plotCorrelation {CAGEr} | R Documentation

## Pairwise scatter plots and correlations of CAGE signal

### Description

Calculates the pairwise correlation between samples and creates a plot matrix showing the correlation coeficients in the upper triangle, the sample names in the diagonal, and the catter plots in the lower triangle.

### Usage

    plotCorrelation(object, what = c("CTSS", "consensusClusters"),
      values = c("raw", "normalized"), samples = "all", method = "pearson",
      tagCountThreshold = 1, applyThresholdBoth = FALSE, plotSize = 800)

    ## S4 method for signature 'CAGEr'
    plotCorrelation(object, what = c("CTSS",
      "consensusClusters"), values = c("raw", "normalized"), samples = "all",
      method = "pearson", tagCountThreshold = 1, applyThresholdBoth = FALSE,
      plotSize = 800)

    plotCorrelation2(object, what = c("CTSS", "consensusClusters"),
      values = c("raw", "normalized"), samples = "all", method = "pearson",
      tagCountThreshold = 1, applyThresholdBoth = FALSE, digits = 3)

    ## S4 method for signature 'CAGEset'
    plotCorrelation2(object, what = c("CTSS",
      "consensusClusters"), values = c("raw", "normalized"), samples = "all",
      method = "pearson", tagCountThreshold = 1, applyThresholdBoth = FALSE,
      digits = 3)

    ## S4 method for signature 'CAGEexp'
    plotCorrelation2(object, what = c("CTSS",
      "consensusClusters"), values = c("raw", "normalized"), samples = "all",
      method = "pearson", tagCountThreshold = 1, applyThresholdBoth = FALSE,
      digits = 3)

    ## S4 method for signature 'SummarizedExperiment'
    plotCorrelation2(object, what = c("CTSS",
      "consensusClusters"), values = c("raw", "normalized"), samples = "all",
      method = "pearson", tagCountThreshold = 1, applyThresholdBoth = FALSE,
      digits = 3)

    ## S4 method for signature 'DataFrame'
    plotCorrelation2(object, what = c("CTSS",
      "consensusClusters"), values = c("raw", "normalized"), samples = "all",
      method = "pearson", tagCountThreshold = 1, applyThresholdBoth = FALSE,
      digits = 3)

    ## S4 method for signature 'data.frame'
    plotCorrelation2(object, what = c("CTSS",
      "consensusClusters"), values = c("raw", "normalized"), samples = "all",
      method = "pearson", tagCountThreshold = 1, applyThresholdBoth = FALSE,
      digits = 3)

    ## S4 method for signature 'matrix'
    plotCorrelation2(object, what = c("CTSS",
      "consensusClusters"), values = c("raw", "normalized"), samples = "all",
      method = "pearson", tagCountThreshold = 1, applyThresholdBoth = FALSE,
      digits = 3)

### Arguments

`object` | A `CAGEr` object or (only for `plotCorrelation2`) a `SummarizedExperiment` or an expression table as a `DataFrame`, `data.frame` or `matrix` object.
`what` | The clustering level to be used for plotting and calculating correlations. Can be either `"CTSS"` to use individual TSSs or `"consensusClusters"` to use consensus clusters, i.e. entire promoters. Ignored for anything else than `CAGEr` objects.
`values` | Use either `"raw"` (default) or `"normalized"` CAGE signal. Ignored for plain expression tables.
`samples` | Character vector indicating which samples to use. Can be either `"all"` to select all samples in a `CAGEr` object, or a subset of valid sample labels as returned by the `sampleLabels` function.
`method` | A character string indicating which correlation coefficient should be computed. Passed to `cor` function. Can be one of `"pearson"`, `"spearman"`, or `"kendall"`.
`tagCountThreshold` | Only TSSs with tag count `>= tagCountThreshold` in either one (`applyThresholdBoth = FALSE`) or both samples (`applyThresholdBoth = TRUE`) are plotted and used to calculate correlation.
`applyThresholdBoth` | See `tagCountThreshold` above.
`plotSize` | Size of the individual comparison plot in pixels - the total size of the resulting png will be `length(samples) * plotSize` in both dimensions. Ignored in `plotCorrelation2`.
`digits` | The number of significant digits for the data to be kept in log scale. Ignored in `plotCorrelation`. In `plotCorrelation2`, the number of points plotted is considerably reduced by rounding the point coordinates to a small number of significant digits before removing duplicates. Chose a value that makes the plot visually indistinguishable with non-deduplicated data, by making tests on a subset of the data.

### Details

In the scatter plots, a pseudo-count equal to half the lowest score is added to the null values so that they can appear despite logarithmic scale.

`SummarizedExperiment` objects are expected to contain raw tag counts in a “counts” assay and the normalized expression scores in a “normalized” assay.

Avoid using large `matrix` objects as they are coerced to `DataFrame` class without special care for efficiency.

`plotCorrelation2` speeds up the plotting by a) deduplicating that data: no point is plot twice at the same coordinates, b) rounding the data so that indistinguishable positions are plotted only once, c) using a black square glyph for the points, d) caching some calculations that are made repeatedly (to determine where to plot the correlation coefficients), and e) preventing coercion of `DataFrames` to `data.frames`.

### Value

Displays the plot and returns a `matrix` of pairwise correlations between selected samples. The scatterplots of `plotCorrelation` are colored according to the density of points, and in `plotCorrelation2` they are just black and white, which is much faster to plot. Note that while the scatterplots are on a logarithmic scale with pseudocount added to the zero values, the correlation coefficients are calculated on untransformed (but thresholded) data.

### Author(s)

Vanja Haberle

Charles Plessy

### See Also

Other CAGEr plot functions: `hanabiPlot`, `plotAnnot`, `plotExpressionProfiles`, `plotInterquantileWidth`, `plotReverseCumulatives`

### Examples

    plotCorrelation2(exampleCAGEexp, what = "consensusClusters", value = "normalized")

[Package CAGEr version 1.24.0 Index]