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procrustes





choosefile   example




choosefile   example




choosefile   example










add color





普氏分析英文说明文档

Function: A method used to analyze the correlation between two sets of data and compare the consistency of two sets of data, such as analyzing the relationship between microbial species composition and the environment.

 

Input:

1. The file must be a tab-separated .txt format file with a header. The name of the table consists of letters, numbers, and underscores, and no suffix name is allowed.

2. Input two sets of data for analysis in sequence. File 1 defaults to the main orthogonal axis data. The first row of the data table is sample information, the first column is specific parameters, such as species, genes, etc., and the second column is the abundance of species or genes in the corresponding sample.

3. Enter the grouping data. The first column of the data table is the sample name, and the second column is the group to which the sample belongs.

Examples:

File 1

 

File 2

 

Groups

 

 

Parameter:

1. File name: custom(English characters)

2. Dimensionality reduction method: PCA (suitable for data matrix with a large number of samples)/PCoA (suitable for data matrix with a small number of samples)

3. Point size: custom

4. Line thickness: custom

5. Picture title: custom(English characters)

6. Group color: custom (add the same color type as the number of groups)

 

Output: After the analysis is completed, a compressed folder of drawing results will be provided for download, which contains the result images in PDF and PNG formats.

 

Interpretation of the results:

1) Different colors in the picture represent different groups;

2) The point mapped on the main orthogonal axis is the sample point from File 1 PCA, represented by a solid circle, and the point mapped on the oblique orthogonal axis is the sample point from File 2 PCA, represented by a triangle

3) The line indicates the paired samples of the two, the length of the line segment is the residual value between the two, the shorter the line segment, the smaller the residual value;

4) M2 is the sum of the squares of the residual values. The smaller the M2 value, the better the consistency of the two sets of data. Use permutation test to calculate the M2 significance P value.

普氏分析英文例子说明

Input: Tab-delimited .txt format file with header

1) File 1: Sample—Environment. The first line is the sample name, the first column is the environmental factor, and the second column is the content of each environmental factor in each sample.file 1

 

2) File 2: Sample-Species, the first row is the sample name, the first column is the species name, and the second column is the abundance of each species in each sample.file 2

 

3) Group file: The first column is the sample name, and the second column is the group name.group file

 

 

Parameter:

1. File name: file 1 (environments), file 2 (species)

2. Dimensionality reduction method: PCA

3. Point size: 4

4. Line thickness: 1.5

5. Picture title: relationship

6. Group color: #FF0000, #0000ff, #ff6600

 

Output:

The result file is compressed, and two types of result graphs with sample label (procrusters.lab) and without label (procrusters) are output at the same time, including PDF and PNG formats, and the other files are OS platform source files.

 

 

 

Interpretation of the results:

1) Different colors in the picture represent different groups;

2) The point mapped on the main orthogonal axis is the sample point from the environmental variable PCA, represented by a circle, and the point mapped on the oblique orthogonal axis is the sample point from the PCA composed of species, represented by a triangle;

3) The line indicates the paired quadrat of the two, the length of the line segment is the residual value between the two, the shorter the line segment, the smaller the residual value;

4) M2 is the sum of squares of the residual values. The smaller the value of M2, the better the consistency of the two sets of data. From the graphical results, it can be seen that the potential relationship between the environment and the species shows better consistency (P< 0.05).