1. Gene expression

2. Gene Expression
DNA
RNA
protein

3. Gene Expression mRNA gene1 mRNA gene2 mRNA gene3 AAAAAAA AAAAAAA

4. Studying Gene Expression 1987-2013
cDNA Microarrays (first high throughput gene expression experiments)
DNA chips (High density oligonucleotide microarrays )
RNA-seq (High throughput sequencing)

5. Classical versus modern technologies to study gene expression
Classical Methods (Microarrays)
-Require prior knowledge on the RNA transcript
Good for studying the expression of known genes
High throughput RNA sequencing
Do not require prior knowledge
Good for discovering new transcripts ,
Good for studying splicing (alternative splicing events)

6. RNA-seq

7. What can we learn from RNAseq?
- Comparing the expression between two genes in the same sample
Comparing the expression between the same gene in different samples
Differential Expression

8. What can we learn from RNAseq?
Comparing the expression between two genes
in the same sample
PROBLEM :
* Genes of different length are expected to have different
number of reads

9. What can we learn from RNAseq?
Possible solution: Normalizing by transcript length and the
total number of reads mapped in the experiment
RPKM =

10. Problems with Normalization
Gene B> Gene A > Gene C
Gene A> Gene B > Gene C
Warning !!! normalization by total number of reads can lead to
false detection of differentially expressed genes

11. What can we learn from RNAseq?
Comparing the expression between the same gene
in different samples
Example : Finding new markers for pluripotency
(תאי גזע עובריים)
(תאים ממוינים)
Good markers
for pluripotency
Highly Expressed
Lowly Expressed

12. What can we learn from RNAseq?
Comparing the expression between the same gene
in different samples
Sample X (Stem cell)
Sample Y (Fibroblasts)
Fold change (FC) = Ratio between the expression of the gene in sample X to the expression of the gene in sample Y
Is fold change enough to evaluate the difference?

13. Finding new markers for pluripotency
Remember: We always need to evaluate the statistical significance of the results (p-value)
Finding new markers for pluripotency
Possible candidates
for being pluripotent
markers *
Expression in stem cells versus fibroblasts
Here we calculate the –log (p-value)
high values denote highly significant results

14. Clustering the data according to expression profiles
NEXT…
Clustering the data according to expression profiles
Clustering organizes things that are close into groups. .
Genes
Expression in different human tissues
Highly Expressed
Lowly Expressed

15. WHY? What can we learn from the clustering genes?
Identify gene function
Set of genes with similar gene expression can infer similar function
Diagnostics and Therapy
A set of genes which differs in the gene expression can indicate a disease state

16. HOW? Different clustering approaches
Supervised Methods (למידה מונחית)
-Support Vector Machine (SVM)
Unsupervised (למידה בלתי מונחית)
- Hierarchical Clustering
- K-means
(will learn next lesson)

17. Clustering organizes things that are close into groups What does it mean for two genes to be close?
We need a mathematical definition of distance between the
expression pattern of two genes
Gene 1
Gene 2
Gene1= (E11, E12, …, E1N)’
Gene2= (E21, E22, …, E2N)’

18. Calculating the distance between two expression patterns
We can use many different distance measures
Gene1= (E11, E12, …, E1N)’
Gene2= (E21, E22, …, E2N)’
Euclidean distance (ED)= Sqrt of Sum of (E1i -E2i)2, i=1,…,N
X1,Y1
Distance
X2,Y2
When N is 100 we have to think abstractly
Low Euclidean Distance
High similarity

19. Calculating the distance between two expression patterns
Pearson correlation
coefficient
High correlation coefficient
High similarity

20. Distance and correlations can produce very different results
Counts
Euclidian distance= Pearson correlation= 0.9
Low similarity
High similarity

21. Clustering the genes according to expression
Gene Cluster
A set of genes that have a similar expression pattern across tissues
High correlation/low Euclidian distance
between the expression vectors within the cluster

22. What can we learn from clusters with similar gene expression ??
Similar expression between genes can suggest that:
-The genes have similar function
-The genes work together in the same pathway/complex

23. Example: Identifying genes that have similar function
HnRNPA1 and SRp40 are not clear homologs based on blast e-value but have a very similar gene expression pattern in different tissues

24. Are hnRNP A1 and SRp40 functionally homologs ??SF SF SF SF SF SF SF SF SF SF SF SF
SRP40
YES!!!!

25. Example: Genes work together in the same complex
Counts
Transcription Factor TF
Long non-coding RNA

26. How can gene expression help in diagnostics?

27. A molecular signature of metastasis in primary solid tumors
Samples were taken from patients with adenocarcinoma.
hundreds of genes
that differentiate between
cancer tissues in different
stages of the tumor were found.
The arrow shows an example
of a tumor cells which
were not detected correctly by
histological or other
clinical parameters.
Ramaswamy et al, 2003 Nat Genet 33:49-54

28. How can gene-expression help in diagnostics ?
RESEARCH QUESTION
Can we distinguish BRCA1 from BRCA2– cancers based solely on their gene expression profiles?
Different patients (BRCA1 or BRCA2)
Genes
HERE we want to cluster the patients not the genes !!!

29. Supervised approaches for diagnostic based on expression data
Support Vector Machine
SVM

30. How can gene-expression help in diagnostics ?
Different patients (BRCA1 or BRCA2)
Genes
DATA
Microarray expression of all genes from two types of
breast cancer patients (BRCA1 and BRCA2)

31. SVM would begin with a set of samples from patients which have been diagnosed as either BRCA1 (red dots) or BRCA2 (blue dots).
Each dot represents a vector of the expression pattern taken from the microarray experiment of a patient.

32. How do SVM’s work with expression data?
The SVM is trained on data which was classified based on histology. ?
After training the SVM to separated the BRCA1 from BRAC2 tumors
given the expression data, we can then apply it to diagnose an
unknown tumor for which we have the equivalent expression data .

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