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
values can be NA but not blank, and imputed automatically
small data (copy and paste):

large data (upload tab-delimited txt file)

Optional
Figure size
figure width:
figure height:

Fontsize and label
point size:
point label fontsize:
axis label fontsize:
ticks number fontsize:
legend title:
legend title fontsize:
legend text fontsize:

Colors (10 user-defined, 20 system default colors)
color1:
color2:
color3:
color4:
color5:
color6:
color7:
color8:
color9:
color10:

Add label


Add ellipse or others (note: ellipse needs at least 4 points)


Fontfamily


Principal components analysis (PCA)

Introduction
PCA is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
Input data instructions
Features (e.g. genes) in rows, Sample in columns. The first row is sample names, the second row is group names (not pure integers), and the other rows are values. PC1, PC2 in the output figure are the first, and the second principal components (degree of explainary of latent variables to differences). ref: prcomp for PCA calculation, FactoMineR R package for plotting
center and scale by default.
Paper example
[Nature communications] Sympathetic axonal sprouting induces changes in macrophage populations and protects against pancreatic cancer. Fig4h
Input sample data
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