More on Exploring Correlations in R

About a year ago I wrote a post about producing scatterplot matrices in R. These are handy for quickly getting a sense of the correlations that exist in your data. Recently someone asked me to pull out some relevant statistics (correlation coefficient and p-value) into tabular format to publish beside a scatterplot matrix. The built-in cor() function will produce a correlation matrix, but what if you want p-values for those correlation coefficients? Also, instead of a matrix, how might you get these statistics in tabular format (variable i, variable j, r, and p, for each i-j combination)? Here's the code (you'll need the PerformanceAnalytics package to produce the plot).


The cor() function will produce a basic correlation matrix.  12 years ago Bill Venables provided a function on the R help mailing list for replacing the upper triangle of the correlation matrix with the p-values for those correlations (based on the known relationship between t and r). The cor.prob() function will produce this matrix.

Finally, the flattenSquareMatrix() function will "flatten" this matrix to four columns: one column for variable i, one for variable j, one for their correlation, and another for their p-value (thanks to Chris Wallace on StackOverflow for helping out with this one).



Finally, the chart.Correlation() function from the PerformanceAnalytics package produces a very nice scatterplot matrix, with histograms, kernel density overlays, absolute correlations, and significance asterisks (0.05, 0.01, 0.001):

Cscan: Finding Gene Expression Regulators with ENCODE ChIP-Seq Data

Recently published in Nucleic Acids Research:

F. Zambelli, G. M. Prazzoli, G. Pesole, G. Pavesi, Cscan: finding common regulators of a set of genes by using a collection of genome-wide ChIP-seq datasets., Nucleic acids research 40, W510–5 (2012).

Cscan web interface screenshot

This paper presents a methodology and software implementation that allows users to discover a set of transcription factors or epigenetic modifications that regulate a set of genes of interest. A wealth of data about transcription factor binding exists in the public domain, and this is a good example of a group utilizing those resources to develop tools that are of use to the broader computational biology community. 

High-throughput gene expression experiments like microarrays and RNA-seq experiments often result in a list of differentially regulated or co-expressed genes. A common follow-up question asks which transcription factors may regulate those genes of interest. The ENCODE project has completed ChIP-seq experiments for many transcription factors and epigenetic modifications for a number of different cell lines in both human and model organisms. These researchers crossed this publicly available data on enriched regions from ChIP-seq experiments with genomic coordinates of gene annotations to create a table of gene annotations (rows) by ChIP-peak signals, with a presence/absence peak in each cell. Given a set of genes of interest (e.g. differentially regulated genes from an RNA-seq experiment), the method evaluates the over-/under-representation of target sites for the DNA binding protein in each ChIP experiment using a Fisher's exact test. Other methods based on motif-enrichment (using position weight matrices derived from databases like TRANSFAC or JASPAR) would miss DNA-binding factors like the Retinoblastoma protein (RB), which lacks a DNA-binding domain and is recruited to promoters by other transcription factors. In addition to overcoming this limitation, the method presented here also has the advantage of considering tissue-specificity and chromatin accessibility.

The web interface is free and doesn't require registration: http://www.beaconlab.it/cscan

Nucleic Acids Research: Cscan: finding common regulators of a set of genes by using a collection of genome-wide ChIP-seq datasets