Training

Introduction to Microarrays and Data Analysis using Rosetta Resolver

This course teaches the introduction to Microarray technology and the use of Rosetta Resolver software to carry out data analysis.

The first part of the course covers the basic concepts and introduction of Microarrays & technologies and the second part of the course is concentrated on workshop. During the workshop the following Rosetta Resolver topics are covered using an example data set. Some of the topics may not be covered because of time constraint.

Importing data from microarrays and related information such as pattern (layout of the chips) and annotation. Revolver concepts, Normalization and p-value.
Assessing the quality of data and determining whether to include data in downstream processing and analyses.
Implementing Experimental Design using the tool Experimental Definition.
Performing analysis and displaying up regulated or down regulated genes in the form of plots or tables. Comparing and Co-relation of profiles, Filtering data points using p-value and fold change.
Performing Trends: A trend plot can help to identify trends, such as the effects of drug over time or the results of increased drug concentration. The trend viewer is a tool that can be used to view multiple sequences across multiple experiments.
Finding Similar Data using the following tools.
1. ROAST: The ROAST feature is similar to BLAST in that one can use to perform similarity searchers to find similar transcriptional response data.
2. GROW: Grow is to identify genes or experiments that are similar to see pattern. It allows one to use multiple query items and search for both similar experiments and sequences simultaneously.
Clustering: Clusters are performed to make quantitative comparisons in one dimension and two dimensions. The results are displayed in the form of heat maps.
Classifying Data: Classifiers help one to combine prior knowledge with observed data. The algorithms help to classify objects such as experiments or genes. The following classifier algorithms are available:
1. Bayesian classifier
2. Prognostic classifier
3. k-NN
Principal Component Analysis (PCA): PCA is a mathematical procedure that transforms a number of variables in gene expression profiles into a number of uncorrelated variables called principal components. The basic goal of PCA is to reduce the dimension of the data.
Statistical Tests: Statistical tests are performed using ANOVA, Student’s t-test and Wilcoxon test on experiments or profile to determine whether any sequences exhibit mean expression intensities that are significantly different between groups of profiles.
Gene Ontology (GO) analysis: Displaying up regulated or down regulated genes into functional categories.
Pathway analysis: Using this tool one can analyze gene expression of components in the biological pathway or pathways of interest.

Students will learn about:

Introduction of microarrays and different technologies, concepts and error model in Resolver.
Uploading microarray data into Resolver and evaluating scan quality.
Experimental Design: Structure and planning the expressing analysis
Working with Expression Data, Analyzing Data and performing data set manipulation using the Groups Menu in Different Resolver viewers.
Analyzing Data for Trends, Clustering data, Classifying data and performing data reduction using principal component analysis.
Performing statistical tests and enriching analysis with biological information using GO ontology and pathways.