注意:直接拷贝文本,粘贴到输入数据框即可
必需输入
输入文本:


可选输入:
图大小
宽:,高:
最小文字尺寸
最大显示word数
英文逗号(,)分割的停用字,例如:不想显示cancer和gene,则填写cancer,gene


颜色和绘图区域
默认颜色,默认长方形
默认颜色,自定义背景图区域
背景图颜色,自定义背景图区域
背景图URL
说明:1,背景图颜色:根据所提供的背景图的颜色,进行绘图
2,背景图区域:在所提供图片的**非白色**区域内绘制(可以PS下)
推荐的搜索下载网站:https://www.onlinewebfonts.com/icon/ 关键词搜索你感兴趣的图片,拷贝粘贴地址。
例如:https://pic.onlinewebfonts.com/svg/img_460260.png,将文字局限于新冠注释器内

此图将消耗 0 微币,约10s出图

词云(wordcloud)

简介
以带颜色的word图显示一段文本中的word,频率越高,字体越大。可以帮助我们快速了解一段文字。注:该图不是矢量图。 数据说明:
数据为一段文字,支持中英文混编
生物医学常见应用举例
新冠病毒COVID-19词云分析。参考:An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation Fig 7.

输入 The unprecedented outbreak of COVID-19 is one of the most serious global threats to public health in this century. During this crisis, specialists in information science could play key roles to support the efforts of scientists in the health and medical community for combatting COVID-19. In this article, we demonstrate that information specialists can support health and medical community by applying text mining technique with latent Dirichlet allocation procedure to perform an overview of a mass of coronavirus literature. This overview presents the generic research themes of the coronavirus diseases: COVID-19, MERS and SARS, reveals the representative literature per main research theme and displays a network visualisation to explore the overlapping, similarity and difference among these themes. The overview can help the health and medical communities to extract useful information and interrelationships from coronavirus-related studies.
输出

1)如何作图?
1,准备作图数据;2,用excel打开数据,调整为示例格式;3,将调整后的数据粘贴到输入框;4,选择参数;5,提交出图

2)为什么不出图?
程序对输入格式有严格要求。请务必仔细查看右侧说明及示例数据

3)如何引用?
140篇文章引用我们(Google Scholoar)。请引用原生R包,Python包等,或使用如下格式:
Heatmap was plotted by http://www.bioinformatics.com.cn, a free online platform for data analysis and visualization.

4)其他常见问题