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📊 An R package for interactively exploring UCSC Xena https://xenabrowser.net/datapages/; Book: https://lishensuo.github.io/UCSCXenaShiny_Book; App online: https://shiny.hiplot.cn/ucsc-xena-shiny/, https://shiny.zhoulab.ac.cn/UCSCXenaShiny

Home Page: https://openbiox.github.io/UCSCXenaShiny/

License: GNU General Public License v3.0

R 99.57% JavaScript 0.21% Dockerfile 0.13% HTML 0.03% CSS 0.07%
r r-package shiny-apps ucsc-xena cancer-dataset cran

ucscxenashiny's Introduction

UCSCXenaShiny

CRAN status lifecycle R-CMD-check check in Biotreasury

UCSCXenaShiny is an R package for interactively exploring UCSC Xena. It is mainly designed to provide a web app (built on the top of {shiny} framework and {UCSCXenaTools} package) for downloading, analyzing and visualizing datasets from UCSC Xena.

Alt

Please cite the following article when you used UCSCXenaShiny in your study:


Shixiang Wang#, Yi Xiong#, Longfei Zhao#, Kai Gu#, Yin Li, Fei Zhao, Jianfeng Li, Mingjie Wang, Haitao Wang, Ziyu Tao, Tao Wu, Yichao Zheng, Xuejun Li, Xue-Song Liu, UCSCXenaShiny: An R/CRAN Package for Interactive Analysis of UCSC Xena Data, Bioinformatics, 2021;, btab561, https://doi.org/10.1093/bioinformatics/btab561.


☁️ Use on cloud

If you don't want to install R and packages locally, or you have no programming experience, try using this tool on Oncoharmony Network (http://shiny.zhoulab.ac.cn/UCSCXenaShiny) or Hiplot ORG platform (https://shiny.hiplot.cn/ucsc-xena-shiny).

🐍 Use with Conda

Name Downloads Version Platforms
Conda Recipe Conda Downloads Conda Version Conda Platforms

Install from conda-forge channel with:

conda install -c conda-forge r-ucscxenashiny

It is possible to list all of the versions of r-ucscxenashiny available on your platform with:

conda search r-ucscxenashiny --channel conda-forge

📦 Use with Docker

Docker Image Version (latest by date)

Docker Image Size (latest by date)

Docker Pulls

UCSCXenaShiny has corresponding docker image at https://hub.docker.com/r/shixiangwang/ucscxenashiny/, you can install the latest version with:

docker pull shixiangwang/ucscxenashiny

From v2, docker pull from GitHub ghcr.io registry is also supported, e.g., docker pull ghcr.io/openbiox/ucscxenashiny:master.

All versions can be found at https://hub.docker.com/r/shixiangwang/ucscxenashiny/tags/. To use a specified version (e.g., 2.0.0; latest code commit will auto-build a tag master), run the following command to install:

docker pull shixiangwang/ucscxenashiny:2.0.0
# docker pull shixiangwang/ucscxenashiny:master  # For latest code, unstable

Run the latest stable docker image and keep it at background with:

docker run -d --name xenashiny -p 3838:3838 shixiangwang/ucscxenashiny

Now you should find the Shiny when you open URL http://127.0.0.1:3838 with your web browser. If you deploy the docker in a public (cloud) Linux server, change 127.0.0.1 to the host IP.

For the first time, it may return a failure about 'take a long time to respond', please refresh the web page.

If the application failed to start. Check if the container has installed all dependencies.

docker exec xenashiny R -e 'source(system.file("shinyapp/utils_pkgs.R", package = "UCSCXenaShiny"))'

Or you can interactively check the container:

docker exec -it xenashiny /bin/bash

You can manage the deployed container with the following commands:

# Stop the container
docker stop xenashiny
# Start the container
docker start xenashiny

⏬ Manual installation

You can install stable release of UCSCXenaShiny from CRAN with:

install.packages("UCSCXenaShiny")

You can install the development version of UCSCXenaShiny from Github with:

remotes::install_github("openbiox/UCSCXenaShiny")

Or r-universe:

install.packages("UCSCXenaShiny", repos = c("https://openbiox.r-universe.dev", "https://cran.r-project.org"))

Other dependent R packages specific to the Shiny application will be automatically installed when you start with app_run() command. If you failed to install UCSCXenaShiny, please check if the following system dependencies have been properly installed or see Troubleshooting section for specific installation issues.

System dependencies installation

When you use Windows/MacOS, please skip reading this sub-section.

As Linux distributions are very diverse, here we only test the installation of UCSCXenaShiny on common used Ubuntu/CentOS. If you are using other Linux distributions, you need to solve the system dependencies installation problems yourself when you encounter R package installation errors. However, the installation of system dependencies on Ubuntu/CentOS could be very good references.

Please note all commands below are execuated with root.

Ubuntu:

apt update -y && apt install -y libcurl4-openssl-dev libssl-dev libxml2-dev \
	libgmp3-dev libmpfr-dev

CentOS:

yum update -y && yum install -y libcurl-devel openssl-devel libxml2-devel \
	gmp-devel mpfr-devel libjpeg-devel cairo-devel

🔰 Usage

First load package:

library(UCSCXenaShiny)

Start Shiny in your R console (ignore this if you just want to use functions in this package):

app_run()
# At default, the Shiny is running under client mode
# It means the data queried from remote UCSC Xena server will
# be saved to temporary directory determined by R
# If you frequently use this tool or deploy this tool as a web service for multiple users
# It is recommended to run it with 'server' mode
# i.e.,
#
# app_run("server")

If you want deploy UCSC Xena Shiny with Shiny Server, please copy App.R and www/ directory under shinyapp. xena.runMode on the top of App.R is recommended to set as "server" instead of "client" (default).

For advanced users, examples for illustrating useful functions to obtain and analyze data are described in vignette.

All exported data and functions are organized at here.

xena.cacheDir and xena.zenodoDir are two options to control where to store data.

e.g.,

xena.cacheDir = "/xena"
xena.zenodoDir = "/xena/datasets"

options(xena.cacheDir = xena.cacheDir, xena.zenodoDir = xena.zenodoDir)

options(xena.runMode = "server")

Option xena.runMode can be used to control the way how the Shiny works. It can be 'client' or 'server'. You can directly set it in app_run().

🎥 Videos

🛠️ Troubleshooting

  1. ERROR: dependencies ‘gmp’, ‘Rmpfr’ are not available for package ‘PMCMRplus’ or ERROR: dependency ‘pairwiseComparisons’ is not available for package ‘ggstatsplot’.

    Your operating system lacks gmp and Rmpfr development libraries.

    # Ubuntu
    apt install -y libgmp3-dev libmpfr-dev
    # CentOS
    yum install -y gmp-devel mpfr-devel
  2. installation of package ‘gridtext’ had non-zero exit status with error info grid-renderer.h:61:94: error: no matching function for call to ‘Rcpp::Vector<10, Rcpp::PreserveStorage>::Vector(int, bool&, const GraphicsContext&)’.

    You have an older C++ version which cannot support C++11 features. This error seems only happen on CentOS. Install a newer C++ and set it as default compiler for R would fix this problem.

    yum install centos-release-scl
    yum install devtoolset-9
    # If you use your non-root account to install packages, 
    # change /root to /home/your_id in the following command
    mkdir -p /root/.R
    vi /root/.R/Makevars 

    Append content to the openning file.

    CXX11=/opt/rh/devtoolset-9/root/usr/bin/g++ -std=c++11
  3. installation of package ‘nloptr’ had non-zero exit status with error info libtool: link: ERROR: no information for variable 'AR' cru.

    The latest version of nloptr can only support R>=4.0. When you are using R3.6 or below would have this issue. So install an older version in R console can fix this.

    packageurl <- "https://cran.r-project.org/src/contrib/Archive/nloptr/nloptr_1.2.1.tar.gz"
    
    install.packages(packageurl, repos=NULL, type="source")

    Reference: https://stackoverflow.com/questions/62900525/install-lme4-from-cran-on-ubuntu

  4. package ‘pacman’ is not available or similar.

    Install it by hand in R console.

    install.packages("pacman")
  5. there is no package called ‘shinythemes’ or similar.

    Install it by hand in R cosole.

    install.packages("shinythemes")
  6. Install package gganatogram failed or similar.

    Install it by hand in R cosole.

    if (!requireNamespace("gganatogram")) {
       library(remotes)
       tryCatch(
          remotes::install_github("jespermaag/gganatogram"),
          error = function(e) {
             remotes::install_git("https://gitee.com/XenaShiny/gganatogram")
          }
       )
    }
  7. Install package ggradar failed or similar.

    Install it by hand in R cosole.

    if (!requireNamespace("ggradar")) {
       library(remotes)
       tryCatch(
          remotes::install_github("ricardo-bion/ggradar"),
          error = function(e) {
             remotes::install_git("https://gitee.com/XenaShiny/ggradar")
          }
       )
    }

✍️ Author

📃 LICENSE

GPLv3 © Openbiox

ucscxenashiny's People

Contributors

byronxy avatar fei0810 avatar kaigu1990 avatar likelet avatar lishensuo avatar longfei8533 avatar mugpeng avatar qins avatar shixiangwang avatar yinlisssss avatar

Stargazers

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ucscxenashiny's Issues

未来开发计划

这个项目很久没有活动了,之前想要依赖大家轮流主持进行开发也未能成行。不过,我们之前的成果是可见的,这个包有1000多次的下载。借此项目的交流我也初步了解了大家的Shiny开发水平,都比我更有经验~

接下来,我们项目将以重构为契机,以”简约、实用、持续可拓展“为核心,推进 XenaShiny 的更新换代。

接下来的任务主要有以下几个方面:

  • 整个 Shiny 界面框架的设计。这个大家都比我有经验得多,希望能够大家讨论并有人举爪实现它,最近赵飞有说过一个很火的框架?。 主要将有以下几个页面:
    • Repository
    • General analysis toolkit
    • Functional Modules
    • Help
    • Developers

每个页面需要支持折叠,这样可以节省空间。上面只要设计大体的框架就可以了。我记得之前看过李胤设计的几个Shiny,里面有很多不错的界面特性。

下面是具体的内容开发:

  • Repository,也可以命名为 Home,在现在的 Repository 上进行修改,增补一个搜索栏。各组件位置的排布和功能的衔接可以讨论下怎么改进。
  • General analysis toolkit,通用数据分析箱,用于数据结构级别的处理、转换、可视化。有空请大家阅读 https://shixiangwang.github.io/home/en/tools/ucscxenatools-api/,了解下 Xena Shiny 的结构和已有的一些操作函数。我把最重要的信息拷贝下来:

Data rows are associated with “sample” IDs.
Sample IDs are unique within a “cohort”. s A “dataset” is a particular assay of a cohort, e.g. gene expression.
Datasets have associated metadata, specifying their data type and cohort.
There are three primary data types: dense matrix (samples by probes), sparse (sample, position, variant), and segmented (sample, position, value).
Dense matrices can be genotypic or phenotypic. Phenotypic matrices have associated field metadata (descriptive names, codes, etc.). Genotypic matricies may have an associated probeMap, which maps probes to genomic locations. If a matrix has hugo probeMap, the probes themselves are gene names. Otherwise, a probeMap is used to map a gene location to a set of probes.

  • Functional Modules,功能性数据分析模块,用于放置带生物学目的的分析功能和流程,有多少可以加多少
  • Help:用于版本更新说明和使用文档
  • Developers 开发者信息展示

目前最重要的任务:

  • 设计和使用框架
  • Repository 页面的功能讨论(更好的组织和表现形式)和修改实现,以及相关RStudio插件,用于支持选择数据后往 RStudio 插入相应的下载代码,以帮助提高分析可重复性
  • General analysis toolkit: 3类数据结构处理功能的讨论与实现
    • dense matrix (samples by probes)
    • sparse (sample, position, variant)
    • segmented (sample, position, value)

其他暂时不用管。

接下来进行一个星期的讨论,然后分配和认领任务。

Weekly Digest (15 December, 2019 - 22 December, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there was 1 commit.
🛠️ Modified clinical information by longfei8533


CONTRIBUTORS

Last week there was 1 contributor.
👤 longfei8533


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Last week there were no stargazers.


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Last week there were no releases.


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Pancan 数据提取与可视化一些问题

我发现目前这个有不少问题。
@Byronxy 先按照你的思路解决先关 checks的问题

  1. 首先我需要你把 vis_toil_TvsN 的数据获取和可视化分开,或者通过条件判断支持一个直接可视化预先获取的数据集。因为我们主要是在 shiny中可视化,如果用户修改一次就调用一次从数据库获取数据的函数这不是一个好的选择。

  2. 我发现你增加了一个数据集,这个要在 https://github.com/openbiox/XenaShiny/blob/master/R/data.R 中进行注释。另外我不知道这个数据集怎么得到的,是否可靠,一定要写清楚。如果能重复数据的来源,请把代码放入 https://github.com/openbiox/XenaShiny/tree/master/data-raw
    image

  3. https://github.com/openbiox/XenaShiny/blob/master/R/GeomSplitViolin.R 这个里面好像主要是提供一个几何函数,我发现你都使用了 export,这个一定要注释所有的函数参数

image

  1. 另外有个ggplot2的问题,我没太搞懂为什么要抛出一个NOTE,你检查下具体怎么修改
    image

等这些问题修正后我根据情况将我之前写的几个 数据操作和可视化函数 修改或者删除。

大家都可以探究下这个问题和可视化方案。

Shiny端相应的界面也需要开发,谁有兴趣呢?

我的想法是用户确定基因选择,然后生成图形后可以调整坐标,颜色等等。确定基因选择后获取数据会比较耗时,所以和后面的尽量间隔开。


在开发过程中,我发现需要频繁调用云端数据。有没有办法将数据cache到本地,如果利用的还是同样的数据,我们就不再从远程数据库请求?当shiny关闭后,将cache的数据删除掉?

Weekly Digest (12 January, 2020 - 19 January, 2020)

Here's the Weekly Digest for openbiox/XenaShiny:

ISSUES

NOISY ISSUE

The issue most discussed this week has been:
🔈 #47 开发流程和工具链, by ShixiangWang
It received 6 comments.

PULL REQUESTS

This week, no pull requests has been proposed by the users.

CONTRIBUTORS

This week, ShixiangWang has contributed in the repository.

STARGAZERS

This week, no user has starred this repository.

COMMITS

This week, there have been no commits.

RELEASES

This week, no releases were published.

That's all for this week, please watch 👀 and star ⭐ openbiox/XenaShiny to receive next weekly updates. 😃

Weekly Digest (27 October, 2019 - 3 November, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week 2 issues were created.
Of these, 1 issues have been closed and 1 issues are still open.

OPEN ISSUES

💚 #47 开发流程和工具链, by ShixiangWang

CLOSED ISSUES

❤️ #46 Sur plot zlf, by longfei8533


PULL REQUESTS

Last week, 1 pull request was created, updated or merged.

MERGED PULL REQUEST

Last week, 1 pull request was merged.
💜 #46 Sur plot zlf, by longfei8533


COMMITS

Last week there were 2 commits.
🛠️ Release 0.4.0 by ShixiangWang
🛠️ [Sur plot zlf (#46) * add survival analysis modules

add survival analysis modules


CONTRIBUTORS

Last week there were 2 contributors.
👤 ShixiangWang
👤 longfei8533


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


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第一阶段开发任务

目的

开发基本的Home页面以及Repository页面。

开发使用Shiny模板shinythemes https://rstudio.github.io/shinythemes/

绘图使用echarts4r https://echarts4r.john-coene.com/articles/get_started.html

要求

Home页面

介绍Xena Shiny并给出汇总信息,类似于GDC(https://portal.gdc.cancer.gov/)或TCIA(https://tcia.at/home)。一开始不用好看,主要是能整出个大致的框架:

  • 标题
  • 简短介绍
  • 信息图展示
  • 资源展示(图标+数字)

image

image

Repository页面

提供数据集的搜索、选择、下载功能。

页面左侧提供一些选择框

image

页面右侧提供用户过滤选择后数据集表格,默认可以展示所有信息(类似https://portal.gdc.cancer.gov/repository),表格需要支持链接,以便于后续开发。

任务领取

一开始可以自己构造数据实现原型。仓库Shiny已经写好了如何导入Xena数据集信息。

image

下面是5个任务。

  • 目前页面框架搭建

整个类似下面的。左上方显示XenaShiny,然后包含3个页面,标签为Home、Repository和Developers

image

  • Home页面标题描述文字与资源标签数字显示

类似

image

  • 信息图

发挥自己的创造力,类似(我说的是形式类似,并不是具体的数据实现要一样)

image

image

  • 数据集过滤器

image

  • 支持链接的表格显示

image

我最近花时间仔细研究下如何获取更多的Xena信息和底层代码,以及根据情况参与以上4项任务中。

加油!

Weekly Digest (8 September, 2019 - 15 September, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


That's all for last week, please 👀 Watch and Star the repository openbiox/XenaShiny to receive next weekly updates. 😃

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Weekly Digest (10 November, 2019 - 17 November, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there was 1 commit.
🛠️ Add ignore by RStudio Cloud by ShixiangWang


CONTRIBUTORS

Last week there was 1 contributor.
👤 ShixiangWang


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


That's all for last week, please 👀 Watch and Star the repository openbiox/XenaShiny to receive next weekly updates. 😃

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转录组分析

目的

实现xena的转录组数据的下游分析

方法

1. 考虑到数据较多,建议通过链接,或者直接访问xena网站,下载xena数据到用户本地电脑以后,再上传到xenashiny上面进行下游分析?还是直接下载存到服务器?

2. 下游分析包括:

a. 主页绘制某个基因的pancer表达情况,类似这样gepia那种风格(http://gepia.cancer-pku.cn/detail.php)
image
b. 考虑到xena的数据都是normalized过的,就直接用了?第二页就分析PCA, 差异分析,同时绘制出火山图和热图。以及感兴趣基因的功能富集分析。
c. 根据临床数据,绘制基因的相关临床相关的箱图
d. 生存分析
e. 一些转录组的相关分析,比如gsea,gsva等等。
目前我能想到的就是这些了,看大家有什么想法没有。

Home 页面修改

image

第2排全部改成估计值,右侧换成 hub 中 cohort 和 sample 数量展示。删除多余的任何数据。

Weekly Digest (29 December, 2019 - 5 January, 2020)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


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Weekly Digest (20 October, 2019 - 27 October, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week 1 issue was created.
It is still open.

OPEN ISSUES

💚 #44 未来开发计划, by ShixiangWang

NOISY ISSUE

🔈 #44 未来开发计划, by ShixiangWang
It received 8 comments.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


That's all for last week, please 👀 Watch and Star the repository openbiox/XenaShiny to receive next weekly updates. 😃

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Weekly Digest (6 October, 2019 - 13 October, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


That's all for last week, please 👀 Watch and Star the repository openbiox/XenaShiny to receive next weekly updates. 😃

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Weekly Digest (26 April, 2020 - 3 May, 2020)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


That's all for last week, please 👀 Watch and Star the repository openbiox/XenaShiny to receive next weekly updates. 😃

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Weekly Digest (16 February, 2020 - 23 February, 2020)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


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Weekly Digest (26 January, 2020 - 2 February, 2020)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


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Last week there were no releases.


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Xena数据集显示与过滤器

除了正在处理的Home页面,Repository页面的设计和实现也可以同步进行,请感兴趣的成员在下方讨论并留言是否愿意主力实现相关功能。

目前我使用DT弄了一个比较粗糙的表格

image

实际最后的实现效果参考:https://portal.gdc.cancer.gov/repository

页面左侧是一堆过滤器(我们不设计关于具体样本的),XenaData提供了10多个变量可以用于筛选。最下方提供一个下载按钮。

页面右侧以表格的形式展示几个重要的信息,展示的数据集是xena_table,表格要支持链接。至少每一行要对应一个链接,可以用于展示数据集详细的信息。

另外看到一个参考链接 https://datatables.net/download/


现在数据有了,我的整体设想就是:

  • 有过滤器可以筛选数据集,右侧实时显示表格(只列几个重要信息),然后表格还有链接可以点击查看某个数据集的详细信息。
  • 在过滤器的下方提供下载按钮,然后可以在下方显示或者弹出一个窗口/页面展示用户选定的用户集,提供操作让用户可以指定下载路径和方式,确定后进行下载。

Weekly Digest (8 December, 2019 - 15 December, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


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月度计划(2019-03:04)

  • 2019-03:Shiny与ShinyDashboard接触和学习
  • 2019-04:echarts4r的学习与Shiny主页(Home)的讨论与开发实现

目标在4月底实现Home页面,探索并实现数据集下载功能(看实际的学习和开发进度,后面这一点我考虑是5月底前应该完成的模板,所以没有写在上面的列表中)。

首先说一个重点:尽量不要在仓库主分支进行修改,除非是简单地增加文件或其他内容。开发时请建立自己的分支,确认某一功能特性完成后向master主分支提交合并请求。

没有克隆过XenaShiny仓库的成员请克隆仓库,并使用RStudio运行https://github.com/openbiox/XenaShiny/tree/master/R下的APP文件。

关于主页的设计大家不需要囿于我之前想设计得更TCIA数据库一样,大家在群里提出并讨论自己对于主页的设想。

我们接下来学习的重点是echarts4r,它类似网页版本的ggplot2,其实echarts的包有不少,但我感觉它最好,文档在https://echarts4r.john-coene.com/。请大家在接下来学习它,熟悉它,精通它。其他有兴趣的也可以使用plotly(ggplot2真正的网页版本)


觉得内容不清楚,还有需要补充和修改的请留言回复我。

2019-05月度开发任务

接下来的开发主题主要有3个内容:

  • 模块:函数化的功能模块 + UI
  • 流程:模块的有序、结构化组合形成常用分析流程 + UI
  • 帮助系统:关于Xena的词条、常见问题QA等

接下来的一个月,我们将以转录组相关的分析为主体内容,以gdcHub Counts, FPKM等结构化表达矩阵为操作对象,构建相关的模块与流程。

指南

模块

模块根据功能划分为几大块

  • 数据导入与导出
  • 数据清理(预处理、转换、标准化等)
  • 统计分析:常见的统计相关分析,如各种检验
  • 组学分析:常见的组学分析单元,如limma分析(模型构建和差异执行需要拆解)
  • 可视化

流程

  • 差异表达分析

帮助文档

  • Xena hub/host, cohort, datasets等基本词条概念
  • 导入导出数据格式指南
  • 一些重要模块参数说明

任务分配与实施

任务要求

  • 所有的基本模块都要打包成函数,以m_开头,才有蛇形命名,如mo_import可以名为为数据导入,由模块组合的pipeline(目前就有一个转录组),以pi_开头。
  • 模块函数都应该写在R目录下,一个函数一个文件,如果功能是相似的,可以写在一个文件中。每个函数都分为文本块与代码块,包的结构我已经设定好了,所以文本块使用markdown就可以,代码块就不用说了。请没有相关经验的务必阅读https://cran.r-project.org/web/packages/roxygen2/vignettes/rd.html,任何不懂的在钉钉群提问。

https://github.com/ShixiangWang/UCSCXenaTools/blob/master/R/workflow.R这个可以随便看一下作为参考,文本块的结构基本都是一致的。

  • UI(包括外观与server执行)都写在App.R

任务

  • 常见文件格式导入,支持文件大小限定
  • 长宽格式数据转换
  • 表达矩阵的log化与unlog化
  • 表达矩阵预处理与标准化
  • 表达差异分析流程(limma, edgR,deseq2)
  • 分析报告
  • boxplots
  • pheatmap

我差异分析做的少,几个包都不怎么会,希望大家多给点意见,把上面的完善下。

Xena小队开发资源与技巧

根据大家的学习、开发经验汇总与该项目相关的资源与技巧

请大家在开发的过程中不断更新评论,我会更新到一楼中。

R包开发

Shiny常用相关包

  • shiny
  • shinydashboard
  • shinyjs
  • shinyBS
  • DT
  • plotly
  • echarts4r

Shiny开发学习资源

资料

参考App

代码管理

代码折叠

使用RStudio,帮助快速定位代码。

阅读官方文档Code Folding and Sections

效果:

image

image

模块化

XenaShiny 项目讨论会 - 2019-11-30

工作流

具体再看看:#47

功能及实现模块的选择

目标用户:

  • 癌症(肿瘤)研究者
  • 生信分析人员

功能与模块:

  • 整个数据库数据集展示与选择
  • 某队列数据临床数据的特征分布展示
  • 数据集下载
  • 独立的模块展示 UI
  • 单基因表达数据的展示
  • 基因表达生存分析

任务分配

  • 整个数据库数据集展示与选择 - 顾恺
  • 某队列数据临床数据的特征分布展示 - 赵飞
  • 数据集下载 - 王诗翔
  • 独立的模块展示 UI - 李胤
  • 单基因表达数据的展示 - 熊逸
  • 基因表达生存分析 - 赵龙飞

学习参考项目

云平台

https://rstudio.cloud/project/714304

Weekly Digest (23 February, 2020 - 1 March, 2020)

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Last week, no pull requests were created, updated or merged.


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Weekly Digest (19 January, 2020 - 26 January, 2020)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


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Last week there were no stargazers.


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Last week there were no releases.


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Weekly Digest (15 September, 2019 - 22 September, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there was 1 stargazer.
jimbrig2011
You are the star! 🌟


RELEASES

Last week there were no releases.


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2019-07月度开发任务

由于个人原因在之前2个月内与大家进度脱节了,因此先看了下UCSCXenaShiny的development版本,按照现有的思路大致还需要完成的工作内容有5个modules和1个pipeline

有些部分可能大家已经完成了,可以先在这个issues里反馈下,然后大家可以相互提点建议完善下

我先按照分析顺序从头先挑2个modules作为本月中旬到8月中旬开发任务,如下:

  • Import&Export
  • Data Tidy

首先我觉得Import&Export模块是用户使用的第一步,这样可以将分析模块和之前我们已经完成的数据搜索及下载模块有效的衔接在一起(不需要每次分析前必须load下数据);这样的话,用户在下载数据后,可以先关闭shiny app,过段时间后再打开shiny app进行下一步分析

接着是Data Tidy模块,这个可方便用户对于数据做一些简单的过滤以及整理,主要是用于减少数据量,方便可视化分析;但是对于不同的数据来源,其数据结构可能不太一致,所以这个模块要么设计的通用性较好点,要么设计的详细点

以上是个人对于这两个模块的想法,欢迎大家补充讨论下

然后是需要先讨论下两个modules的UI界面设计,需要添加哪些UI功能,大家留言一起讨论呗

Weekly Digest (1 September, 2019 - 8 September, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week 2 issues were created.
Of these, 2 issues have been closed and 0 issues are still open.

CLOSED ISSUES

❤️ #36 🐛 Fix pancan showing, by ShixiangWang
❤️ #35 更改版本号为开发, by ShixiangWang


PULL REQUESTS

Last week, 2 pull requests were created, updated or merged.

MERGED PULL REQUEST

Last week, 2 pull requests were merged.
💜 #36 🐛 Fix pancan showing, by ShixiangWang
💜 #35 更改版本号为开发, by ShixiangWang


COMMITS

Last week there were 3 commits.
🛠️ 🐛 Fix pancan showing (#36) by ShixiangWang
🛠️ 更改版本号为开发 (#35) by ShixiangWang
🛠️ Add weekly digest by ShixiangWang


CONTRIBUTORS

Last week there was 1 contributor.
👤 ShixiangWang


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there was 1 release.
🚀 v0.3.0 v0.3.0


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Weekly Digest (2 February, 2020 - 9 February, 2020)

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ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


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Weekly Digest (9 February, 2020 - 16 February, 2020)

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ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


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Weekly Digest (22 December, 2019 - 29 December, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there was 1 commit.
🛠️ Update NEWS.md by ShixiangWang


CONTRIBUTORS

Last week there was 1 contributor.
👤 ShixiangWang


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


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关于datatable中添加复选框的问题

遇到的问题是这样的,代码如下,如果初始不变动滑动条的话,是正常运行的;勾选掉某个复选框后,其最下方输出的位置变成了FALSE;
如果变动滑动条,比如从6改成4,那么再勾选掉3-7这几个位置的复选框后,其下方对应位置的值没有发生变化,而勾选掉1-2这两个位置的复选框则是正常
我觉得大致原因是由于HTML的重复id冲突了,使得shiny中对应的那些id的部件失效了(意思是:最开始生成了初始滑动条对应的5个复选框插件,后来将滑动条的偏小值从6改成了4,生成了7个复选框插件,但后5个插件的Id是跟初始的时候是一样的)
因为我们的网站是有过滤插件,所以用到网站上也是这个BUG
大家有什么解决办法吗?我JS不怎么会写。。如果有人会的话,单纯用HTML+JS试试?

library(shiny)
library(DT)
shinyApp(
  ui = fluidPage(
    sliderInput(inputId = "number", label = "Number", min = 1, max = 20, value = c(6,12), step = 1),
    DT::dataTableOutput('x1'), 
    verbatimTextOutput('x2')
  ),
  
  server = function(input, output) {
    # create a character vector of shiny inputs
    shinyInput = function(FUN, len, id, ...) {
      inputs = character(len)
      for (i in seq_len(len)) {
        inputs[i] = as.character(FUN(paste0(id, i), label = NULL, ...))
      }
      inputs
    }
    
    # obtain the values of inputs
    shinyValue = function(id, len) {
      unlist(lapply(seq_len(len), function(i) {
        value = input[[paste0(id, i)]]
        if (is.null(value)) NA else value
      }))
    }
    
    # a sample data frame
    res <- reactive({
      res = data.frame(
        v1 = shinyInput(numericInput, 20, 'v1_', value = 0),
        v2 = shinyInput(checkboxInput, 20, 'v2_', value = TRUE),
        v3 = 1:20,
        v4 = sample(LETTERS, 20, TRUE),
        stringsAsFactors = FALSE
      )
      res <- dplyr::filter(res, v3 > input$number[1] & v3 < input$number[2])
      
      return(res)
    })
    
    # render the table containing shiny inputs
    output$x1 = DT::renderDataTable(
      res(), server = FALSE, escape = FALSE, selection = 'none', 
      options = list(
        preDrawCallback = JS('function() { Shiny.unbindAll(this.api().table().node()); }'),
        drawCallback = JS('function() { Shiny.bindAll(this.api().table().node()); } ')
      )
    )
    # print the values of inputs
    output$x2 = renderPrint({
      data.frame(v1 = shinyValue('v1_', 20), v2 = shinyValue('v2_', 20))
    })
  }
)

Weekly Digest (5 January, 2020 - 12 January, 2020)

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ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there was 1 stargazer.
kojix2
You are the star! 🌟


RELEASES

Last week there were no releases.


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Weekly Digest (22 September, 2019 - 29 September, 2019)

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ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


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Weekly Digest (17 November, 2019 - 24 November, 2019)

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ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


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Weekly Digest (13 October, 2019 - 20 October, 2019)

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ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


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Weekly Digest (28 August, 2019 - 4 September, 2019)

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ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


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Weekly Digest (29 September, 2019 - 6 October, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week, no issues were created.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


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Weekly Digest (24 November, 2019 - 1 December, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


ISSUES

Last week 2 issues were created.
Of these, 0 issues have been closed and 2 issues are still open.

OPEN ISSUES

💚 #53 XenaShiny 项目讨论会 - 2019-11-30, by ShixiangWang
💚 #52 模块池, by ShixiangWang

NOISY ISSUE

🔈 #53 XenaShiny 项目讨论会 - 2019-11-30, by ShixiangWang
It received 4 comments.


PULL REQUESTS

Last week, no pull requests were created, updated or merged.


COMMITS

Last week there were no commits.


CONTRIBUTORS

Last week there were no contributors.


STARGAZERS

Last week there were no stargazers.


RELEASES

Last week there were no releases.


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开发流程和工具链

根据 https://rtask.thinkr.fr/building-big-shiny-apps-a-workflow-2/ 文章的指引,我总结下我们协作开发的流程和使用的工具链

主要的工具是 shiny 和 golem 包,前者开发,后者辅助开发和测试

开发之前看下 https://thinkr-open.github.io/golem/articles/b_dev.html 了解下一些便利函数的用法,另外 golem 自动加了一些工具包,大家可以学习下提高开发效率

    processx,
    attempt,
    DT,
    glue,
    htmltools

另外还内置了一些工具函数在 R 目录下:

image

下面是 shiny 和 golem 的备查表,请大家有机会打印放到身边作为参考:

cheatsheet.pdf
golem_cheatsheet_v0.1.pdf

整体的 UI 布局我们暂时使用 Shiny Dashboard,文档见 https://rstudio.github.io/shinydashboard/get_started.html

Weekly Digest (3 May, 2020 - 10 May, 2020)

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UCSC Xena数据库概览

层级关系:

  • UCSC Xena
    • Data Hub
      • Cohort
        • Dataset

Xena所有的数据集都可以在https://xenabrowser.net/datapages/进行查看。

如页面右侧显示了几个Data hub

image

点击就可以查看对应的信息,做了哪些处理,有哪些参考文献。

UCSC Xena收集了非常多公开的癌症数据集,为了便于不同的数据之间进行比较,数据重新统一做了处理,因此非常有参考或者挖掘的意义。

页面正文提供的是队列信息以及包含的数据集个数

image

点进去可以发现不同的数据集信息

image

如果点击某个数据集就可以查看数据集更详细的信息,包括含义,下载链接,度量单位,匹配探针,文献等,除了下载链接被UCSCXenaTools抓取了,其他都没有,所有本身我之前的包就有很大的提升空间,不过我想都直接在XenaShiny中实现。

数据集的信息都在XenaData这个对象中实现了

image

所以可以在XenaShiny的主页显示一些数据集信息,类似下面的

image

并且可以利用它过滤和筛选用户想要的数据。

大家有时间运行一下https://github.com/ShixiangWang/UCSCXenaToolshttps://cran.r-project.org/web/packages/UCSCXenaTools/vignettes/USCSXenaTools.html上的代码,使用过后肯定会有更清楚的认识和见解。其中还有很多事情我还没有实现,我希望能够通过XenaShiny展示出来,这有待于大家一起持续学习和努力~

模块池

这里是模块池,一些未来开发的目标。

完成的打勾,并标记主要的开发人员。

  • 整个数据库数据集展示与选择
  • 某队列数据临床数据的特征分布展示
  • 数据集下载
  • 独立的模块展示 UI
  • 单基因表达数据的展示
  • 基因表达生存分析
  • RStudio 插件-实现插入数据下载代码
  • 点击 数据集,跳转到 UCSC Xena 对应页面(对应 UCSCXenaTools 的 XenaBrowse()功能)

Weekly Digest (1 December, 2019 - 8 December, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


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App won't start, "no file found" error

Hi,

After a fresh install, remotes::install_github("openbiox/XenaShiny"), attempt to app_run() fails with error:

Error in system.file("inst", "shinyapp", "modules", package = "UCSCXenaShiny",  : 
  no file found

R version is 3.6.1, all latest updates included. What may be wrong?

Thanks,
Mikhail

Weekly Digest (3 November, 2019 - 10 November, 2019)

Here's the Weekly Digest for openbiox/XenaShiny:


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Last week there was 1 stargazer.
ying-ge
You are the star! 🌟


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