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This article mainly shows you the "sample analysis of information extraction in various regions of VennDiagram", which is easy to understand and well-organized. I hope it can help you solve your doubts. Let the editor lead you to study and study the "sample analysis of information extraction in various regions of VennDiagram".

VennDiagram is an R package for Venn drawing, which can draw up to five sets of data, one of which is venn.diagram ().

According to the parameter adjustment related to this parameter, multi-themes such as color and font of Venn diagram can be modified directly. And its drawing data, that is, the data corresponding to venn.diagram (), is a list vector.

The reading and collation of data can be referred to:

R language drawing Venn diagram null NA processing: https://www..com/article/259 data reading method

1. For example, after similar data reading and processing, get a vector xList. The basic data is as follows:

Summary (xList) Length Class Mode AT_4000 4000-none- characterAT_3892 3892-none- characterAT_3593 3593-none- characterAT_3000 3000-none- characterAT_2987 2987-none- character

A total of five sets of data, each group of data as shown above, venn.diagram () drawing:

Venn.plot = venn.diagram (xList,filename = NULL) grid.newpage () grid.draw (venn.plot)

Then you can adjust it based on the relevant code.

2 how to obtain the information of each region is a key problem in drawing Venn diagram.

It can be based on the calculation function calculate.overlap or get.venn.partitions provided by VennDiagram

Take the former as an example:

Area=calculate.overlap (xList) > typeof (area) [1] "list" > summary (area) Length Class Mode a31 220-none- charactera30 286-none- charactera29 285-none- charactera28 175-none- charactera27 178-none- charactera26 149-none- charactera25 136-none- charactera24 206-none- charactera23 356-none- charactera21 217-none- charactera20 284-none- charactera19 255-none -charactera18-none- charactera17-none- charactera16 131123123none- charactera14-none- charactera13 187- none- charactera12-none- charactera11-none- charactera11-none- charactera10-none- charactera9-none- charactera8-209The none- charactera7-none- charactera7-none- charactera6-none- charactera5 165- none- charactera4 1531111-none- charactera2 224-none- charactera1 273-none- character

As the above results show that the five groups have a total of 31 regions of data, you can use the returned list to extract the results of each region, such as taking out the top 10 of A1 regions (a total of 273):

> area$a1 [1:10] [1] "AT3G27960", "AT4G12170", "AT1G58270", "AT3G55290", "AT1G27360", "AT2G02790", "AT1G42990", "AT3G25011" [9] "AT2G24617" and "AT5G55910" are all the contents of the article "sample Analysis of VennDiagram Regional Information extraction". Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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