表RNA-seq，1代表DNA-seq 179 | -T5 \ #线程数 180 | -i hg38 \ #指定参考基因组的basename 181 | -r /home/seq/SRR10502962_1.fastq \ 182 | -R /home/seq/SRR10502962_2.fastq \ 183 | -o SRR10502962.bam #输出文件 184 | ``` 185 | 186 | ## 4. 统计基因counts数 187 | 188 | 在这里，我们仅介绍一个工具featureCounts。 189 | 190 | featuresCounts软件用于统计基因/转录本上mapping的reads数，也就是用于raw count定量。该软件不仅支持基因/转录本的定量，也支持exon，gene bodies，genomic bins，chromsomal locations等区间的定量 191 | 192 | ### 4.1 下载gtf基因组注释文件 193 | 194 | 撰写本教程时的最新版本为103，如有更新可以去官网看看再来下载 195 | 196 | ```shell 197 | #我们将基因组注释文件存放到refer文件夹中 198 | mkdir /home/seq/refer 199 | cd /home/seq/refer 200 | #下载 201 | wget http://ftp.ensembl.org/pub/release-103/gtf/homo_sapiens/Homo_sapiens.GRCh38.103.gtf.gz 202 | ``` 203 | 204 | ### 4.2 使用featureCounts进行定量分析 205 | 206 | ```shell 207 | #选择gtf路径 208 | gtf="/home/seq/refer/Homo_sapiens.GRCh38.103.gtf.gz" 209 | #featureCounts 定量分析 210 | featureCounts -T 5 \ #线程数 211 | -p \ #针对paired-end数据 212 | -t exon \ #跟-g一样的意思，其是默认将exon作为一个feature 213 | -g gene_id \ #从注释文件中提取Meta-features信息用于read count，默认是gene_id 214 | -a $gtf \ #输入GTF/GFF基因组注释文件 215 | -o all.id.txt \ #输出文件 216 | *.bam #待处理数据 217 | 218 | #去除多余信息，矩阵保存为counts.txt 219 | cat all.id.txt | cut-f1,7- > counts.txt 220 | 221 | ``` 222 | 223 | 到这里，我们就已经得到一个Counts矩阵了，后续的分析被称为转录组下游分析。将在后面的教程中继续介绍 224 | 225 | | Geneid | SRR10502962.bam | 226 | | --------------- | --------------- | 227 | | ENSG00000223972 | 1 | 228 | | ENSG00000227232 | 132 | 229 | | ENSG00000278267 | 3 | 230 | | ENSG00000243485 | 0 | 231 | 232 | -------------------------------------------------------------------------------- /docs/SCGLUE/README.md: -------------------------------------------------------------------------------- 1 | # 分析3：单细胞样本对齐 2 | 3 | 前面提到，我们在单细胞多组学中，会得到大量的细胞，每个细胞都有不同的状态。对于mofa而言，2018年的nature使用了scnmt技术，该技术可以同时测定一个细胞的ATAC与RNA情况，但在大部分情况下，我们只能从同一个样本中分别进行ATAC与RNA的测定，而不是对同一个细胞进行测定，但是从理论上来说，应该是有一些细胞是相像的，于是，如何把这些细胞对齐，成为了一个需要解决的问题。 -------------------------------------------------------------------------------- /docs/SCGLUE/scglue_1.md: -------------------------------------------------------------------------------- 1 | # 单细胞样本对齐： 环境配置 2 | 3 | 单细胞样本对齐，只需要在Python环境即可完成 4 | 5 | ## 1. Python部分 6 | 7 | 在Python部分，主要有以下几个包需要被安装：scglue，scanpy，mofapy2以及episcanpy 8 | 9 | ### 1.1 conda环境 10 | 11 | 在Python部分需要安装的包，可能与过往需要的包会起到冲突，所以我们新建一个conda环境 12 | 13 | ```python 14 | conda create -n rna python=3.6 15 | conda activate rna 16 | ``` 17 | 18 | 通过上述两行代码，我们现在进入了一个叫rna的python虚拟环境，这个环境是非常干净的，没有什么多余的包，所以我们在下一步中将依次安装需要的依赖 19 | 20 | ### 1.2 Jupyterlab安装 21 | 22 | 由于这是一个新的python环境，所以我们需要重新装一下jupyter 23 | 24 | ``` 25 | conda install -c conda-forge jupyterlab 26 | ``` 27 | 28 | ### 1.3 scanpy安装 29 | 30 | 接下来进入正题，我们安装单细胞处理所必需的包-scanpy 31 | 32 | ```python 33 | conda install seaborn scikit-learn statsmodels numba pytables 34 | conda install -c conda-forge python-igraph leidenalg 35 | pip install scanpy 36 | ``` 37 | 38 | ### 1.4 scglue安装 39 | 40 | 安装完scanpy后，我们安装一下用于单细胞配对的包scglue 41 | 42 | ```shell 43 | conda install -c defaults -c pytorch -c bioconda -c conda-forge -c scglue scglue --yes 44 | ``` 45 | 46 | ### 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-------------------------------------------------------------------------------- /docs/SCGLUE/scglue_3.md: -------------------------------------------------------------------------------- 1 | # 单细胞样本对齐： 模型准备 2 | 3 | 我们在准备好需要对齐的两个单细胞数据后，便需要开始构建模型了 4 | 5 | ## 1. 数据清洗 6 | 7 | ### 1.1 导入包 8 | 9 | ```python 10 | import anndata 11 | import networkx as nx 12 | import scanpy as sc 13 | import scglue 14 | import pandas as pd 15 | import numpy as np 16 | from matplotlib import rcParams 17 | ``` 18 | 19 | ### 1.2 导入数据 20 | 21 | ``` 22 | rna = anndata.read_h5ad("/content/GSE174367rna_61472.h5ad") 23 | rna 24 | ``` 25 | 26 | > AnnData object with n_obs × n_vars = 61472 × 58721 obs: 'cell_type', 'Sample.ID', 'Batch', 'Age', 'Sex', 'PMI', 'Tangle.Stage', 'Plaque.Stage', 'Diagnosis', 'RIN' var: 'gene_ids', 'feature_types', 'genome' 27 | 28 | ``` 29 | atac = anndata.read_h5ad("/content/GSE174367atac_61472.h5ad") 30 | atac 31 | ``` 32 | 33 | > AnnData object with n_obs × n_vars = 61472 × 217707 obs: 'cell_type', 'ran', 'Sample.ID', 'Batch', 'Age', 'Sex', 'PMI', 'Tangle.Stage', 'Plaque.Stage', 'Diagnosis', 'RIN' var: 'feature_types', 'genome', 'chrom', 'chromStart', 'chromEnd', 'n_counts' 34 | 35 | ### 1.3 rna数据处理 36 | 37 | ```python 38 | rna.layers["raw"] = rna.X.copy() 39 | rna.layers["raw"] = rna.X.copy() 40 | rna.var_names_make_unique() 41 | sc.pp.filter_genes(rna, min_cells=3) 42 | sc.pp.normalize_total(rna, target_sum=1e4) 43 | sc.pp.log1p(rna) 44 | sc.pp.highly_variable_genes(rna, min_mean=0.0125, max_mean=3, min_disp=0.5) 45 | ``` 46 | 47 | ### 1.4 rna数据可视化 48 | 49 | ```python 50 | sc.tl.pca(rna, n_comps=100, svd_solver="auto") 51 | sc.pp.neighbors(rna, metric="cosine") 52 | sc.tl.umap(rna) 53 | sc.pl.umap(rna, color="cell_type") 54 | rna.write_h5ad('GSE174367rna_61472_process.h5ad',compression="gzip") 55 | ``` 56 | 57 | ![do](https://raw.githubusercontent.com/Starlitnightly/bioinformatic_galaxy/master/img/do.png) 58 | 59 | ### 1.5 atac数据处理 60 | 61 | ```python 62 | scglue.data.lsi(atac, n_components=100, n_iter=15) 63 | sc.pp.neighbors(atac, use_rep="X_lsi", metric="cosine") 64 | sc.tl.umap(atac) 65 | sc.pl.umap(atac, color="cell_type") 66 | atac.write_h5ad('GSE174367atac_61472_process.h5ad',compression="gzip") 67 | ``` 68 | 69 | ![do2](C:\Users\FernandoZeng\Desktop\biobook\SCGLUE\scglue_3.assets\do2.png) 70 | 71 | ### 1.6 Construct prior regulatory graph 72 | 73 | 因为我们得到的rna只有基因的名字，我们还需要知道他们在染色体上的位置，所以在这里，我们用scglue中标注基因的方法 74 | 75 | #### 1.6.1 下载gtf标注文件 76 | 77 | ```shell 78 | #人源 79 | wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_31/gencode.v31.chr_patch_hapl_scaff.annotation.gtf.gz 80 | #鼠源 81 | wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M25/gencode.vM25.chr_patch_hapl_scaff.annotation.gtf.gz 82 | ``` 83 | 84 | #### 1.6.2 标注 85 | 86 | ```python 87 | scglue.data.get_gene_annotation( 88 | rna, gtf="gencode.v31.chr_patch_hapl_scaff.annotation.gtf.gz", 89 | gtf_by="gene_name" 90 | ) 91 | rna.var.loc[:, ["chrom", "chromStart", "chromEnd"]].head() 92 | ``` 93 | 94 | ### 1.6.3 去除空标注基因 95 | 96 | 有一些基因并没有在gtf中找到对应的染色体位置，我们需要把这一部分基因去掉 97 | 98 | ```python 99 | rna.var['dell']=np.zeros(len(rna.var)) 100 | a=rna.var[~rna.var['chromStart'].isnull()].index 101 | rna.var.loc[a,'dell']=1 102 | rna1=rna[:,rna.var.dell==1] 103 | rna1.var = rna1.var.astype({"chromStart": int, "chromEnd": int}) 104 | rna1 105 | ``` 106 | 107 | 到此，rna与atac数据的预处理已经完成。 108 | 109 | ## 2. 多组学网络构建 110 | 111 | 根据组学层的先验关系，我们用scglue构建分子层之间的关系 112 | 113 | ### 2.1 构建图网络 114 | 115 | ```python 116 | graph = scglue.genomics.rna_anchored_prior_graph(rna1, atac) 117 | graph.number_of_nodes(), graph.number_of_edges() 118 | ``` 119 | 120 | > 100%|██████████| 35363/35363 [00:04<00:00, 7923.69it/s] 121 | > 122 | > (253070, 613256) 123 | 124 | ### 2.2 检查节点 125 | 126 | ```python 127 | # Graph node covers all omic features 128 | all(graph.has_node(gene) for gene in rna1.var_names), \ 129 | all(graph.has_node(peak) for peak in atac.var_names) 130 | ``` 131 | 132 | > (True, True) 133 | 134 | ```python 135 | # Edge attributes contain weights and signs 136 | for _, e in zip(range(5), graph.edges): 137 | print(f"{e}: {graph.edges[e]}") 138 | ``` 139 | 140 | > ('AL669831.3', 'chr1:629708-630559', 0): {'dist': 0, 'weight': 1.0, 'sign': 1} ('AL669831.3', 'chr1:631640-631948', 0): {'dist': 0, 'weight': 1.0, 'sign': 1} ('AL669831.3', 'chr1:632511-633105', 0): {'dist': 0, 'weight': 1.0, 'sign': 1} ('AL669831.3', 'chr1:633740-634682', 0): {'dist': 0, 'weight': 1.0, 'sign': 1} ('AL669831.3', 'chr1:778234-779324', 0): {'dist': 0, 'weight': 1.0, 'sign': 1} 141 | 142 | ### 2.3 保存图网络 143 | 144 | ```python 145 | rna1.write("rna_preprocessed.h5ad", compression="gzip") 146 | atac.write("atac_preprocessed.h5ad", compression="gzip") 147 | nx.write_graphml(graph, "prior.graphml.gz") 148 | ``` 149 | 150 | 到这里，模型的预处理就算是完成了，更多的可以参照scglue的官方说明文档。 -------------------------------------------------------------------------------- /docs/SCGLUE/scglue_4.md: -------------------------------------------------------------------------------- 1 | # 单细胞样本对齐： 模型训练 2 | 3 | 在本小节，我们就要开始对齐单细胞了 4 | 5 | ## 1. 数据准备 6 | 7 | ### 1.1 导入包 8 | 9 | ```python 10 | import anndata 11 | import itertools 12 | import networkx as nx 13 | import numpy as np 14 | import pandas as pd 15 | import scanpy as sc 16 | import scglue 17 | from matplotlib import rcParams 18 | ``` 19 | 20 | ### 1.2 导入数据 21 | 22 | ```python 23 | rna = anndata.read_h5ad("rna_preprocessed.h5ad") 24 | atac = anndata.read_h5ad("atac_preprocessed.h5ad") 25 | graph = nx.read_graphml("prior.graphml.gz") 26 | ``` 27 | 28 | ## 2 模型训练 29 | 30 | ### 2.1 设置模型参数 31 | 32 | ```python 33 | scglue.plot.set_publication_params() 34 | rcParams["figure.figsize"] = (4, 4) 35 | 36 | scglue.models.configure_dataset( 37 | rna, "NB", use_highly_variable=True, 38 | use_layer="raw", use_rep="X_pca" 39 | ) 40 | 41 | scglue.models.configure_dataset( 42 | atac, "NB", use_highly_variable=True, 43 | use_rep="X_lsi" 44 | ) 45 | 46 | graph = graph.subgraph(itertools.chain( 47 | rna.var.query("highly_variable").index, 48 | atac.var.query("highly_variable").index 49 | )) 50 | ``` 51 | 52 | ### 2.2 模型训练 53 | 54 | ```python 55 | 56 | glue = scglue.models.SCGLUEModel( 57 | {"rna": rna, "atac": atac}, sorted(graph.nodes), 58 | random_seed=0 59 | ) 60 | 61 | glue.compile() 62 | 63 | glue.fit( 64 | {"rna": rna, "atac": atac}, 65 | graph, edge_weight="weight", edge_sign="sign", 66 | directory="glue" 67 | ) 68 | ``` 69 | 70 | > ``` 71 | > [INFO] SCGLUEModel: Setting `graph_batch_size` = 28027 72 | > [INFO] SCGLUEModel: Setting `align_burnin` = 10 73 | > [INFO] SCGLUEModel: Setting `max_epochs` = 38 74 | > [INFO] SCGLUEModel: Setting `patience` = 5 75 | > [INFO] SCGLUEModel: Setting `reduce_lr_patience` = 3 76 | > [INFO] SCGLUETrainer: Using training directory: "glue" 77 | > [INFO] SCGLUETrainer: [Epoch 10] train={'g_nll': 0.383, 'g_kl': 0.004, 'g_elbo': 0.387, 'x_rna_nll': 0.246, 'x_rna_kl': 0.009, 'x_rna_elbo': 0.254, 'x_atac_nll': 0.029, 'x_atac_kl': 0.0, 'x_atac_elbo': 0.03, 'dsc_loss': 0.684, 'gen_loss': 0.286}, val={'g_nll': 0.425, 'g_kl': 0.004, 'g_elbo': 0.429, 'x_rna_nll': 0.246, 'x_rna_kl': 0.009, 'x_rna_elbo': 0.254, 'x_atac_nll': 0.029, 'x_atac_kl': 0.0, 'x_atac_elbo': 0.029, 'dsc_loss': 0.692, 'gen_loss': 0.287}, 24.2s elapsed 78 | > [INFO] SCGLUETrainer: [Epoch 20] train={'g_nll': 0.338, 'g_kl': 0.004, 'g_elbo': 0.342, 'x_rna_nll': 0.244, 'x_rna_kl': 0.009, 'x_rna_elbo': 0.253, 'x_atac_nll': 0.029, 'x_atac_kl': 0.0, 'x_atac_elbo': 0.029, 'dsc_loss': 0.688, 'gen_loss': 0.282}, val={'g_nll': 0.442, 'g_kl': 0.004, 'g_elbo': 0.447, 'x_rna_nll': 0.245, 'x_rna_kl': 0.008, 'x_rna_elbo': 0.253, 'x_atac_nll': 0.029, 'x_atac_kl': 0.0, 'x_atac_elbo': 0.029, 'dsc_loss': 0.691, 'gen_loss': 0.286}, 24.3s elapsed 79 | > Epoch 21: reducing learning rate of group 0 to 2.0000e-04. 80 | > Epoch 21: reducing learning rate of group 0 to 2.0000e-04. 81 | > 2021-10-22 16:24:42,283 ignite.handlers.early_stopping.EarlyStopping INFO: EarlyStopping: Stop training 82 | > [INFO] EarlyStopping: Retoring checkpoint "22"... 83 | > ``` 84 | 85 | ### 2.3 保存模型 86 | 87 | ```python 88 | glue.save("glue/final.dill") 89 | ``` -------------------------------------------------------------------------------- /docs/SCGLUE/scglue_5.md: -------------------------------------------------------------------------------- 1 | # 单细胞样本对齐： 对齐细胞 2 | 3 | 在上一小节，我们已经获得了训练好的对齐模型，在本小节中，我们将根据模型参数配对细胞。 4 | 5 | ## 1. 数据准备 6 | 7 | ### 1.1 导入包 8 | 9 | ```python 10 | import anndata 11 | import networkx as nx 12 | import scanpy as sc 13 | import scglue 14 | import numpy as np 15 | import pandas as pd 16 | from matplotlib import rcParams 17 | ``` 18 | 19 | ### 1.2 导入数据 20 | 21 | ```python 22 | rna = anndata.read_h5ad("rna_preprocessed.h5ad") 23 | atac = anndata.read_h5ad("atac_preprocessed.h5ad") 24 | glue = scglue.models.load_model("final.dill") 25 | ``` 26 | 27 | ## 2. 整合模型 28 | 29 | ### 2.1 数据预处理 30 | 31 | ```python 32 | rna.obs['domain']='scRNA-seq' 33 | atac.obs['domain']='scATAC-seq' 34 | rna.obsm["X_glue"] = glue.encode_data("rna", rna) 35 | atac.obsm["X_glue"] = glue.encode_data("atac", atac) 36 | ``` 37 | 38 | ### 2.2 导出细胞参数(特征向量) 39 | 40 | ```python 41 | rna_loc=pd.DataFrame(rna.obsm['X_glue'], index=rna.obs.index) 42 | atac_loc=pd.DataFrame(atac.obsm['X_glue'], index=atac.obs.index) 43 | ``` 44 | 45 | ### 2.3 配对细胞 46 | 47 | ```python 48 | len1=(len(rna_loc)//5000)+1 49 | xl=[] 50 | for j in range(len1): 51 | 52 | c=pd.DataFrame() 53 | for i in range(len1): 54 | t1=rna_loc.iloc[5000*(i):5000*(i+1)] 55 | t2=atac_loc.iloc[5000*(j):5000*(j+1)] 56 | a=np.corrcoef(t1,t2)[len(t1):,0:len(t1)] 57 | b=pd.DataFrame(a,index=t2.index,columns=t1.index) 58 | 59 | c=pd.concat([c,b],axis=1) 60 | del t1 61 | del t2 62 | del a 63 | del b 64 | for i in range(len(c)): 65 | xl.append(c.columns[np.where(c.iloc[i]==c.iloc[i].max())[0]].values[0]) 66 | del c 67 | print('Now epoch is {}'.format(j)) 68 | res=pd.DataFrame(index=atac_loc.index) 69 | res['pair']=xl 70 | res.to_csv('pair_res.csv') 71 | ``` 72 | 73 | ## 3. 过滤重复细胞 74 | 75 | ### 3.1 导入配对数据 76 | 77 | ```python 78 | pair=pd.read_csv('pair_res.csv') 79 | pair.columns=['scATAC','scRNA'] 80 | ``` 81 | 82 | ### 3.2 过滤重复细胞 83 | 84 | ```python 85 | rna_only_pair=list(set(pair['scRNA'])) 86 | atac_only_pair=[] 87 | for i in rna_only_pair: 88 | atac_only_pair.append(pair[pair['scRNA']==i]['scATAC'].iloc[0]) 89 | new_pair=pd.DataFrame() 90 | new_pair['scRNA']=rna_only_pair 91 | new_pair['scATAC']=atac_only_pair 92 | new_name=[] 93 | for i in range(len(new_pair)): 94 | k='cell_{0}'.format(i) 95 | new_name.append(k) 96 | new_pair['sample']=new_name 97 | new_pair.to_csv('new_pair.csv') 98 | ``` 99 | 100 | ### 3.3 提取scRNA-seq的配对细胞 101 | 102 | ```python 103 | delli=[] 104 | for i in rna.obs.index: 105 | if i in rna_only_pair: 106 | delli.append('tr') 107 | else: 108 | delli.append('fa') 109 | rna.obs['delli']=delli 110 | rna_pair=rna[rna.obs['delli']=='tr'] 111 | rna_pair.write_h5ad('rna_pair.h5ad',compression="gzip") 112 | ``` 113 | 114 | > View of AnnData object with n_obs × n_vars = 23770 × 35363 115 | 116 | ### 3.4 提取scATAC-seq的配对细胞 117 | 118 | ```python 119 | delli=[] 120 | for i in atac.obs.index: 121 | if i in atac_only_pair: 122 | delli.append('tr') 123 | else: 124 | delli.append('fa') 125 | atac.obs['delli']=delli 126 | atac_pair=atac[atac.obs['delli']=='tr'] 127 | atac_pair.write_h5ad('atac_pair.h5ad',compression="gzip") 128 | ``` 129 | 130 | ### 3.5 scRNA-seq与scATAC-seq的细胞类型配对 131 | 132 | 由于我们配对的细胞仅仅是特征向量相似，而不是真正意义上的相似，可能存在scRNA-seq的男性细胞与scATAC-seq的女性细胞比较相似的情况，为了避免性别与诊断出错，我们对配对后的细胞进行进一步配对 133 | 134 | #### 3.5.1 男女性别平衡 135 | 136 | 由于女性样本比男性样本要多两个，我们随机去除两个女性样本使得男：女=1：1 137 | 138 | ```python 139 | rna_sex=rna_pair[(rna_pair.obs['Sample.ID']!='Sample-22') & (rna_pair.obs['Sample.ID']!='Sample-27')] 140 | rna_sex.obs.index=rna_pair.obs[(rna_pair.obs['Sample.ID']!='Sample-22') & (rna_pair.obs['Sample.ID']!='Sample-27')].index.values 141 | for i in list(set(rna_sex.obs['cell_type'])): 142 | print(i,len(rna_sex.obs.loc[rna_sex.obs['cell_type']==i])/len(rna_sex.obs)) 143 | ``` 144 | 145 | > ODC 0.622879391860573 146 | > 147 | > MG 0.0530731435987763 148 | > 149 | > INH 0.0965977565588208 150 | > 151 | > EX 0.11754890145545564 152 | > 153 | > OPC 0.03680355984054881 154 | > 155 | > ASC 0.06711782701399834 156 | > 157 | > PER.END 0.0059794196718271995 158 | 159 | ```python 160 | atac_sex=atac_pair[(atac_pair.obs['Sample.ID']!='Sample-22') & (atac_pair.obs['Sample.ID']!='Sample-27')] 161 | atac_sex.obs.index=atac_pair.obs[(atac_pair.obs['Sample.ID']!='Sample-22') & (atac_pair.obs['Sample.ID']!='Sample-27')].index 162 | for i in list(set(atac_sex.obs['cell_type'])): 163 | print(i,len(atac_sex.obs.loc[atac_sex.obs['cell_type']==i])/len(atac_sex.obs)) 164 | ``` 165 | 166 | > ODC 0.6223660050712599 167 | > 168 | > MG 0.05993704642825916 169 | > 170 | > INH 0.10037597272011892 171 | > 172 | > EX 0.10457287750284165 173 | > 174 | > OPC 0.03733496546297106 175 | > 176 | > ASC 0.06933636443123196 177 | > 178 | > PER.END 0.006076768383317304 179 | 180 | ``` 181 | ret_sex= list(set(rna_sex.obs.index).intersection(atac_sex.obs.index)) 182 | len(ret_sex) 183 | ``` 184 | 185 | #### 3.5.2 细胞类型配对-性别 186 | 187 | ``` 188 | rna_sex_F=rna_sex[rna_sex.obs['Sex']=='F'] 189 | rna_sex_F.obs.index=rna_sex.obs[rna_sex.obs['Sex']=='F'].index 190 | atac_sex_F=atac_sex[atac_sex.obs['Sex']=='F'] 191 | atac_sex_F.obs.index=atac_sex.obs[atac_sex.obs['Sex']=='F'].index 192 | ret_F=list(set(rna_sex_F.obs.index).intersection(atac_sex_F.obs.index)) 193 | 194 | rna_sex_M=rna_sex[rna_sex.obs['Sex']=='M'] 195 | rna_sex_M.obs.index=rna_sex.obs[rna_sex.obs['Sex']=='M'].index 196 | atac_sex_M=atac_sex[atac_sex.obs['Sex']=='M'] 197 | atac_sex_M.obs.index=atac_sex.obs[atac_sex.obs['Sex']=='M'].index 198 | ret_M=list(set(rna_sex_M.obs.index).intersection(atac_sex_M.obs.index)) 199 | 200 | ``` 201 | 202 | #### 3.5.3 细胞类型配对-诊断 203 | 204 | ``` 205 | rna_sex_ad=rna_sex[ret_M+ret_F][rna_sex[ret_M+ret_F].obs['Diagnosis']=='AD'] 206 | atac_sex_ad=atac_sex[ret_M+ret_F][atac_sex[ret_M+ret_F].obs['Diagnosis']=='AD'] 207 | ret_ad=list(set(rna_sex_ad.obs.index).intersection(atac_sex_ad.obs.index)) 208 | 209 | rna_sex_ctrl=rna_sex[ret_M+ret_F][rna_sex[ret_M+ret_F].obs['Diagnosis']=='Control'] 210 | atac_sex_ctrl=atac_sex[ret_M+ret_F][atac_sex[ret_M+ret_F].obs['Diagnosis']=='Control'] 211 | ret_ctrl=list(set(rna_sex_ctrl.obs.index).intersection(atac_sex_ctrl.obs.index)) 212 | ``` 213 | 214 | ### 3.6 保存新配对列表 215 | 216 | ```python 217 | pair=pd.read_csv('new_pair.csv') 218 | pair.set_index(pair.columns[0],inplace=True) 219 | pair.set_index(pair.columns[2],inplace=True) 220 | #pair.columns=['scATAC','scRNA'] 221 | pair.loc[ret_ad+ret_ctrl].to_csv('mofa_pre_pair.csv') 222 | !cp /content/mofa_pre_pair.csv /content/drive/MyDrive/mofa_gse174367 223 | ``` 224 | 225 | -------------------------------------------------------------------------------- /docs/SUMMARY.md: -------------------------------------------------------------------------------- 1 | # Summary 2 | 3 | * [Bioinformatics Tutorial](README.md) 4 | * [目录](README.md) 5 | * [配置：基础环境配置](CONFIG/README.md) 6 | * [配置1：Python环境](CONFIG/config_1.md) 7 | * [配置2：Linux环境](CONFIG/config_2.md) 8 | * [配置3：R语言环境](CONFIG/config_3.md) 9 | * [分析1：RNA-seq上游分析](RNASEQUP/README.md) 10 | * [1-1. 环境配置](RNASEQUP/rnasequp_1.md) 11 | * [1-2. 上游分析全教程](RNASEQUP/rnasequp_2.md) 12 | * [分析2：RNA-seq下游分析](RNASEQ/README.md) 13 | * [2-1. 环境配置](RNASEQ/rnaseq_1.md) 14 | * [2-2. 差异表达基因(DEG)分析](RNASEQ/rnaseq_2.md) 15 | * [2-3. 通路富集分析(GSEA)](RNASEQ/rnaseq_3.md) 16 | * [分析3：单细胞样本对齐](SCGLUE/README.md) 17 | * [3-1. 环境配置](SCGLUE/scglue_1.md) 18 | * [3-2. 数据预处理](SCGLUE/scglue_2.md) 19 | * [3-3. 模型准备](SCGLUE/scglue_3.md) 20 | * [3-4. 模型训练](SCGLUE/scglue_4.md) 21 | * [3-5. 对齐细胞](SCGLUE/scglue_5.md) 22 | * [分析4：MOFA单细胞多组学因子分析](MOFA/README.md) 23 | * [4-1. 环境配置](MOFA/mofa_1.md) 24 | * [4-2. 单细胞样本对齐](MOFA/mofa_2.md) 25 | * [4-3. scRNA-seq数据处理](MOFA/mofa_3.md) 26 | * [4-4. scATAC-seq数据处理](MOFA/mofa_4.md) 27 | * [4-5. MOFA模型构建](MOFA/mofa_5.md) 28 | * [4-6. MOFA下游分析-R语言环节](MOFA/mofa_6.md) 29 | * [4-7. MOFA下游分析-Python环节](MOFA/mofa_7.md) 30 | * [分析5：TMT蛋白组学分析](TMT/README.md) 31 | * [5-1. 环境配置](TMT/tmt_1.md) 32 | * [5-2. TMT蛋白组学上游分析](TMT/tmt_2.md) 33 | * [5-3. TMT蛋白组学下游分析](TMT/tmt_3.md) 34 | * [分析6：多组学分析](MultiOMIC/README.md) 35 | * [6-1. 数据准备](MultiOMIC/book-1.md) 36 | * [6-2. GLUE多组学整合](MultiOMIC/book-2.md) 37 | * [6-3. 细胞亚群分析](MultiOMIC/book-3.md) 38 | * [6-4. 差异表达分析](MultiOMIC/book-4.md) 39 | * [6-5. 细胞通讯分析](MultiOMIC/book-5.md) 40 | * [6-6. 动态调控分析](MultiOMIC/book-6.md) 41 | * [6-7. 转录因子网络](MultiOMIC/book-7.md) 42 | * [绘图：Python可视化](PLOT/README.md) 43 | * [1-1. 火山图](PLOT/plot_1.md) 44 | * [1-2. 箱线图](PLOT/plot_2.md) 45 | * [1-3. 热图(聚类)](PLOT/plot_3.md) 46 | 47 | -------------------------------------------------------------------------------- /docs/TMT/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/.DS_Store -------------------------------------------------------------------------------- /docs/TMT/README.md: -------------------------------------------------------------------------------- 1 | # 分析5：TMT蛋白组学分析 2 | 3 | -------------------------------------------------------------------------------- /docs/TMT/tmt_1.assets/image-20220126012940259-6120196.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_1.assets/image-20220126012940259-6120196.png -------------------------------------------------------------------------------- /docs/TMT/tmt_1.assets/image-20220126012940259.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_1.assets/image-20220126012940259.png -------------------------------------------------------------------------------- /docs/TMT/tmt_1.md: -------------------------------------------------------------------------------- 1 | # TMT蛋白组学. 环境配置 2 | 3 | 对于TMT串联质谱分析，我们这里使用MaxQuant完成对raw文件的处理，同时，我们可以考虑使用服务器来完成处理。这意味着，环境我们既可以选择windows，也可以选择linux，我们首先介绍一下windows环境如何配置 4 | 5 | ## 1. Windows环境配置 6 | 7 | 在Windows环境下，我们环境配置分为以下几个步骤： 8 | 9 | ### 1.1 .Net环境安装 10 | 11 | 在本教程中，我们使用的MaxQuant对应的版本为1.6.7.10，这个版本要求的.Net框架为2.0。于是，我们从微软官网下载：https://dotnet.microsoft.com/en-us/download/dotnet/2.1 12 | 13 | ![image-20220126012940259](tmt_1.assets/image-20220126012940259-6120196.png) 14 | 15 | ### 1.2 MaxQuant安装 16 | 17 | 在windows下，我们只需要将MaxQuant下载下来即可直接使用（装完.net框架后） 18 | 19 | 下载地址：https://maxquant.net/ 20 | 21 | ## 2. Linux环境下的安装 22 | 23 | 由于我们有时候会不想用自己的电脑跑，更希望在服务器上运行。在这里，我们使用的是Ubuntu18.04来对蛋白组学数据进行处理。 24 | 25 | ### 2.1 .Net环境安装 26 | 27 | 使用 APT 进行安装可通过几个命令来完成。 安装 .NET 之前，请运行以下命令，将 Microsoft 包签名密钥添加到受信任密钥列表，并添加包存储库。 28 | 29 | 打开终端并运行以下命令： 30 | 31 | Bash 32 | 33 | ```bash 34 | wget https://packages.microsoft.com/config/ubuntu/18.04/packages-microsoft-prod.deb -O packages-microsoft-prod.deb 35 | sudo dpkg -i packages-microsoft-prod.deb 36 | rm packages-microsoft-prod.deb 37 | ``` 38 | 39 | ### 2.2 安装 SDK 40 | 41 | .NET Core SDK 使你可以通过 .NET Core 开发应用。 如果安装 .NET Core SDK，则无需安装相应的运行时。 若要安装 .NET Core SDK，请运行以下命令： 42 | 43 | Bash 44 | 45 | ```bash 46 | sudo apt-get update; \ 47 | sudo apt-get install -y apt-transport-https && \ 48 | sudo apt-get update && \ 49 | sudo apt-get install -y dotnet-sdk-2.1 50 | ``` 51 | 52 | ### 2.3 验证安装 53 | 54 | 我们在终端输入 55 | 56 | ```bash 57 | sudo dotnet --version 58 | ``` 59 | 60 | 如果显示正确的版本号，那么就意味着安装成功了。 -------------------------------------------------------------------------------- /docs/TMT/tmt_2.assets/image-20220126013900416-6120205.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_2.assets/image-20220126013900416-6120205.png -------------------------------------------------------------------------------- /docs/TMT/tmt_2.assets/image-20220126013900416.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_2.assets/image-20220126013900416.png -------------------------------------------------------------------------------- /docs/TMT/tmt_2.assets/image-20220126014204530.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_2.assets/image-20220126014204530.png -------------------------------------------------------------------------------- /docs/TMT/tmt_2.assets/image-20220126014423239.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_2.assets/image-20220126014423239.png -------------------------------------------------------------------------------- /docs/TMT/tmt_2.assets/image-20220126014658979.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_2.assets/image-20220126014658979.png -------------------------------------------------------------------------------- /docs/TMT/tmt_2.assets/image-20220126014844199.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_2.assets/image-20220126014844199.png -------------------------------------------------------------------------------- /docs/TMT/tmt_2.assets/image-20220126014944222.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_2.assets/image-20220126014944222.png -------------------------------------------------------------------------------- /docs/TMT/tmt_2.assets/image-20220126015312481.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_2.assets/image-20220126015312481.png -------------------------------------------------------------------------------- /docs/TMT/tmt_2.assets/image-20220126015354523.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_2.assets/image-20220126015354523.png -------------------------------------------------------------------------------- /docs/TMT/tmt_2.assets/image-20220126015849066.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_2.assets/image-20220126015849066.png -------------------------------------------------------------------------------- /docs/TMT/tmt_2.md: -------------------------------------------------------------------------------- 1 | # TMT蛋白组学：上游分析 2 | 3 | 顾名思义，上游分析即从质谱raw文件中得到蛋白定量的矩阵，无论是windows还是linux，都需要在windows的软件内设置以下的参数 4 | 5 | ## 1. 参数设置 6 | 7 | 对于我没提到的参数，都保持默认即可 8 | 9 | ### 1.1 原始数据窗格 10 | 11 | - Set experiment：对每一个样本设定实验内容，每个实验作为一个单独的输出 12 | - Set reference channels：这个是TMT串联质谱必须要设置的参数，使得标记可以被分离 13 | - Set parameter group：如果你的实验有多个组别可以设置，但一般多组别会分开跑 14 | 15 | ![image-20220126013900416](tmt_2.assets/image-20220126013900416-6120205.png) 16 | 17 | ### 1.2 组特定参数窗格 18 | 19 | #### 1.2.1 Type 20 | 21 | - 对于TMT6plex，我们选择**Reporter ion MS2**作为类型、 22 | - Isobaric：我们选择**6plex TMT** 23 | 24 | ![image-20220126014204530](tmt_2.assets/image-20220126014204530.png) 25 | 26 | #### 1.2.2 Modification 27 | 28 | - Variable modifications: 选择Oxidation（M），Acetyl（Protein N-term），Deamidation（NQ） 29 | - Fixed modification：选择Carbamidomethyl（C） 30 | 31 | ![image-20220126014423239](tmt_2.assets/image-20220126014423239.png) 32 | 33 | #### 1.2.3 Digestion 34 | 35 | - Digestion mode：Specific 36 | - Enzyme：Trypsin/P（实验使拿什么酶消化就选什么） 37 | - Max. missed：设置成2即可 38 | 39 | ![image-20220126014658979](tmt_2.assets/image-20220126014658979.png) 40 | 41 | #### 1.2.4 Label-free quantification 42 | 43 | 设置成None 44 | 45 | ![image-20220126014844199](tmt_2.assets/image-20220126014844199.png) 46 | 47 | ### 1.3 全局参数窗格 48 | 49 | #### 1.3.1 Sequence 50 | 51 | - Fasta files：选择你要搜的蛋白质库，在这里，我们选择从Uniprot上的Mus全蛋白库 52 | - Min. peptide length: 设置成7，太低会出现误差 53 | - Max. peptide mass [Da]: 设置成6000 54 | 55 | ![image-20220126014944222](tmt_2.assets/image-20220126014944222.png) 56 | 57 | #### 1.3.2 Protein quantification 58 | 59 | select Use only unmodified peptides and a list of modifications such as Oxidation (M), Acetyl (Protein N-term) and Deamidation. 60 | 61 | ![image-20220126015312481](tmt_2.assets/image-20220126015312481.png) 62 | 63 | #### 1.3.3 MS/MS analyzer 64 | 65 | - a. FTMS MS/MS match tolerance: 0.05 Da 66 | - b. ITMS MS/MS match tolerance: 0.6 Da 67 | 68 | ![image-20220126015354523](tmt_2.assets/image-20220126015354523.png) 69 | 70 | ## 2. 搜库（获取表达矩阵） 71 | 72 | 在上述环节，我们已经设置好了所有参数，那么我们首先点击**文件->保存参数**，得到一个mqpar.xml文件 73 | 74 | ### 2.1 Windows 75 | 76 | 我们在左下角设置好处理器的数量，一般设置的比CPU数少1会比较稳定，不会闪退，效率也会最高，然后直接开始即可 77 | 78 | 开始后性能窗格会显示当前进度，可以点击Details按钮左边的勾查看，也可以点击左上角show all activity查看 79 | 80 | ![image-20220126015849066](tmt_2.assets/image-20220126015849066.png) 81 | 82 | ### 2.2 Linux 83 | 84 | 我们将MaxQuant拷贝到Linux上，首先进入mpqar.xml所在的文件夹，在终端输入 85 | 86 | ```bash 87 | sudo nohup dotnet /home/MaxQuant/bin/MaxQuantCmd.exe mqpar.xml 88 | ``` 89 | 90 | 即可开始运行，其中`/home/MaxQuant/bin/MaxQuantCmd.exe`为我们拷贝的路径，等待时间24-48小时不等 91 | 92 | -------------------------------------------------------------------------------- /docs/TMT/tmt_3.assets/image-20220126021021714.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_3.assets/image-20220126021021714.png -------------------------------------------------------------------------------- /docs/TMT/tmt_3.assets/image-20220126022018085.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_3.assets/image-20220126022018085.png -------------------------------------------------------------------------------- /docs/TMT/tmt_3.assets/image-20220126022234665.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_3.assets/image-20220126022234665.png -------------------------------------------------------------------------------- /docs/TMT/tmt_3.assets/image-20220126022304695.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_3.assets/image-20220126022304695.png -------------------------------------------------------------------------------- /docs/TMT/tmt_3.assets/image-20220126022354890.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Starlitnightly/bioinformatic_tutorial/58c572931aadf75e479eca3ef3506cb0585c9337/docs/TMT/tmt_3.assets/image-20220126022354890.png -------------------------------------------------------------------------------- /docs/TMT/tmt_3.md: -------------------------------------------------------------------------------- 1 | # TMT蛋白组学：下游分析 2 | 3 | 在前面的分析，我们已经得到了蛋白组学的搜库结果，那么我们怎么才能将其分析出具有生物学意义的东西呢？ 4 | 5 | 在结果的路径下，有一个Combined文件夹，里面的txt文件夹存放了我们的搜库结果，其中的evidence.txt跟proteinGroup.txt为最重要的两个，在这里，我们使用R包proteusTMT进行下游分析。 6 | 7 | ## 1. 安装依赖 8 | 9 | Proteus can be installed directly from GitHub. First, you need to install BioConductor and limma: 10 | 11 | ``` 12 | install.packages("BiocManager") 13 | BiocManager::install() 14 | BiocManager::install("limma") 15 | ``` 16 | 17 | You also need devtools: 18 | 19 | ``` 20 | install.packages("devtools") 21 | ``` 22 | 23 | In order to run examples or vignette code, additional packages with example data need to be installed: 24 | 25 | ``` 26 | devtools::install_github("bartongroup/proteusLabelFree") 27 | devtools::install_github("bartongroup/proteusTMT") 28 | devtools::install_github("bartongroup/proteusSILAC") 29 | ``` 30 | 31 | Finally, you can install proteus: 32 | 33 | ``` 34 | devtools::install_github("bartongroup/Proteus", build_opts= c("--no-resave-data", "--no-manual"), build_vignettes=TRUE) 35 | ``` 36 | 37 | Note: use `build_vignettes = FALSE` if you run into problems with vignettes installation. 38 | 39 | ## 2. meta文件准备 40 | 41 | 对于Proteus，其中最为重要的是我们需要对TMT6plex的每一个标记进行标注清楚，在这里，我们准备一个模板作为参考 42 | 43 | - experiment：这个是你在上游分析搜库的时候，对每一个raw设置的experiment情况，比如Ctrl1.raw文件里面，包含了6个TMT标记，其中每一个标记代表不同的样本，比如Ctrl1包括了WT1-1，WT2-1，WT3-1，SHED1-1，SHED2-1，SHED3-1，WT1-1指的是野生型的第一个重复 44 | - measure：TMT6plex将返回6个Reporter intensity，每一个对应上述的一个sample 45 | - sample：表示TMT6plex里都有哪六个样本 46 | - condition：样本分组情况 47 | - replicate：重复次数 48 | 49 | ![image-20220126021021714](tmt_3.assets/image-20220126021021714.png) 50 | 51 | ## 3. Proteus分析 52 | 53 | ### 3.1 加载依赖 54 | 55 | ``` 56 | library(proteusTMT) 57 | library(proteus) 58 | ``` 59 | 60 | ### 3.2 导入数据 61 | 62 | #### 3.2.1 列名设置 63 | 64 | The default `measure.cols` object is designed for label-free data. For TMT data we need to specify all reporter intensity columns. In out example we have 10 reporter columns, numbered from 1 to 6: 65 | 66 | ``` 67 | measCols <- paste0("Reporter intensity ", 1:6) 68 | names(measCols) <- paste0("reporter_", 1:6) 69 | ``` 70 | 71 | 意味着我们将Reporter intensity 1重命名为reporter_1 72 | 73 | #### 3.2.2 导入evidence和meta 74 | 75 | ``` 76 | evi <- readEvidenceFile('txt/evidence.txt', measure.cols=measCols) 77 | meta <- read.delim('meta.txt', header=TRUE, sep="\t") 78 | ``` 79 | 80 | #### 3.2.3 创建peptide数据集 81 | 82 | ``` 83 | pepdat <- makePeptideTable(evi, meta, measure.cols=measCols, aggregate.fun=aggregateMedian, experiment.type="TMT") 84 | plotCount(pepdat) 85 | ``` 86 | 87 | ![image-20220126022018085](tmt_3.assets/image-20220126022018085.png) 88 | 89 | ### 3.3 蛋白结果数据 90 | 91 | #### 3.3.1 创建蛋白结果数据 92 | 93 | We create protein data using the high-flyer method. 94 | 95 | ``` 96 | prodat <- makeProteinTable(pepdat, aggregate.fun=aggregateHifly, hifly=3) 97 | ``` 98 | 99 | #### 3.3.2 Normalization 100 | 101 | For TMT data we recommend using CONSTANd normalization [Maes et al. 2016](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4974351/pdf/zjw2779.pdf). 102 | 103 | ``` 104 | prodat.norm <- normalizeTMT(prodat) 105 | write.csv(prodat.norm[["tab"]],file='prodat.csv') 106 | ``` 107 | 108 | These two figures show reporter intensity distributions before and after normalization. 109 | 110 | ``` 111 | plotSampleDistributions(prodat, fill="replicate") 112 | ``` 113 | 114 | ![image-20220126022234665](tmt_3.assets/image-20220126022234665.png) 115 | 116 | ``` 117 | plotSampleDistributions(prodat.norm, log.scale=FALSE, fill="replicate") 118 | ``` 119 | 120 | ![image-20220126022304695](tmt_3.assets/image-20220126022304695.png) 121 | 122 | #### 3.3.3 Clustering 123 | 124 | We can use the same function `plotClustering()` to see the dendrogram for the proteins. 125 | 126 | ``` 127 | plotClustering(prodat.norm) 128 | ``` 129 | 130 | ![image-20220126022354890](tmt_3.assets/image-20220126022354890.png) 131 | 132 | ### 3.4 差异表达分析 133 | 134 | 我们想比较SHED组跟WT组的差异情况，于是 135 | 136 | ``` 137 | res <- limmaDE(prodat.norm, conditions=c("WT", "SHED")) 138 | write.csv(res,file='WT_SHED_result.csv') 139 | ``` 140 | 141 | 得到的csv文件与前面的prodat.csv文件，即可参考RNA-seq的下游分析进行，这里不做过多的阐述 142 | 143 | RNA-seq下游分析教程：https://starlitnightly.github.io/bioinformatic_tutorial/RNASEQ/rnaseq_2.html -------------------------------------------------------------------------------- /docs/index.md: -------------------------------------------------------------------------------- 1 | # Bioinformatics_tutorial 2 | 3 | - 作者：starlitnightly 4 | - 日期：2021.11.02 5 | 6 | !!! note 楔子 7 | 撰写这个笔记，目的在于提供一个生物信息学相关的教程吧 8 | 9 | ——2021.11.02，星夜 10 | 11 | ## 目录 12 | 13 | - 【配置】：[CONFIG](https://starlitnightly.github.io/bioinformatic_tutorial/CONFIG/) 14 | - 【分析1: RNA-seq上游】：[RNASEQUP](https://starlitnightly.github.io/bioinformatic_tutorial/RNASEQUP/) 15 | - 【分析2: RNA-seq下游】：[RNASEQDOWN](https://starlitnightly.github.io/bioinformatic_tutorial/RNASEQ/) 16 | - 【分析3: 单细胞样本对齐】：[GLUEPAIR](https://starlitnightly.github.io/bioinformatic_tutorial/SCGLUE/) 17 | - 【分析4: MOFA单细胞多组学因子分析】：[MOFA](https://starlitnightly.github.io/bioinformatic_tutorial/MOFA/) 18 | - 【绘图：Python数据可视化-生物信息学专栏】：[VISABLE](https://starlitnightly.github.io/bioinformatic_tutorial/PLOT/) 19 | 20 | ## 数据 21 | 22 | 【绘图：Python数据可视化-生物信息学专栏】：[DATA](https://github.com/Starlitnightly/bioinformatic_tutorial/tree/main/PLOT/data) 23 | 24 | ## License 25 | 26 | 知识共享许可协议 27 | 28 | 本作品采用知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议进行许可。 -------------------------------------------------------------------------------- /docs/overrides/main.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | 3 | {% block content %} 4 | {{ super() }} 5 | 6 | {% if git_page_authors %} 7 |
8 | 9 | Authors: {{ git_page_authors | default('enable mkdocs-git-authors-plugin') }} 10 | 11 |
12 | {% endif %} 13 | {% endblock %} -------------------------------------------------------------------------------- /mkdocs.yml: -------------------------------------------------------------------------------- 1 | site_name: Bioinformatic Tutorial (Daxing Research) 2 | repo_url: https://github.com/Starlitnightly/bioinformatic_tutorial 3 | edit_uri: edit/main/docs/ 4 | 5 | nav: 6 | - 写在前面: index.md 7 | - 基础环境配置: 8 | - 配置1. Python环境: CONFIG/config_1.md 9 | - 配置2. Linux环境: CONFIG/config_2.md 10 | - 配置3. R语言环境: CONFIG/config_3.md 11 | - RNA-seq上游分析: 12 | - 1-1. 环境配置: RNASEQUP/rnasequp_1.md 13 | - 1-2. 上游分析全教程: RNASEQUP/rnasequp_2.md 14 | - RNA-seq下游分析: 15 | - 2-1. 环境配置: RNASEQ/rnaseq_1.md 16 | - 2-2. 差异表达基因(DEG)分析: RNASEQ/rnaseq_2.md 17 | - 2-3. 通路富集分析(GSEA): RNASEQ/rnaseq_3.md 18 | - 单细胞样本对齐: 19 | - 3-1. 环境配置: SCGLUE/scglue_1.md 20 | - 3-2. 数据预处理: SCGLUE/scglue_2.md 21 | - 3-3. 模型准备: SCGLUE/scglue_3.md 22 | - 3-4. 模型训练: SCGLUE/scglue_4.md 23 | - 3-5. 对齐细胞: SCGLUE/scglue_5.md 24 | - MOFA单细胞多组学因子分析: 25 | - 4-1. 环境配置: MOFA/mofa_1.md 26 | - 4-2. 单细胞样本对齐: MOFA/mofa_2.md 27 | - 4-3. scRNA-seq数据处理: MOFA/mofa_3.md 28 | - 4-4. scATAC-seq数据处理: MOFA/mofa_4.md 29 | - 4-5. MOFA模型构建: MOFA/mofa_5.md 30 | - 4-6. MOFA下游分析-R语言环节: MOFA/mofa_6.md 31 | - 4-7. MOFA下游分析-Python环节: MOFA/mofa_7.md 32 | - TMT蛋白组学分析: 33 | - 5-1. 环境配置: TMT/tmt_1.md 34 | - 5-2. TMT蛋白组学上游分析: TMT/tmt_2.md 35 | - 5-3. TMT蛋白组学下游分析: TMT/tmt_3.md 36 | - 多组学分析: 37 | - 6-1. 数据准备: MultiOMIC/book-1.md 38 | - 6-2. GLUE多组学整合: MultiOMIC/book-2.md 39 | - 6-3. 细胞亚群分析: MultiOMIC/book-3.md 40 | - 6-4. 差异表达分析: MultiOMIC/book-4.md 41 | - 6-5. 细胞通讯分析: MultiOMIC/book-5.md 42 | - 6-6. 动态调控分析: MultiOMIC/book-6.md 43 | - 6-7. 转录因子网络: MultiOMIC/book-7.md 44 | - Python可视化: 45 | - 1-1. 火山图: PLOT/plot_1.md 46 | - 1-2. 箱线图: PLOT/plot_2.md 47 | - 1-3. 热图(聚类): PLOT/plot_3.md 48 | 49 | 50 | plugins: 51 | - mkdocs-jupyter 52 | 53 | theme: 54 | name: material 55 | custom_dir: docs/overrides 56 | 57 | palette: 58 | - media: "(prefers-color-scheme: light)" 59 | scheme: default 60 | toggle: 61 | icon: material/toggle-switch-off-outline 62 | name: Switch to dark mode 63 | - media: "(prefers-color-scheme: dark)" 64 | scheme: slate 65 | toggle: 66 | icon: material/toggle-switch 67 | name: Switch to light mode 68 | 69 | features: 70 | - navigation.instant 71 | - navigation.tracking 72 | - navigation.indexes 73 | 74 | markdown_extensions: 75 | - admonition 76 | - pymdownx.details 77 | - attr_list 78 | - md_in_html 79 | - 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12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.du.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Dutch` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hi=function(){this.pipeline.reset(),this.pipeline.add(e.hi.trimmer,e.hi.stopWordFilter,e.hi.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hi.stemmer))},e.hi.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.hi.trimmer=e.trimmerSupport.generateTrimmer(e.hi.wordCharacters),e.Pipeline.registerFunction(e.hi.trimmer,"trimmer-hi"),e.hi.stopWordFilter=e.generateStopWordFilter("अत अपना अपनी अपने अभी अंदर आदि आप इत्यादि इन इनका इन्हीं इन्हें इन्हों इस इसका इसकी इसके इसमें इसी इसे उन उनका उनकी उनके उनको उन्हीं उन्हें उन्हों उस उसके उसी उसे एक एवं एस ऐसे और कई कर करता करते करना करने करें कहते कहा का काफ़ी कि कितना किन्हें किन्हों किया किर किस किसी किसे की कुछ कुल के को कोई कौन कौनसा गया घर जब जहाँ जा जितना जिन जिन्हें जिन्हों जिस जिसे जीधर जैसा जैसे जो तक तब तरह तिन तिन्हें तिन्हों तिस तिसे तो था थी थे दबारा दिया दुसरा दूसरे दो द्वारा न नके नहीं ना निहायत नीचे ने पर पहले पूरा पे फिर बनी बही बहुत बाद बाला बिलकुल भी भीतर मगर मानो मे में यदि यह यहाँ यही या यिह ये रखें रहा रहे ऱ्वासा लिए लिये लेकिन व वग़ैरह वर्ग वह वहाँ वहीं वाले वुह वे वो सकता सकते सबसे सभी साथ साबुत साभ सारा से सो संग ही हुआ हुई हुए है हैं हो होता होती होते होना होने".split(" ")),e.hi.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.hi.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var t=i.toString().toLowerCase().replace(/^\s+/,"");return r.cut(t).split("|")},e.Pipeline.registerFunction(e.hi.stemmer,"stemmer-hi"),e.Pipeline.registerFunction(e.hi.stopWordFilter,"stopWordFilter-hi")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.ja.min.js: -------------------------------------------------------------------------------- 1 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.ja=function(){this.pipeline.reset(),this.pipeline.add(e.ja.trimmer,e.ja.stopWordFilter,e.ja.stemmer),r?this.tokenizer=e.ja.tokenizer:(e.tokenizer&&(e.tokenizer=e.ja.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.ja.tokenizer))};var t=new e.TinySegmenter;e.ja.tokenizer=function(i){var n,o,s,p,a,u,m,l,c,f;if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new 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t(){for(;!C.in_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}for(;!C.out_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}return!1}function s(){for(var r;;)if(C.bra=C.cursor,r=C.find_among(p,3))switch(C.ket=C.cursor,r){case 1:C.slice_from("y");break;case 2:C.slice_from("i");break;case 3:if(C.cursor>=C.limit)return;C.cursor++}}function u(){return _<=C.cursor}function c(){return d<=C.cursor}function a(){var r=C.limit-C.cursor;C.find_among_b(g,3)&&(C.cursor=C.limit-r,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del()))}function l(){var r;w=!1,C.ket=C.cursor,C.eq_s_b(1,"e")&&(C.bra=C.cursor,u()&&(r=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-r,C.slice_del(),w=!0,a())))}function m(){var r;u()&&(r=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-r,C.eq_s_b(3,"gem")||(C.cursor=C.limit-r,C.slice_del(),a())))}function f(){var r,e,i,n,o,t,s=C.limit-C.cursor;if(C.ket=C.cursor,r=C.find_among_b(h,5))switch(C.bra=C.cursor,r){case 1:u()&&C.slice_from("heid");break;case 2:m();break;case 3:u()&&C.out_grouping_b(j,97,232)&&C.slice_del()}if(C.cursor=C.limit-s,l(),C.cursor=C.limit-s,C.ket=C.cursor,C.eq_s_b(4,"heid")&&(C.bra=C.cursor,c()&&(e=C.limit-C.cursor,C.eq_s_b(1,"c")||(C.cursor=C.limit-e,C.slice_del(),C.ket=C.cursor,C.eq_s_b(2,"en")&&(C.bra=C.cursor,m())))),C.cursor=C.limit-s,C.ket=C.cursor,r=C.find_among_b(k,6))switch(C.bra=C.cursor,r){case 1:if(c()){if(C.slice_del(),i=C.limit-C.cursor,C.ket=C.cursor,C.eq_s_b(2,"ig")&&(C.bra=C.cursor,c()&&(n=C.limit-C.cursor,!C.eq_s_b(1,"e")))){C.cursor=C.limit-n,C.slice_del();break}C.cursor=C.limit-i,a()}break;case 2:c()&&(o=C.limit-C.cursor,C.eq_s_b(1,"e")||(C.cursor=C.limit-o,C.slice_del()));break;case 3:c()&&(C.slice_del(),l());break;case 4:c()&&C.slice_del();break;case 5:c()&&w&&C.slice_del()}C.cursor=C.limit-s,C.out_grouping_b(z,73,232)&&(t=C.limit-C.cursor,C.find_among_b(v,4)&&C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-t,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del())))}var d,_,w,b=[new e("",-1,6),new e("á",0,1),new e("ä",0,1),new e("é",0,2),new e("ë",0,2),new e("í",0,3),new e("ï",0,3),new e("ó",0,4),new e("ö",0,4),new e("ú",0,5),new e("ü",0,5)],p=[new e("",-1,3),new e("I",0,2),new e("Y",0,1)],g=[new e("dd",-1,-1),new e("kk",-1,-1),new e("tt",-1,-1)],h=[new e("ene",-1,2),new e("se",-1,3),new e("en",-1,2),new e("heden",2,1),new e("s",-1,3)],k=[new e("end",-1,1),new e("ig",-1,2),new e("ing",-1,1),new e("lijk",-1,3),new e("baar",-1,4),new e("bar",-1,5)],v=[new e("aa",-1,-1),new e("ee",-1,-1),new e("oo",-1,-1),new e("uu",-1,-1)],q=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],z=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],j=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],C=new i;this.setCurrent=function(r){C.setCurrent(r)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var e=C.cursor;return r(),C.cursor=e,o(),C.limit_backward=e,C.cursor=C.limit,f(),C.cursor=C.limit_backward,s(),!0}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.nl.stemmer,"stemmer-nl"),r.nl.stopWordFilter=r.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" ")),r.Pipeline.registerFunction(r.nl.stopWordFilter,"stopWordFilter-nl")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.no.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Norwegian` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.no=function(){this.pipeline.reset(),this.pipeline.add(e.no.trimmer,e.no.stopWordFilter,e.no.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.no.stemmer))},e.no.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.no.trimmer=e.trimmerSupport.generateTrimmer(e.no.wordCharacters),e.Pipeline.registerFunction(e.no.trimmer,"trimmer-no"),e.no.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(){var e,r=w.cursor+3;if(a=w.limit,0<=r||r<=w.limit){for(s=r;;){if(e=w.cursor,w.in_grouping(d,97,248)){w.cursor=e;break}if(e>=w.limit)return;w.cursor=e+1}for(;!w.out_grouping(d,97,248);){if(w.cursor>=w.limit)return;w.cursor++}a=w.cursor,a=a&&(r=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,e=w.find_among_b(m,29),w.limit_backward=r,e))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:n=w.limit-w.cursor,w.in_grouping_b(c,98,122)?w.slice_del():(w.cursor=w.limit-n,w.eq_s_b(1,"k")&&w.out_grouping_b(d,97,248)&&w.slice_del());break;case 3:w.slice_from("er")}}function t(){var e,r=w.limit-w.cursor;w.cursor>=a&&(e=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,w.find_among_b(u,2)?(w.bra=w.cursor,w.limit_backward=e,w.cursor=w.limit-r,w.cursor>w.limit_backward&&(w.cursor--,w.bra=w.cursor,w.slice_del())):w.limit_backward=e)}function o(){var e,r;w.cursor>=a&&(r=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,e=w.find_among_b(l,11),e?(w.bra=w.cursor,w.limit_backward=r,1==e&&w.slice_del()):w.limit_backward=r)}var s,a,m=[new r("a",-1,1),new r("e",-1,1),new r("ede",1,1),new r("ande",1,1),new r("ende",1,1),new r("ane",1,1),new r("ene",1,1),new r("hetene",6,1),new r("erte",1,3),new r("en",-1,1),new r("heten",9,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",12,1),new r("s",-1,2),new r("as",14,1),new r("es",14,1),new r("edes",16,1),new r("endes",16,1),new r("enes",16,1),new r("hetenes",19,1),new r("ens",14,1),new r("hetens",21,1),new r("ers",14,1),new r("ets",14,1),new r("et",-1,1),new r("het",25,1),new r("ert",-1,3),new r("ast",-1,1)],u=[new r("dt",-1,-1),new r("vt",-1,-1)],l=[new r("leg",-1,1),new r("eleg",0,1),new r("ig",-1,1),new r("eig",2,1),new r("lig",2,1),new r("elig",4,1),new r("els",-1,1),new r("lov",-1,1),new r("elov",7,1),new r("slov",7,1),new r("hetslov",9,1)],d=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],c=[119,125,149,1],w=new n;this.setCurrent=function(e){w.setCurrent(e)},this.getCurrent=function(){return w.getCurrent()},this.stem=function(){var r=w.cursor;return e(),w.limit_backward=r,w.cursor=w.limit,i(),w.cursor=w.limit,t(),w.cursor=w.limit,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.no.stemmer,"stemmer-no"),e.no.stopWordFilter=e.generateStopWordFilter("alle at av bare begge ble blei bli blir blitt både båe da de deg dei deim deira deires dem den denne der dere deres det dette di din disse ditt du dykk dykkar då eg ein eit eitt eller elles en enn er et ett etter for fordi fra før ha hadde han hans har hennar henne hennes her hjå ho hoe honom hoss hossen hun hva hvem hver hvilke hvilken hvis hvor hvordan hvorfor i ikke ikkje ikkje ingen ingi inkje inn inni ja jeg kan kom korleis korso kun kunne kva kvar kvarhelst kven kvi kvifor man mange me med medan meg meget mellom men mi min mine mitt mot mykje ned no noe noen noka noko nokon nokor nokre nå når og også om opp oss over på samme seg selv si si sia sidan siden sin sine sitt sjøl skal skulle slik so som som somme somt så sånn til um upp ut uten var vart varte ved vere verte vi vil ville vore vors vort vår være være vært å".split(" ")),e.Pipeline.registerFunction(e.no.stopWordFilter,"stopWordFilter-no")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.stemmer.support.min.js: -------------------------------------------------------------------------------- 1 | !function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new Array(t),s=0;s=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},in_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},out_grouping:function(t,i,s){if(this.cursors||e>3]&1<<(7&e)))return this.cursor++,!0}return!1},out_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e>s||e>3]&1<<(7&e)))return this.cursor--,!0}return!1},eq_s:function(t,i){if(this.limit-this.cursor>1),f=0,l=o0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n+_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n+_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},find_among_b:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit_backward,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o=0;m--){if(n-l==u){f=-1;break}if(f=r.charCodeAt(n-1-l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n-_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n-_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},replace_s:function(t,i,s){var e=s.length-(i-t),n=r.substring(0,t),u=r.substring(i);return r=n+s+u,this.limit+=e,this.cursor>=i?this.cursor+=e:this.cursor>t&&(this.cursor=t),e},slice_check:function(){if(this.bra<0||this.bra>this.ket||this.ket>this.limit||this.limit>r.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),r.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.sv.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Swedish` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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