 |
| |
 |
| |
 |
|
 |
 |
 |
Import Data
Easy importing, Most array platforms and
scanner formats (CodeLink, AB, Agilent,
CombiMatrix, GPR, ImaGene, ScanAlyze,
QuantArray, Affymetrix, etc |
| |
|
|
 |
|
Visualization
Powerful and rich data visualization framework
(3D,2D scatter plots,Profile Plot, Hear Map,
Histogram, Profile Match, Spot Image, Annotation
View, Matrix, Bar Chart, Summary Statistics),
Filtering, A Python-based scripting ability |
|
|
|
|
 |
|
Statistical
analysis
Normalization, Paired/unpaired T-test,
Permuation test, Mann Whitney test,
1(2)-way ANOVA, Multiple group analysis, Repeated
measures, Multiple testing, Filtering based on p-value,
Fold -change, FDR, PCR |
| |
|
|
 |
|
Cluster
analysis
Clustering algorithms like K-means, Hierarchical,
EigenValue, Self organizing Maps(SOM),
Random Walk , Principal Components
Analysis(PCA) and variety Distance function like
Euclidean, Square Euclidean, Manhattan,
Chebychev, Differential, Pearson absolute and
Pearson Centered |
| |
|
|
 |
|
Classification
& prediction
Powerful tools that allow researchers to exploit
microarray data for learning-based prediction of
outcomes of gene expression. Four powerful
machine learning algorithms like Support Vector
Machine (SVM), Decision Tree (DT), Neural
Network (NN) and Naive Bayesion (NB) to
classify samples or genes into discrete classes
|
| |
|
|
 |
|
Gene annotation
Real-time gene information updating from web
sources, Chromosomal Lacation and
Pathway Views , Gene Ontology(GO) Browsing,
GO cluster (hierarchies), PubMed Correlation
Matrix |
|
 |
|
| |
|
