BioQUEST / SCALE-IT Module From Omics Data to Knowledge Case 1: Microarrays Namyong Lee Minnesota State University, Mankato Matthew Macauley Clemson University.

BioQUEST / SCALE-IT Module

From Omics Data to Knowledge Case 1: Microarrays

Namyong Lee Minnesota State University, Mankato

Matthew Macauley Clemson University

Sumona Mondal Clarkson University

Fusheng Tang University of Arkansas, Little Rock

Goals

Provide a guideline for teachers in different disciplines to explore different -omics data.

The instructor will guide the students through a tutorial of the experimental process, including: data retrieval, statistical design and analysis, biological analysis, and model validation.

Module Outline

1. Introduce Microarray and RNAseq technology.

2. Locate available public expression data

3. Formulate questions from the dataset.

4. Design computational and statistical experiments.

5. Interpret biological significance of identified genes. (UniProt, IntAct, and Reactome will be used.)

6. Validate the biological model (using ATLAS).

Step 1: Introduce gene expression and microarray and RNAseq technology.

How is gene expression measured?

Introduce microarrays and RNAseq. Compare and contrast these two.

What is gene expression?

Step 2: Locate available public expression data

ArrayExpress is a database of gene expression and other microarray data at the European Bioinformatics Institute (EBI)

www.ebi.ac.uk/arrayexpress/

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Sample data set (from EBI ArrayExpress)

Obtaining data; an example

• Go to ArrayExpress and search “colon cancer.”• Select Accession E-GEOD-42368, titled “p53-

dependent regulation of gene expression following DNA damage” for Homo sapiens.

• Download the processed data as a zip file. • Create a spreadsheet (e.g., Excel) and copy over

the data into it, one column per sample. • Each column should have an ILMN_ID number,

and then for each sample, an expression level and p-value.

• Organize the data by increasing p-values.• Use david.abcc.ncifcrf.gov/ to locate gene

names from ILMN_IDs.

Preprocessing

Why Preprocessing?: The data may have non-biological variation in the standardized data.

Thresholding Scaling (log transformation) Standardize Normalization (Quantile Normalization) Reducing the data set (by pairwise t-test)

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Were there genes whose expression profiles were correlated with colon cancer?

If so, how can we accurately determine which of the samples are cancerous based entirely on gene expression profiles?

Can any subtypes be identified by cluster analysis across samples ?

Step 3: Formulate questions about the data

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Class Prediction: Develop a multi-gene predictor of class label for a sample using its gene expression profile. (pairwise t-test)

Class Discovery: Use a various clustering algorithms to discover clusters among samples and genes. (K-means, hclust, PAM,…)

Step 4: Computational and statistical experiments with R & Bioconductor

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Hierarchical Clustering Results

Over expressed in cancer tissues

Over expressed in normal tissues

Gene 187 (Hsa.9972)

Step 5: Model for Cancer Therapy

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NCEH1 20X

ABCBs 2~3X

ABCB7 10X

Down-regulation of NCEH1 blocks cancer development?

Step 6: Validation of Model

Search PubMed for NCEH1 and cancer

http://www.ncbi.nlm.nih.gov/pubmed/17052608

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Thank you!