1. Gene expression analysis
Tutorial 7
Gene expression analysis

2. Gene expression analysis
Expression data
GEO
UCSC
ArrayExpress
General clustering methods
Unsupervised Clustering
Hierarchical clustering
K-means clustering
Tools for clustering
EPCLUST
Mev
Functional analysis
Go annotation

3. Gene expression data sources
Microarrays
RNA-seq experiments

4. Expression Data Matrix
Gene 1
-1.2
-2.1 -3
-1.5
1.8
2.9
Gene 2
2.7
0.2
-1.1
1.6
-2.2
-1.7
Gene 3
-2.5
1.5
-0.1 -1
0.1
Gene 4
2.6
2.5
-2.3
Gene 5
2.2
Gene 6
-2.9
-1.9
-2.4
Each column represents all the gene expression levels from a single experiment.
Each row represents the expression of a gene across all experiments.

5. Expression Data Matrix
Gene 1
-1.2
-2.1 -3
-1.5
1.8
2.9
Gene 2
2.7
0.2
-1.1
1.6
-2.2
-1.7
Gene 3
-2.5
1.5
-0.1 -1
0.1
Gene 4
2.6
2.5
-2.3
Gene 5
2.2
Gene 6
-2.9
-1.9
-2.4
Each element is a log ratio: log2 (T/R).
T - the gene expression level in the testing sample
R - the gene expression level in the reference sample

6. Expression Data Matrix
Black indicates a log ratio of zero, i.e. T=~R
Green indicates a negative log ratio, i.e. T<R
Grey indicates missing data
Red indicates a positive log ratio, i.e. T>R

7. Different representations
Microarray Data:
Different representations
T>R
Log ratio
Log ratio
T<R
Exp
Exp

8. How to search for expression profiles
GEO (Gene Expression Omnibus)
Human genome browser
ArrayExpress

10. Searching for expression profiles in the GEO
Datasets - suitable for analysis with GEO tools
Expression profiles by gene
Probe sets
*further curated= statistically comparable datasets
Microarray experiments
Groups of related microarray experiments

11. Clustering
Download dataset
Statistic analysis

12. Clustering analysis

13. Clustering
Download dataset
Statistic analysis

14. The expression distribution for different lines in the cluster

15. Searching for expression profiles in the Human Genome browser.

16. Keratine 10 is highly expressed in skin

17. ArrayExpress

22. How to analyze gene expression data

23. Unsupervised Clustering - Hierarchical Clustering

24. Hierarchical Clustering
genes with similar expression patterns are grouped together and are connected by a series of branches (dendrogram). 2 1 3 4 5 6 1 6 3 5 2 4
Leaves (shapes in our case) represent genes and the length of the paths between leaves represents the distances between genes.

25. How to determine the similarity between two genes? (for clustering)
Patrik D'haeseleer, How does gene expression clustering work?, Nature Biotechnology 23, (2005) ,

26. Hierarchical clustering finds an entire hierarchy of clusters.
If we want a certain number of clusters we need to cut the tree at a level indicates that number (in this case - four).

27. Hierarchical clustering result
Five clusters

28. Unsupervised Clustering – K-means clustering
An algorithm to classify the data into K number of groups.
K=4

29. How does it work? 1 2 3 4
The centroid of each of the k clusters becomes the new means.
k initial "means" (in this casek=3) are randomly selected from the data set (shown in color).
k clusters are created by associating every observation with the nearest mean
Steps 2 and 3 are repeated until convergence has been reached.
The algorithm divides iteratively the genes into K groups and calculates the center of each group. The results are the optimal groups (center distances) for K clusters.

30. How should we determine K?
Trial and error
Take K as square root of gene number

31. Tools for clustering - EPclust

38. In the input matrix each column should represents a gene and each row should represent an experiment (or individual).
Hierarchical clustering
Edit the input matrix: Transpose,Normalize,Randomize
K-means clustering

39. In the input matrix each column should represents a gene and each row should represent an experiment (or individual).
Hierarchical clustering

40. Data
Clusters

41. In the input matrix each column should represents a gene and each row should represent an experiment (or individual).
K-means clustering

42. Samples found in cluster
Graphical representation of the cluster
Graphical representation of the cluster

43. 10 clusters, as requested

44. Tools for clustering - MeV
Multi experiment viewer

45. Gene expression function analysis
1007_s_at
1053_at
117_at
121_at
1255_g_at
1294_at
1316_at
1320_at
1405_i_at
1431_at
1438_at
1487_at
1494_f_at
1598_g_at
What can we learn from clusters?

46. Gene Ontology (GO)
The Gene Ontology project provides an ontology of defined terms representing gene product properties. The ontology covers three domains:

47. Gene Ontology (GO)
Cellular Component (CC) - the parts of a cell or its extracellular environment.
Molecular Function (MF) - the elemental activities of a gene product at the molecular level, such as binding or catalysis.
Biological Process (BP) - operations or sets of molecular events with a defined beginning and end, pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms.

48. The GO tree

49. GO sources ISS Inferred from Sequence/Structural Similarity
IDA Inferred from Direct Assay
IPI Inferred from Physical Interaction
TAS Traceable Author Statement
NAS Non-traceable Author Statement
IMP Inferred from Mutant Phenotype
IGI Inferred from Genetic Interaction
IEP Inferred from Expression Pattern
IC Inferred by Curator
ND No Data available
IEA Inferred from electronic annotation

50. Search by AmiGO

51. Results for alpha-synuclein

52. DAVID http://david.abcc.ncifcrf.gov/
DAVID 
Functional Annotation Bioinformatics Microarray Analysis
Identify enriched biological themes, particularly GO terms
Discover enriched functional-related gene/protein groups
Cluster redundant annotation terms
Explore gene names in batch 

53. annotation
classification
ID conversion

54. Functional annotation
Upload
Annotation options

57. Gene expression analysis
Expression data
GEO
UCSC
ArrayExpress
General clustering methods
Unsupervised Clustering
Hierarchical clustering
K-means clustering
Tools for clustering
EPCLUST
Mev
Functional analysis
Go annotation