Read before use
1, check data with precheck (windows version) tools
2, data from excel, copy and paste data into the input frame
3, data from txt, must tab-seperated, copy and paste data into the input frame
4, specieal and non-English characters such as #, <, >, %, (, ), α are not friendly
5, use point as decimal separator, not comma. e.g. 3.14, not 3,14 as pi
Required
small data (copy and paste)

large data (upload tab-delimited txt file, english name, < 20M)

Optional
Figure size
figure width:
figure height:

Fontsize
axis label fontsize:
axis ticks fontsize:
legend title fontsize:
legend text fontsize:
feature name fontsize:

Colors, 10+ with default colors
color 1:
color 2:
color 3:
color 4:
color 5:
color 6:
color 7:
color 8:
color 9:
color 10:

sample point size:

Ellipse
68% confidence interval
note: if only 2 samples within a group, you can not add ellipse


Circle (correlation)


Fontfamily


Principal components analysis (PCA)

Introduction
Principal components analysis, PCA. Plotted by ggbiplot
Input data instructions
samples are in columns, features are in rows. The first row is sample names (unique), the second row is group names, other rows are data values.
PC1 and PC2 is the first and the second Principal components (explainary extend of latent variable to the differences). Points represent samples, different colors represent different groups. Ellipses represent 68% confidence intervals of core regions. Arrows represent original variables, the directions of arrows represent correlation between original variable and principle components, lengths represent devotion of original data to principle components.
Paper example
Input Examples
Output

1) How to plot?
1, Put data in excel according to the example format.
2, Copy and paste into input frame.
3, Input pre-checking button to check input
4, After checking pass, select parameters, submit and download

2) How to cite?
4000+ papers in (Google Scholar)
Tang D, Chen M, Huang X, Zhang G, Zeng L, Zhang G, Wu S, Wang Y. SRplot: A free online platform for data visualization and graphing. PLoS One. 2023 Nov 9;18(11):e0294236. doi: 10.1371/journal.pone.0294236. PMID: 37943830.

3) FAQs