GESS
- GENE EXPRESSION STATISTICAL SYSTEM FOR MICROARRAYS
Purchase the
latest edition of GESS.
GESS 2006 makes statistical analysis of microarray gene
expression data fast and easy. With this well-documented package,
you will not have to worry about complex programming or confusing
statistical output. Because this software was developed by a company
with over 20 years of experience creating commercial statistical
software, you will be confident with your results.
GESS is a standalone system. Although it is
integrated with NCSS, you do not have to own NCSS to
run it. You can use it with any statistical software you want.
GESS comes with electronic (pdf) manuals that contain
tutorials, examples, annotated output, references, formulas, and
complete instructions on each procedure. And, if you cannot find an
answer in the manual, our free technical support staff (which
includes three Ph.D. statisticians) is available.
Choose
GESS. It's more comprehensive, easier-to-use, accurate,
and less expensive than any other microarray statistical analysis
program on the market.
Accuracy
We at NCSS have put a great deal of
effort into finding the most accurate algorithms possible. The
programs have been tested and verified over and over, both by us and
by our customers.
Simple File Organization
Expression files are organized
using the built-in spreadsheet, making it simple to specify and
analyze related factors.
Array Quality and Filtering
Graphical and numeric
summaries, designed to quickly alert the user to artifacts, bad
spots, spatial anomalies, or poor arrays, are easily obtained for
each array. Expression values may be filtered according to
well-described criteria. Several normalization procedures are also
on hand.
Statistical Analysis
A wide variety of statistical
analyses is available. Each technique is well-documented and
described in easy-to-understand terms. Graphical summaries of
statistical output help interpret the results. Follow-up statistical
analyses are obtained with over 150 statistical procedures in
NCSS.
Supported
Plaforms
Affymetrix®
GenePix®
Agilent®
Two-Channel
Files
Expression Data Files
Affymetrix®
Arrays
RMA Expression Algorithm
Gene List
Subsets
Chip Quality Summaries
Background
Correction
Quantile Normalization
Comparative Box
Plots
Spatial Anomaly Plots
Two-Channel
Arrays
Whole-Array Box Plots
Print-tip Box Plots
MA
Loess Plots
Spatial Anomaly Plots
Spot QC Summaries
Array
QC Summaries
Weak Signal Filters
Background Filters
Loess
Normalization
Gene List Subsets
Subset Box Plots
Dye-Swap
Compatible
Statistical Analyses
Fold-Change Analysis
Paired
T-Tests
One-Group T-Tests
Two-Group T-Tests
One-Factor
ANOVA
Two-Factor ANOVA
Repeated Measures ANOVA
GLM
ANOVA
Analysis of Covariance
Multiple Regression
Cox
Regression
Logistic Regression
Principal Components
Analysis
Hierarchical Cluster Analysis
Multiple Testing
Correction
Bonferroni
False Discovery
Rate
Hierarchical Clustering
Eight Clustering
Methods
Three Scaling Methods
Double Dendrograms
Custom
Heat Maps
Data Utilities
Save Data to
Spreadsheet
File Description
Export to TXT
File
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