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Production of a chord diagram is described in detail here. You use the R package with either,

library(chorddiag)
library (circulize)

The first level clusters are predefined and the second layer of clustering is what you need to work out. The obvious stat is correlation, secondly (better approach IMO) you might look at covariance, although I don't exactly know what this "interaction" constitutes, which makes it difficult.

My personal view is the diagramatic representation doesn't really matter, its the robustness of the stats behind the diagram that counts.

I was a bit surprised to learn ggplot2 doesn't do circular diagrams :-(

Lymphoblastoid Cell lines

I think you need to clarify the experiment on these cells lines, I assume its a time course, i.e. time series.

At a guess this is RNA expression, if you are using a chip then its between predetermined genes (i.e. spotted on the chip). If it is RNA seq then it can be anything. I suspect you are treating the cells and monitoring changes in RNA expression as a function of their biological (immunological?) activity. If the basic measure is time, i.e. a time series - thus two genes being expressed in the same unit time the best opening statistic is covariance. If all you want to do is make a chord diagram out of it, I think thats fine, this will form a 2x2 variance matrix for which any standard clustering will work (Jaccard distances [there will be better ones]) or direct plotting from the matrix. If you looking at unique expression behaviour to attempt to unlock some sort of pathway then you are into more serious regression time series (autoregression that sort of thing - this is complicated). If its oscillations then its a separate branch of stats based on sinosoids (sine waves) called periodicity analysis. HOWEVER, if the interaction is genetic similarity (gene families) e.g. mRNA then you want a phylogenetics measure, which if its clustering neighbor-joining is fine.

My personal view is to keep it simple and the chord diagram will do the selling.

Production of a chord diagram is described in detail here. You use the R package with either,

library(chorddiag)
library (circulize)

The first level clusters are predefined and the second layer of clustering is what you need to work out. The obvious stat is correlation, secondly (better approach IMO) you might look at covariance, although I don't exactly know what this "interaction" constitutes, which makes it difficult.

My personal view is the diagramatic representation doesn't really matter, its the robustness of the stats behind the diagram that counts.

I was a bit surprised to learn ggplot2 doesn't do circular diagrams :-(

Lymphoblastoid Cell lines

I think you need to clarify the experiment on these cells lines, I assume its a time course, i.e. time series.

At a guess this is RNA expression, if you are using a chip then its between predetermined genes (i.e. spotted on the chip). If it is RNA seq then it can be anything. I suspect you are treating the cells and monitoring changes in RNA expression as a function of their biological (immunological?) activity. If the basic measure is time, i.e. a time series - thus two genes being expressed in the same unit time the best opening statistic is covariance. If all you want to do is make a chord diagram out of it, I think thats fine, this will form a 2x2 variance matrix for which any standard clustering will work (Jaccard distances [there will be better ones]) or direct plotting from the matrix. If you looking at unique expression behaviour to attempt to unlock some sort of pathway then you are into more serious regression time series (autoregression that sort of thing - this is complicated). If its oscillations then its a separate branch of stats based on sinosoids (sine waves) called periodicity analysis. HOWEVER, if the interaction is genetic similarity (gene families) e.g. mRNA then you want a phylogenetics measure, which if its clustering neighbor-joining is fine.

My personal view if it is the experiment I think, I would,

1. Construct a matrix of the covariance of the (I assume) time series
2. Make sure the covariance is standarised between -1 to 1 (Pearson approach), R-cran.
3. Assign a tight cut-off (tricky because there's no "hard cut-off") but say 0.7 (you need to experiment a bit)
4. Plot the raw covariance on the chord diagram.

Easy, simple and highly visual.

Production of a chord diagram is described in detail here. You use the R package with either,

library(chorddiag)
library (circulize)

The first level clusters are predefined and the second layer of clustering is what you need to work out. The obvious stat is correlation, secondly (better approach IMO) you might look at covariance, although I don't exactly know what this "interaction" constitutes, which makes it difficult.

My personal view is the diagramatic representation doesn't really matter, its the robustness of the stats behind the diagram that counts.

I was a bit surprised to learn ggplot2 doesn't do circular diagrams :-(

Lymphoblastoid Cell lines

I think you need to clarify the experiment on these cells lines, I assume its a time course, i.e. time series.

At a guess this is RNA expression, if you are using a chip then its between predetermined genes (i.e. spotted on the chip). If it is RNA seq then it can be anything. I suspect you are treating the cells and monitoring changes in RNA expression as a function of their biological (immunological?) activity. If the basic measure is time, i.e. a time series - thus two genes being expressed in the same unit time the best opening statistic is covariance. If all you want to do is make a chord diagram out of it, I think thats fine, this will form a 2x2 variance matrix for which any standard clustering will work (Jaccard distances [there will be better ones]) or direct plotting from the matrix. If you looking at unique expression behaviour to attempt to unlock some sort of pathway then you are into more serious regression time series (autoregression that sort of thing - this is complicated). If its oscillations then its a separate branch of stats based on sinosoids (sine waves) called periodicity analysis. HOWEVER, if the interaction is genetic similarity (gene families) e.g. mRNA then you want a phylogenetics measure, which if its clustering neighbor-joining is fine.

Production of a chord diagram is described in detail here. You use the R package with either,

library(chorddiag)
library (circulize)

The first level clusters are predefined and the second layer of clustering is what you need to work out. The obvious stat is correlation, secondly (better approach IMO) you might look at covariance, although I don't exactly know what this "interaction" constitutes, which makes it difficult.

My personal view is the diagramatic representation doesn't really matter, its the robustness of the stats behind the diagram that counts.

I was a bit surprised to learn ggplot2 doesn't do circular diagrams :-(

Lymphoblastoid Cell lines

I think you need to clarify the experiment on these cells lines, I assume its a time course, i.e. time series.

At a guess this is RNA expression, if you are using a chip then its between predetermined genes (i.e. spotted on the chip). If it is RNA seq then it can be anything. I suspect you are treating the cells and monitoring changes in RNA expression as a function of their biological (immunological?) activity. If the basic measure is time, i.e. a time series - thus two genes being expressed in the same unit time the best opening statistic is covariance. If all you want to do is make a chord diagram out of it, I think thats fine, this will form a 2x2 variance matrix for which any standard clustering will work (Jaccard distances [there will be better ones]) or direct plotting from the matrix. If you looking at unique expression behaviour to attempt to unlock some sort of pathway then you are into more serious regression time series (autoregression that sort of thing - this is complicated). If its oscillations then its a separate branch of stats based on sinosoids (sine waves) called periodicity analysis. HOWEVER, if the interaction is genetic similarity (gene families) e.g. mRNA then you want a phylogenetics measure, which if its clustering neighbor-joining is fine.

My personal view is to keep it simple and the chord diagram will do the selling.

Production of a chord diagram is described in detail here. You use the R package with either,

library(chorddiag)
library (circulize)

The first level clusters are predefined and the second layer of clustering is what you need to work out. The obvious stat is correlation, secondly (better approach IMO) you might look at covariance, although I don't exactly know what this "interaction" constitutes, which makes it difficult.

My personal view is the diagramatic representation doesn't really matter, its the robustness of the stats behind the diagram that counts.

I was a bit surprised to learn ggplot2 doesn't do circular diagrams :-(

Lymphoblastoid Cell lines

I think you need to clarify the experiment on these cells lines, I assume its a time course, i.e. time series.

Production of a chord diagram is described in detail here. You use the R package with either,

library(chorddiag)
library (circulize)

The first level clusters are predefined and the second layer of clustering is what you need to work out. The obvious stat is correlation, secondly (better approach IMO) you might look at covariance, although I don't exactly know what this "interaction" constitutes, which makes it difficult.

My personal view is the diagramatic representation doesn't really matter, its the robustness of the stats behind the diagram that counts.

I was a bit surprised to learn ggplot2 doesn't do circular diagrams :-(

Lymphoblastoid Cell lines

I think you need to clarify the experiment on these cells lines, I assume its a time course, i.e. time series.

At a guess this is RNA expression, if you are using a chip then its between predetermined genes (i.e. spotted on the chip). If it is RNA seq then it can be anything. I suspect you are treating the cells and monitoring changes in RNA expression as a function of their biological (immunological?) activity. If the basic measure is time, i.e. a time series - thus two genes being expressed in the same unit time the best opening statistic is covariance. If all you want to do is make a chord diagram out of it, I think thats fine, this will form a 2x2 variance matrix for which any standard clustering will work (Jaccard distances [there will be better ones]) or direct plotting from the matrix. If you looking at unique expression behaviour to attempt to unlock some sort of pathway then you are into more serious regression time series (autoregression that sort of thing - this is complicated). If its oscillations then its a separate branch of stats based on sinosoids (sine waves) called periodicity analysis. HOWEVER, if the interaction is genetic similarity (gene families) e.g. mRNA then you want a phylogenetics measure, which if its clustering neighbor-joining is fine.