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Order a GGEbiplot license
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To see how user-friendly and
how powerful GGEbiplot is, try the
free test version of GGEbiplot. It is fully
functional but is limited to small (7 by 7)
datasets.
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To order GGEbiplot, fill
this Order Sheet
and send it to us
via email.
The software will be sent
to you, licensed to your name, within ONE day.
Below is a partial description
of the GGEbiplot functions. A relatively up-to-date description can
be viewed
online.
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Components
(First
year cost) |
Functionalities
More
advanced descriptions |
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Basic functions
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All possible models (due to different data
transformations, scaling, centering, and singular value partitions) for
a given two-way table, including GGE biplots and AMMI biplots.
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All functions necessary
for generating and visualizing a biplot;
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All options for
modifying the appearance of a biplot (changing font, size, style, and
color of the biplot and all its components), various ways of labeling
the entries and testers (by full names, partial names, indices, any
symbols), show/hide the entries or testers, etc. (See
links in
View
and
Format);
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Conventional
analysis including ANOVA, pair-wise comparison, correlation, and
stability analysis; Numerical outputs recording the biplot analysis
process;
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Saving/printing/copying the biplot images into a graphical file (see
File);
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Data
plotting/diagnosis: plotting by name any two entries across testers or
any two testers across all entries ("Accessories");
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Two input formats
for two-way (e.g., genotype by environment) data;
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Reading data from
Microsoft Excel, Microsoft Access, or Comma-delimited text files;
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Formatting Entries
(row factors) and testers (column factors) for font size, style, and
color INDIVIDUALLY or in group (Format).
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A powerful
Breeder's Kit
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Real-time Data Manipulation |
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Allows biplotting
transposed data (making it easy for data input and resulting in 3
additional multiplicative models)
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Allows biplotting of
the balanced subset or partially balanced subsets
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Allows biplotting any
subset of the original data by user-specified deleting of any number of
the entries and/or testers
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Go back to a previous
subset or the original data
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Entry and tester stratifications
based on vector length, mean value, or biplot position.
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4 Missing data
filling options.
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Adding derived
variables
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3-way
data input and visualization |
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Direct
input and biplot analyses of genotype by environment by trait three-way
data,
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Generating biplots
of all possible two-way tables by selecting any, some, or all of the
environments, genotypes, or traits.
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GGE biplots of any of
the traits
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Genotype by trait
biplots in one, some, or all of the environments
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Trait
relationships based on genetic, environmental, and phenotypic data
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Genotype classification treating each
environment-trait as a different variable
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Other types of biplots
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4-way
data input and visualization |
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This adds another
dimensional over the three-way data functions. Therefore it makes the
3-way data functions many times more handy
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Inputs
year-location-genotype-trait four-way data from a single file and allows
generation and visualization of all possible two-way tables through
selecting any, some, or all of the years, locations, genotypes, or
traits. In addition to biplot analysis, this module also serves a
powerful data manipulation/sub-setting tool.
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Multi-way data
input and ANOVA |
- Read data from a spreadsheet of any format as soon as the traits
are in parallel columns. It can be 3-wy, 4-way, or any way, and the
columns of the factors (year, location, site, others) do not have to
follow an order.
- Conduct ANOVA for any number of the factors, across year, across
locations, or by year, by location.
- LSD is calculated and entries ranked on there Mean values divided
by LSD, which is a superior way to visually compare multiple entries.
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Variable by variable
(V-V) biplot
analysis |
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Applicable to studies
aiming at understanding the relationships between and within two sets of
variables: response variables vs. explanatory variables.
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QTL identification
using multi-environment data; visualizing QTL main effect and specific
QTL by environment interactions, which naturally leads to the
development of marker based selection strategies specific to different
mega-environments.
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Interpretation of
genotype-by-environment interactions using genetic covariables
(explanatory traits, genetic markers, QTL, genes, etc.)
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Conduct congruence
analysis, i.e, quantifying the similarity between patterns of two
matrices or biplots.
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Biplot assisted Association Analysis,
including correlation and Multiple Regression |
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Identifies associated
variables (e.g. markers/traits) for any particular variable (e.g. marker
or trait) at user specified significant level with/without removing the
effects of one or more covariables (covariate analysis).
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Any of the variables
can be specified as a dependent variable, independent variable, or
covariable.
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This function can be
used to identify 1) linked markers, 2) associated traits, 3) QTL
identification, 4) pleiotropic effects of a genetic locus, and 5)
removing irrelevant variables/markers prior to multiple regression - the
last point is essential when there are too many independent variables
relative to the number of observations.
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Results
displayed both graphically (in a biplot) and numerically.
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Conduct multiple
regression using any of the variables as the dependent variable and
others as independent variables;
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Delete
independent variables from the multiple regression model at the user’s
(visual) judgment or based on log ratio (a technique that has been used
in QTL mapping), which removes variables that are only indirectly
associated with the dependent variable;
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Generates linear
regression model with interaction (digenic epistasis) terms and/or
quadratic terms;
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Results
displayed both
graphically (in a biplot) and numerically.
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3D biplot viewer |
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Visualize a constantly rotating 3D biplot;
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Adjust rotation speed;
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Freeze image to output an interesting pattern;
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Rotate any of the three axes while holding the other
two;
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Visualize 2D biplots of PC1 vs PC2, PC1 vs PC3, and PC2
vs PC3; and
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Save the current view of the 3D biplot into a graphic
file, in color or in black-white
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Show/hide testers/entries.
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Show/Hide the vectors.
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Essential for
understanding patterns for large datasets
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