Chromosome structure via Euclidean Distance Matrices

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<big>'''Chromosome structure via Euclidean Distance Matrices'''</big>
<big>'''Chromosome structure via Euclidean Distance Matrices'''</big>
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The [http://www.convexoptimization.com/TOOLS/Multimodal_Empirical_Image_Correlation_Decomposition_Fleming.avi data represents the auto-correlation coefficients (6MB video)] for gene expression of 3827 genes from the circular chromosome of E.coli across 49 different experimental conditions. In the data, the axis is ordered according the order in which genes appear in the E.coli circular chromosome with an arbitrary start and end point. The expression was smoothed at various resolutions to highlight spatial patterns at different scales. In this way the correlation matrices compliment eachother. Bright green indicates a correlation coefficient of +1 and bright red indicates anticorrelation, -1. It is assumed that the E.coli chromosome is structured such that genes which posetively correlate are close in distance within the cell, whereas genes which anticorrelate are far in distance. The exact relation is unknown but it would be interesting to try the alternate hypothesis to see what effect this has on the structure of the molecule.
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The [http://www.convexoptimization.com/TOOLS/Multimodal_Empirical_Image_Correlation_Decomposition_Fleming.avi data represents the auto-correlation coefficients (6MB video)] for gene expression of 3827 genes from the circular chromosome of E.coli across 49 different experimental conditions. In the data, the axis is ordered according the order in which genes appear in the E.coli circular chromosome with an arbitrary start and end point. The expression was smoothed at various resolutions to highlight spatial patterns at different scales. In this way the correlation matrices compliment each other. Bright green indicates a correlation coefficient of +1 and bright red indicates anticorrelation, -1. It is assumed that the E.coli chromosome is structured such that genes which posetively correlate are close in distance within the cell, whereas genes which anticorrelate are far in distance. The exact relation is unknown but it would be interesting to try the alternate hypothesis to see what effect this has on the structure of the molecule.
In that data video, the left frame represents autocorrelation of a subset of genes at successively increasing levels of resolution. The right frame is the same autocorrelation except that, prior to decomposition of the signal into different resolutions, the positions of the genes along the chromosome are randomly permuted. The idea is that this represents the autocorrelation one would expect if there were no information in the relative positions of the genes along the chromosome. As such, the right frame is a null hypothesis.
In that data video, the left frame represents autocorrelation of a subset of genes at successively increasing levels of resolution. The right frame is the same autocorrelation except that, prior to decomposition of the signal into different resolutions, the positions of the genes along the chromosome are randomly permuted. The idea is that this represents the autocorrelation one would expect if there were no information in the relative positions of the genes along the chromosome. As such, the right frame is a null hypothesis.

Revision as of 03:33, 4 July 2008

Chromosome structure via Euclidean Distance Matrices

The data represents the auto-correlation coefficients (6MB video) for gene expression of 3827 genes from the circular chromosome of E.coli across 49 different experimental conditions. In the data, the axis is ordered according the order in which genes appear in the E.coli circular chromosome with an arbitrary start and end point. The expression was smoothed at various resolutions to highlight spatial patterns at different scales. In this way the correlation matrices compliment each other. Bright green indicates a correlation coefficient of +1 and bright red indicates anticorrelation, -1. It is assumed that the E.coli chromosome is structured such that genes which posetively correlate are close in distance within the cell, whereas genes which anticorrelate are far in distance. The exact relation is unknown but it would be interesting to try the alternate hypothesis to see what effect this has on the structure of the molecule.

In that data video, the left frame represents autocorrelation of a subset of genes at successively increasing levels of resolution. The right frame is the same autocorrelation except that, prior to decomposition of the signal into different resolutions, the positions of the genes along the chromosome are randomly permuted. The idea is that this represents the autocorrelation one would expect if there were no information in the relative positions of the genes along the chromosome. As such, the right frame is a null hypothesis.

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