Pine Biotech example of applying unsupervised methods of differentially expressed genes in alzheimer patients vs. control. The results point to active genes and motifs related to known alzheimer functional pathways.

1. Case Study: Unsupervised Analysis of
Gene Expression data.
Data from http://www.ncbi.nlm.nih.gov/pubmed/19361613

2. Microarray Gene Expression Data: Confirmed Pathogological diagnosis on late-onset
Alzheimer Disease (188 controls, 176 cases)
Unsupervised Method to Identify Important Genes applied
to Gene Expression in Alzheimer Disease
(data from: http://www.ncbi.nlm.nih.gov/pubmed/19361613)
MV
Genes that define the
gene expression
specificity of patients
Uniformly distributed
(“random”) genes
Viral titer
linked genes
VT
MV
Genes that define the
gene expression
specificity of patients
Uniformly distributed
(“random”) genes
Viral titer
linked genes
VT
Schematic representation of the algorithms main idea

3. Ttest values for AD/control differentiation: by individual network modules (clusters)
Co-expression network methods can be successfully applied to
mechanisms of complex diseases, such as Alzheimer's disease
and diabetes. We developed a novel co-expression network
approach that is robust under a series of tests for valida@ng
sta@s@cally significant network modules. A set of gene
expression data of Alzheimer's disease produced co-
expression network modules (clusters) that are associated
with the disease.
Ttest%values%for%AD/control%%differen5a5on:%
by%individual%network%modules%(clusters)%
Co-Expression Network of Genes and its Modules
Cluster 40 (highest peak) identified from unsupervised analysis is
used for further analysis
Network Modules (Clusters) with high T-test values for differentiation between Alzheimer and control patients
The integra@on approach can be illustrated by analysis of the Alzheimer gene expression microarray data (but equally can be applied to other highthroughput data). The approach generates a network of gene co-expression, and selects the network modules (clusters
of co-expression) that differen@ate between AD and control pa@ents. Several network co-expression modules (clusters 40, 44, and 47 below -- 188 genes together) are highly differen@a@ng:

4. DAVID Functional Annotation Chart: detected enriched classes of genes
Enrichment Score:1.3= non log scale of 0.05- so interest is in higher than 1.3, only one group of genes were significant by enrichment
% is Number of genes involved in given term is divided by the total number of user's input genes, i.e., percentage of user's input gene hitting a given term.
Annota8on Term # mapped genes P-value Benjamini
Zinc Finger Regions: C2H2- type 9 10 9.80E-05 3.10E-02
Zinc Finger Regions: C2H2- type 11 9 9.90E-05 1.60E-02
Zinc Finger Regions: C2H2-type 8 10 2.10E-04 2.20E-02
KRAB (Krueppel-associated box) 9 2.30E-04 1.80E-02
Zinc Finger Region : C2H2- type 10 9 2.40E-04 1.50E-02
Zinc Finger Region : C2H2- type 12 8 2.50E-04 1.30E-02
KRAB (Krueppel-associated box) 9 4.20E-04 7.60E-02
Zinc Finger Region : C2H2- type 6 10 6.70E-04 3E-02

5. KRAB-Zinc Finger Protein’s could play a crucial role in
Alzheimers, as its has been associated with the onset of
the disease.
Shulman JM, Chibnik LB,Aubin C, Schneider JA, Bennett DA, De
Jager PL. Intermediate Phenotypes Identify Divergent Pathways to
Alzheimer’s Disease. Domschke K, ed. PLoS ONE.
2010;5(6):e11244. doi:10.1371/journal.pone.0011244.
C2H2 Zinc Fingers function in a variety of ways,
including: DNA-binding domains Protein-Protein
Interactions Regulation of gene expression in the central
nervous system Important role in brain development
Gower-Winter SD, Levenson CW. Zinc in the central nervous
system: From molecules to behavior. BioFactors (Oxford, England).
2012;38(3):186-193. doi:10.1002/biof.1012.
Coexpressed KRAB-Zinc Finger C2H2 genes are associated with Alzheimer disease

6. Summary:
Further literature review can lead to better biological
interpretation.At the same time, understanding what role these
genes play in Alzheimers in this specific example can remain
challenging and requires further studies.
Aims: Unsupervised Method (clustering) between those with late-
onset Alzheimer’s disease and control
Results: Cluster 40 identified 138 unique genes whose expression
clustering demonstrated link with Alzheimers disease
Using Gene Ontology (Panther), DAVID, and KEGG , numerous
genes and pathways were associated with Alzheimer’s disease,
specific mention of Zinc Finger (C2H2 KRAB - Krueppel associated
box) gene enrichment is interesting.
A Microarray Gene Expression Data: Confirmed Pathological
diagnosis on late-onset Alzheimer Disease (188 controls, 176 cases)