Clustering / PCA and Visualization
Introduction to Clustering
Clustering is used to group biological samples or genes into separate clusters based on their statistical behavior. The main objective of clustering is to find similarities between experiments or genes (given their expression ratios across all genes or samples, respectively), and then group similar samples or genes together to assist in understanding relationships that might exist among them.
Apply K-Means, Jarvis-Patrick, or agglomerative hierarchical clustering to your dataset, or perhaps try a Self-Organizing Map (SOM). The results of each clustering experiment is listed in the Experiments navigator under the dataset it was based on. Each experiment result item is tagged with an icon to indicate the experiment type.
Visualize the Clustering Experiment Results - GeneLinkerô has an extensive set of plots that can be used to visualize the results of clustering hopefully revealing interesting or significant patterns.
Introduction to Principal Component Analysis
Component Analysis is an unsupervised or class-free approach to finding the most informative or explanatory features in data. In particular, Principal Component Analysis (PCA) substantially reduces the complexity of data in which a large number of variables (e.g. thousands) are interrelated, such as in large-scale gene expression data obtained across a variety of different samples or conditions. PCA accomplishes this by computing a new, much smaller set of uncorrelated variables which best represent the original data. PCA is a powerful, well-established technique for data reduction and visualization. 2D and 3D PCA plots often place objects with similar patterns near each other.
Principal Component Analysis (PCA)
Apply PCA by genes or by samples. Again, the experiment results are listed in the Experiments navigator tagged with the PCA icon.
Visualize the PCA Results - GeneLinkerô offers a variety of 2D plots and a 3D Score plot to give a clear picture of the hidden structure in the data.