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[English Version](./) | [中文版](./zh)
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Visualization of data is required for most scientific research. Based on visual graphics, we can better display the major features of scientific data. The clinical medical students and junior researchers frequently need to spend dozens of days or even months to learn visualization tools, such as SPSS, Origin, and Graphpad. If you want to specialize in data analysis and modeling, you need to learn one or more programming languages (such as MATLAB, R, and Python). To reach the level where you can freely explore data, you need to spend more time on in-depth learning and advanced.
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Interactive visualization and personalized in-depth interpretation of multi-dimensional scientific research data have become one of the major challenges for quantitative researches.
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In recent years, with the development of various cloud computing platforms (such as Galaxy and DNAnexus in the biomedical field) and IT hardware and software (such as distributed computing, container technology, package manager, workflow language, etc.), junior researchers can easily obtain the upstream result. Moreover, when the upstream analysis process of conventional omics data tends to be stable and perfect, the customization and variability of the upstream analysis process have been greatly reduced. The visualization and personalized in-depth interpretation in the downstream process of data analysis have become the biggest challenge facing current users:
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1. Most of the visualization software or methods developed by the open source user community have not been well integrated under a unified user interface;
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2. There is no active collaborative community for the visualization of scientific research data in China, and the "** Draw Group" has become one of the few choices for primary scientific research users;
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3. There is a lack of core data visualization software and platforms similar to Graphpad and MATLAB in China. After being banned by the United States, they can only spend additional costs to migrate the process or start over;
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4. There are several disadvantages of existing platforms and tools: the user interface is not beautiful enough; lack of the Chinese and English support; it is still difficult to get started; the file Manager of some platforms is not convenient; users cannot actively participate in platform development；
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5. Data visualization tools are still relatively scarce.
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4. There are several disadvantages of existing platforms and tools: the user interface is not beautiful enough; lack of the Chinese and English support; it is still difficult to get started; the file Manager of some platforms is not convenient; users cannot actively participate in platform development.
[Hiplot](https://hiplot.com.cn) is one of the community-driven bioinformatics data visualization platforms. This project started in October 2019 and accelerate in 2020 after COVID-19 breakout. It consists of a 6-person core team covering the IT, bioinformatics students, statistical analysts, and clinicians. The interdisciplinary cooperation may help us for better development of this application.
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The major development goals of Hiplot:
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- Provide the simple and easy-to-use Web and RESTful APIs for the bioinformatics data visualization tools that are no graphical interface support yet.
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- Features cover the basic statistics and visualization, predictable clinical models, data analysis of macrobiotics, and the bioinformatics applications of deep learning methods
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- Building a collaborative bioinformatics community for the data visualization
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Hiplot (https://hiplot.com.cn) is a free and comprehensive cloud platform for scientific computation and visualization based on web technology, which is supported by open source communities. It provides more simple web and command-line interfaces compared with other similar platforms.
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Untill now, we have been developed more than 40 visualization plugins on the Hiplot platform and are still increasing:
Download publications and supplementary data based on [bget](https://github.com/openanno/bget)
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If you want to contribute the Hiplot project, you can fell free to join our community: [Discord](https://discord.gg/vX6tSax).
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Editable table view and switched file uploader simplify the data loading process for most tasks. Data, parameters, and task history can be imported and exported via the JSON files for enhancing reproducibility. A single R language script or function can be used to generate the web front-end interface, command-line interface, and the meta description of a Hiplot plugin. Up to now, hundreds of free applications have been working on Hiplot, including basic statistics, predictive models, multi-omics data analysis, and text-mining.
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This work is licensed under a <arel="license"href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
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# How to contribute
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You can join the user community first: [Discord](https://discord.gg/vX6tSax).
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You can join the user community first: [Discord](https://discord.gg/vX6tSax) or [Wechat Group](https://docs.qq.com/doc/DS09na3NVYk9OcHVp).
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All feedback and visualization scripts can be shared on the user community. We will respond instantly and update the corresponding visualization plugin . For the security of our website, we will do not share the web service code publicly, and only the core team can access and modified it. If you want to join our core team, you can also feel free to contact us: [email protected].
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE)
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```
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## Desktop Client
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We build the desktop client of Hiplot based on the [Electron](https://www.electronjs.org/). The demo data and UI components are fixed in the desktop client under the given version.
It is required to login Hiplot server first using the `hctl login` command. `hctl plot` command can be used to draw plots by using the parameter file and data files.
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# Download
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## Desktop Client
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We build the desktop client of Hiplot based on the [Electron](https://www.electronjs.org/). The demo data and UI components are fixed in the desktop client under the given version.
It is required to login Hiplot server first using the `hctl login` command. `hctl plot` command can be used to draw plots by using the parameter file and data files.
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