1. A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression
By Alfredo A Kalaitzis and Neil D Lawrence
Presented by MOHAMMAD OMAR FARUK

2. Definition Gene Expression:
Gene expression is the process by which the information contained within a gene becomes a useful product.
Gene Expression Profiling:
Gene expression profiling is the measurement of the activity( the expression) of thousands of genes at once to create a global picture of the cellular function.
Inactive Gene or Quite Gene:
Gene which show negligible changes in nRNA concentration levels in response to treatment or perturbations.

3. Background
Gene expression profiles give a snapshot of mRNA concentration levels as encoded by the genes of an organism under given experimental conditions.
Due to the decreasing cost of gene expression microarray time series experiments have become commonplace giving a broader picture of the gene regulation process.
The experimental conditions under which gene expression measurements are taken cannot be perfectly controlled due to corrupted noise.
This noise are from either of biological origin or arising through the measurement process.
The analysis of gene expression microarray time-series is an important problems in systems biology.

4. Background
Gene expression is a temporal process. Different proteins required for different functions and under different conditions.
But early studies focuses on just on a single point of time.
So if the temporal nature of data is ignored the it will not possible to detect such expression properly.

5. Testing for Expression
Removing inactive or quiet genes allows the focus to dwell on genes that have responded to treatment.
If we measure the absolute level of gene expression then a quiet gene would be one whose expression level is indistinguishable from noise.
Alternatively, if we hybridizing two samples to the same array and quantifying the ratio of the expression levels then a quite gene will show a similar response in both hybridized samples.
If we remove these quite gene it would be a harmless effect in the processing pipeline.

6. Gaussian Process
Gaussian process is a particular kind of statistical model where observations occur in a continuous domain, e.g. time or space.
It the natural generalization of a multivariate Gaussian distribution to a Gaussian distribution over a specific family of functions -- a family defined by a covariance function or kernel.

7. Gene Expression Analysis with GP
Gaussian Process offer an easy to implement approach to the true signal and noise embedded in a gene expression time-series.
In the context of expression trajectory estimation, a GP coupled with the squared exponential covariance function.
In literature GP priors uses for modeling transcriptional regulation, to detect interval of differential expression.
In this paper they focuses on one-sample testing to rank the differential expression and ultimately detect quite and active genes.

8. Result
They used three set of data. One set is sampled by BATS (Bayesian hierarchical model for the analysis of time-series) and other set is sampled by GP. Both set are in-silico datasets.
Third set is TSNI-experimental which is the experimental set coming from a study on primary mouse keratinocytes.
They applied GP regression and BATS on each of the datasets. BATS model can employ three different noise model: Gaussian, Student-t and double exponential.
Given a ground truth they evaluate the quality of rankings and compare different algorithms.

9. Result: Simulated Data

10. Result: Experimental Data

11. Discussion
On BATS –sampled data BATS maintain stable supremacy over the GP.
This performance gap is partially due to the lack of robust noise model for the GP.
Squared exponential covariance function models for infinitely differentiable function.
BATS-sampled data are biased in the underlying function and contain a small degree of differentially.
The experimental data are more complex and due to the limited polynomial degree of BATS, its not adequate for this types of data.

12. Future Work and Conclusions
In future they will add more robust model for GP.
The Gaussian process framework offers a natural way of handling biological replicates.
It also provides confidence intervals along the estimated curves of gene expression.
Gaussian processes should be a standard tool in the analysis of gene expression time series

13. Questions?