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Finding the biological meaning for gene clusters
The Pathway Analysis consists of different steps: Data preprocessing, Discriminatory Genes Analysis or Cluster Analysis, biological annotation analysis, and signaling network analysis.
Annotation enrichment analysis
Based on cluster or discriminatory genes analysis, the annotation enrichment analysis finds groups of coregulated genes that share biological functions or characteristics, such as:
• Involvement in the same pathways
• Neighbouring chromosome location
• Correlation with the same disease or cellular process
• Sharing common protein motifs
The proprietary TreeRanker Software identifies significantly enriched annotations within a set of genes; the respective genes can be further analyzed with the signaling network analysis.
Signaling network analysis
Signaling network analysis visualizes if and how the genes of interest or their protein products could interact. Common direct regulators or downstream targets can be detected, and activation or inhibition is distinguished. This analysis is based on biomedical literature using the PathwayArchitect™ Software, Stratagene, for pathway analysis and visualization.
Next to algorithmic aspects, the available data sources are crucial for successful pathway analysis. Partly, data are imported from commercially and publicly available databases. Further proprietary data sources have been created by Miltenyi Biotec´s bioinformatics specialists. All data sources are continually updated.
The minimum requirement for bioinformatics services is data of four microarray experiments (sample vs. control). Please supply the expression data in compliance with the requirements described in the Bioinformatics Services file format info sheet. |
| Package |
Data preprocessing
- Missing value review
- Data inconsistency review/ outlier detection
- Filtering of genes without adequate data support
- Filtering of unregulated genes
- Log transformation of ratio data
- Median centering over samples and genes*
Determination of the top discriminatory genes for two classes of samples with SAM or t-test/ANOVA*
Annotation of the top discriminatory genes with functional and disease-related information, if available
Hierarchical clustering of samples and genes for one setting*
Annotation analysis for the most prominent cluster in the cluster tree
Discussion of the results for three outstanding clusters*
Signaling network analysis for these three clusters*
PathwayArchitect™Software, Stratagene
Written report detailing procedures and most significant findings
Supplementary CD-ROM, content dependent on performed analyses:
- Report in PDF format
- Heatmap coded file of processed data
- Top discriminatory genes table
- Table incl. functional annotation for all genes
- Cluster tree
- Annotation results for each analyzed cluster
- Up to three BIN images
Bioinformatics consulting service by bioinformatics scientist (2 hours)
*if applicable |
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| Figure 1 |
Signaling network analysis
The network analysis shows known interactions and biological processes for the genes of interest. In addition, this method can find regulators or targets that were not present on the microarray. |
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| Figure 2 |
Signaling network analysis
The example shows the genes of interest (green circle) and their interacting molecules. The mode of interaction is illustrated by the connecting lines between the molecules.
A blue line depicts binding, while a blue arrow stands for the regulation of gene expression. Grey arrows with green boxes represent regulation in general and grey arrows with orange circles indicate protein modifications. |
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