PCOA     More powerful tools, please visit (Dynamic principal coordinate analysis)





Select a file  Example

Select a file  Example


Select a file   Example









  Email me when task completed.



Function:
PCoA (principal co-ordinates analysis) is a means of visualizing the level of similarity of individual cases of a dataset.


Input:

(1) The input file can be abundance table(OTU,species,funtion abundance table)or similarity distance matrix

(2)Grouping file is optional. The tool supports no more than 2 grouping information in a single sample.

(3) The input file should be a tab-delimited “.txt” file


Parameters:

(1)Dot size:Dot size: define the dots size(the default value is 3)

(2)Group name:define the group name

(3)Dot transparency:define the dots transparency if case of overlapping of dots


Output:

PCoA_sites.xls:document dot sites in a PCoA plot.


PCoA_eig.xls:document the sum of all eigenvalues and importance of each axis.


pcoa_plot.png/pdf: PCoA plot of PCo1-PCo2, PCo1-PCo3 and PCo2-PCo3.


Tool machinery:

(1)If importing an OTU table, the tool will first normalize each OTU in the relative abundance, then calculate sample similarity distance of Bray-Curtis by R. Finally, PCoA is performed in R and plotted using ggplot2 software.

(2)If importing similarity distance matrix (e.g. Unifrac distance), the PCoA tool will calculate and plot a chart directly from the input matrix.

例子文件:

Abundance table    Grouping file1    Grouping file2

Output:


生物云平台 生物云平台 生物云平台