该工具为Omicshare VIP会员专属使用,注册用户可免费使用两次。会员>>

choosefile *  example

choosefile *  example

choosefile   example

1. adjustment of global parameters and sample parameters

2. Adjustment of response variables and explanatory variables

3. Graphic resizing and downloading

1. Function:

CCA analysis is used to explain the influence of variables on response variables, for example, to analyze the influence of environmental factors on the distribution of microbial community.

2. Application scope:

It is applicable to all data suitable for CCA analysis, such as transcriptome, metabolomics, proteins and 16S.

3. Input

A. response variable file: table file, the first behavior sample information, and the first column is response variable information (such as species abundance table of microbial 16s sequencing).

Description of uploaded file format: the format is as follows. The data must be separated by tab characters. You can choose to open it in Excel and save it as a. txt file separated by tab key.

B. explanatory variable file: table file, the first line is the name of explanatory variable (such as various environmental factor tables), the first column is the sample name, and the file format is consistent with the response. Note: There can be no spaces in the explanatory variable table. Please delete the lines with spaces before uploading the file.

C .grouping file (optional): table file, the first column is sample information, and the second column is sample grouping information. Grouping file can be optionally entered. Enter grouping file legend to display grouping. If you don't enter it, the legend will display all sample information.

4. Output

1) task output

The program calculates and draws according to the input file, and switches tasks in the result display to check the task status.

2) Graphics global parameters:

You can modify the graphic title, font type and dot transparency, check the display frame, legend, grid line and scale line, and choose to adjust the coordinate axis range adaptively or manually enter the range of horizontal and vertical axes.

3) Graphic sample modification:

A. point size: the size of sample points can be selected.

B. peripheral shape and area transparency: add peripheral shapes (ellipses and connecting lines) for grouping and adjust the transparency of peripheral areas.

C. check the display: origin auxiliary line, centroid, centroid name and sample name

D. Color matching and shape: you can choose different color matching schemes and sample shapes

4) Response variable modification:

Check the display variable and name, and adjust the color and size of points.

5) Modification of explanatory variables:

Check the display variable to adjust the thickness, stretching ratio and color of the variable arrow.

6) Image resizing and downloading

It can adjust the height and width of graphics and preview them. Provide SVG and png formats for downloading.

7) Table results

A. DCA result table: used to judge the linearity of data, including Eigenvalues, Decorana_values and Axis_lengths. If the maximum value of Axis lengths in the first four axes of DCA ranking results exceeds 4, it is more appropriate to choose single peak model ranking (CCA analysis); If it is less than 3, it is better to choose linear model (RDA analysis); If it is between 3 and 4, both the unimodal model and the linear model are feasible.

B. Sample coordinate table: records the position of samples in each dimension

C. Response variable coordinate table: records the position of response variable in each dimension

D. Explanatory variable coordinates: records the positions of explanatory variables in various dimensions

E. explanatory variable correlation table: two columns of data represent cosine value of included angle between explanatory variable arrow and sorting axis, indicating correlation between explanatory variable and sorting axis.

Results Display      (Click " task ID" to view different analysis results)