Significance analysis of microarray transcript levels in time series experiments
2007

Analyzing Gene Expression Changes Over Time

publication Evidence: moderate

Author Information

Author(s): Di Camillo Barbara, Toffolo Gianna, Nair Sreekumaran K, Greenlund Laura J, Cobelli Claudio

Primary Institution: University of Padova

Hypothesis

Can we effectively select differentially expressed genes in time series experiments with limited data?

Conclusion

Method 2 outperforms the other methods for short time series, while Method 3 is better for long time series.

Supporting Evidence

  • Method 2 outperformed Method 1 in terms of precision and recall for short time series.
  • Method 3 performed better than Method 2 for long time series.
  • The study utilized synthetic data to evaluate the performance of the methods.

Takeaway

This study looks at how to find genes that change their activity over time, even when we don't have a lot of data. It shows that some methods work better than others depending on how much data we have.

Methodology

The study compares three methods for selecting differentially expressed genes using synthetic data and applies them to a real case study.

Potential Biases

Potential bias due to reliance on synthetic data and assumptions about error distributions.

Limitations

The methods may not perform well if the number of replicates is insufficient or if the error characteristics change over time.

Digital Object Identifier (DOI)

10.1186/1471-2105-8-S1-S10

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