Home > Bio Information > DNA Chip Analysis Software     It is easier to understand the information about quality of raw data through statistical value and various Plotting. Also it provides pre-management of data that is used for analysis. - One-dye chip data importing: ABi chip, etc. - Two-dye chip data importing: GenePix   format (.gpr), ImaGene format, etc. - Box plot, MA plot, Histogram, Correlation   Scatter plot - Normalization for one-dye case:   Global shift, Lowess normalization,   Quantile normalization - Normalization for two-dye case: Global   normalization, Global Lowess, Block   Lowess - Convenient Gene Expression Matrix   Generation   Various statistical applications are available such as basic fold change, and comparison between 2-class or even Multi-class. Also it is able to compare and analyze the results by Venn Diagram and Volcano Plot etc. - Fold Change: One-dye/Two-dye case - Parametric Test for 2-class comparison:   Welch’s T-test, Z-test - Nonparametric Test for 2-class   comparison: Mann-Whitney test - Paired Test: Paired T-test, Wilcoxon signed   rank test - Parametric Test for multiclass comparison:   ANOVA - Nonparametric Test for multiclass   comparison: Kruskal-Wallis H-test - Multiple Test Correction: Bonferroni,   Holm’s procedure, etc. - Volcano Plot in powerful interactive mode - Venn Diagram for result combination - Sample Correlation Plot with differentially   expressed genes   It provides various clustering methods and visualization, is also able to predict the validation related to clustering results and the number of first stages of Cluster - Hierarchical Clustering with useful Linkage methods - Dendrogram with diverse graphic options for publication - K-means Clustering - SOM (Self Organizing Map): U-matrix, 2D Topographic   Profiling - Statistical Clustering Validation   In the feature selection, the user is able to analyze and distinguish the test data by accuracy measurement of analysis. The Whole Computation method enables the user to analyze faster by a more accurate analysis prediction. - Feature Selection: BSS/WSS, two-sample t-test, DEG finding   tests - Classifier: weighted KNN, Nearest Centroid, Fisher’s LDA, etc. - Error Estimation: Cross Validation, Bootstrap, etc. - Whole Computation: All three processes just by one-click! - Powerful Visualization: Sample PCA (Principal Component   Analysis)   The gene lists from differentially expressed genes, gene clusters, marker genes can be further studied using biological annotation and pathway information. The interrelationship among genes can be explored via KEGG pathway information. This mapping of genes onto pathways offer an integrated view of gene interactions. - Pathway Search: given gene lists, all related KEGG pathways explored - Pathway Mapping: mapping genes onto pathways - Up-/down-regulation display with heatmap - User-defined numerical setting for up-/down-regulation - User_defined coloring setting for up-/down-regulation - Useful display for time series experiments