Description Usage Arguments Details Value Author(s) See Also Examples

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | ```
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)
``` |

`object` |
A |

`what` |
The clustering level to be used for plotting and calculating
correlations. Can be either |

`values` |
Use either |

`samples` |
Character vector indicating which samples to use. Can be
either |

`method` |
A character string indicating which correlation coefficient
should be computed. Passed to |

`tagCountThreshold` |
Only TSSs with tag count |

`applyThresholdBoth` |
See |

`plotSize` |
Size of the individual comparison plot in pixels - the
total size of the resulting png will be |

`digits` |
The number of significant digits for the data to be kept in log
scale. Ignored in |

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`

.

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.

Vanja Haberle

Charles Plessy

Other CAGEr plot functions: `hanabiPlot`

,
`plotAnnot`

,
`plotExpressionProfiles`

,
`plotInterquantileWidth`

,
`plotReverseCumulatives`

1 | ```
plotCorrelation2(exampleCAGEexp, what = "consensusClusters", value = "normalized")
``` |

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