Tutorials/Use Case Scenarios

Tutorial 1: Gene Expression During Rat Spinal Cord Development

This tutorial covers data import and transposition, normalization, renaming experiments, K-Means clustering, matrix tree, centroid, and cluster plots, generating experiment and workflow reports, and exporting images.

Tutorial 2: Analysis of NCI60 Data

This tutorial covers importing and preprocessing data, renaming datasets, estimating missing values, agglomerative hierarchical clustering, matrix tree plots, color matrix plots, resizing and customizing plots, and generating reports.

Tutorial 3: Jarvis-Patrick Clustering

This tutorial covers estimating missing values, normalization, performing Jarvis-Patrick clustering analysis on the datasets from the first two tutorials, and displaying data in a matrix tree plot.

Tutorial 4: Self-Organizing Maps (SOMs)

This tutorial covers importing data, using the table viewer, the summary statistics chart, value removal, filtering, normalization, using Self-Organizing Maps to cluster Leukemia data, visualizing SOM results in a SOM plot and in a cluster plot.

Tutorial 5: Principal Component Analysis (PCA)

This tutorial demonstrates how to use Principal Component Analysis as a method of extracting more information from data. The tutorial covers data import and displaying PCA results in various plots including: scree, loadings line, color matrix, score (raw and normalized) and 3D score (raw and normalized) plots.

Sample Workflow Using Spotted Array N-Fold Culling With Log Transformation

This workflow is used for ratio (Cy3/Cy5) data to filter out genes that do not show a large induction or repression in any sample in the dataset, and then to log normalize the data so that inductions and repressions have equal but opposite sign.

Platinum Tutorial 6: Learning to Distinguish Cancer Classes

This tutorial demonstrates how to train GeneLinker™ Platinum's artificial neural networks ANNs) to distinguish between sample classes. As an example, data on four similar tumor types is studied. Program features covered include importing variables, the SLAM™ association-mining technology (algorithm and viewer), creating gene lists for filtering, filtering, classification, and classification plots.

Platinum Tutorial 7: IBIS Classification

This tutorial demonstrates how to search for a gene to use as an IBIS classifier. One IBIS classifier is produced using Linear Discriminant Analysis (LDA) and a second is produced using Quadratic Discriminant Analysis (QDA). An IBIS Gradient plot is used to analyze the results of the classifier creation.

Tutorial 8: Affymetrix Data

This tutorial demonstrates how to use Affymetrix data in GeneLinker™.

Platinum Tutorial 9: Support Vector Machines

This tutorial demonstrates how to train GeneLinker™ Platinum's committee of SVMs to distinguish between sample classes. As an example, data on two leukemia types is studied. Program features covered include importing variables, creating learners, classification, and classification plots.