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.
This tutorial demonstrates how to train GeneLinkerô 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 SLAMô 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 GeneLinkerô.
Platinum Tutorial 9: Support Vector Machines
This tutorial demonstrates how to train GeneLinkerô 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.