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Time series experiments

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Time series experiments

• Why is this a separate lecture: – The price of microarrays are decreasing – more  time series experiments are coming – Often a more complex experimental design – Many time points makes this a large experiment – The analysis method is often specified by what you  are looking for
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Time series experiments

• Why consider doing a time series experiment: – Biology is dynamic  • The genes can change their expression in  different ways during time – Observe cascades and secondary effects
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Time series experiments

• Peaks

895 900 905 0. 0 0. 5 1. 0 1. 5 2. 0 time Exp re ssi on  le ve l
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Time series experiments

• Level change

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Experimental design (Ex.d)

• Often larger and complex  • What to consider in an experimental design of a time  series study: – Time points – Distance between time points – Replicates – Control – Technology
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Ex.d.: Time points and the distance between 

them

• How to choose: – What is already known about the system/genes  • Theoretical and experimental knowledge Early /late response The direction of the response Will changes in some genes influence others – What is your field of interest
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Ex.d.: Replicates

• How many replicates to use: – Many time points but few replicates  • Should be enough to do the analysis you want!
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Ex.d.: Control

• What to use as a control and how many controls? – One control point or one time series control – Differences between treatments – The other time points as a control
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Ex.d.: Control

• One control – Use one time point or a untreated sample as control • Ok if no other time effects are expected, such as  growth, phase of expression  • A untreated time series as control – Same time points, technology, sample source and   same handling except the treatment – Find genes that change due to treatment over time,  filter out some of the effects due to handling and  time
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Ex.d.: Control

Vertically: Differences due treatment, but you reduce

the effect of the handling

Horizontally: Differences due to treatment, time and

some handling effect T2

T1 T3 T4

Tc2

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Ex.d.: Control

• Differences between treatments ­ compare two or more  time series • Have the same: – Time points – Conditions – Sample source – Technology – Handling
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Ex.d.: Control

• The other time points as a control:  – Changes due to treatment, time but it does not  exclude the effect of the handling – Less arrays used – See short time effects and long time effects’ – Ok if no other time effects are expected, such as  growth, phase of expression  T2 T1 T3 T4
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Ex.d.: Technology

• What technology to choose – Homemade/commercial – One channel – Two channel: Reference design is recommended • What comparisons are to be done • Budget
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Experimental design

• Often larger and complex  • What to consider in an experimental design of a time  series study: – Time points – Distance between time points – Replicates – Control – Technology
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Pre­processing

• The same steps as for non time series microarray  experiments, but not always the same algorithm or the  same use of the algorithms: – QC – Filtering – Normalization – ...
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Analysis of time series data

• Clustering • T­test between time points • PCA • PLS • Cyclic • Profile • ...
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Analysis: Clustering

• Filter the data set before clustering • Cluster the genes • Use correlation as a distance measure (as a starting  point) • There exist no right number of clusters – a priori  knowledge is needed 
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Analysis: Clustering

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Analysis: T­test

• One time point vs. another time point • You have 3 time points: T1, T2 and T3 • What should you compare?
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Analysis: T­test

• Early response: • Late response: • (T1­T2)(T2­T3)  – In common – Not in common T2 T1 T3 T2
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Analysis: T­test

• The smaller changes: T1 vs. T3 T2 T1 T3 T1 T3 g1 g3 g3 g2 g3
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Analysis: T­test

• If you have a large time series, it can be time  consuming to do this • Hard to select the ones to compare • Solution: – Compare all consecutive pairs of time points and  then make a profile based on this – Other types of analysis: PCA, PLS, …
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PCA

• Projecting the genes or samples into (a low  dimensionality) space that displays most of the  variance in the (high dimensionality) data – i.e. PC1 and PC2
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Partial Least Square (PLS)

• Using external information to guide the projection – Instead of projecting the genes into a space that  shows greatest variance, the genes are projected  into a space that will help identify genes with  particular characteristics, e.g. as below or cyclic  genes
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Example: Cycling genes

• Expectation of cyclic genes  – One top and one bottom within one cycle – Time between top and bottom is half a cycle • Sine and cosine are known shapes that have these  characteristics • Use sine and cosine to search for genes that are  correlated with these shapes • The combination of the two allows us to identify all  phase shifts of these shapes
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J­Express: Cellcycle

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GSEA with continuous profiles

• The genes are ranked based on their correlation to an  interesting (continuous) profile • After ranking the genes, looking for genesets that are  overrepresented towards the top of the ranked list is  the same as for differential expression
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GSEA with continuous profiles

• Use a gene profile from the dataset as the search  profile • Create a profile – Ascending or decending profile – Peak profile
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Summary

• Time series experiments are now affordable and  within reach for more groups. • Time series experiments are more complex • Time series experiments demand more planning
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Acknowledgement

Presentation made in collaboration with Ingrid  Østensen (former NMC Oslo).

References

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