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Display results of correlation analyses

Source: R/analysis_correlation.R
plot.GEandAScorrelation.Rd

Plot, print and display as table the results of gene expression and alternative splicing

Usage

# S3 method for class 'GEandAScorrelation'
x[genes = NULL, ASevents = NULL]

# S3 method for class 'GEandAScorrelation'
plot(
  x,
  autoZoom = FALSE,
  loessSmooth = TRUE,
  loessFamily = c("gaussian", "symmetric"),
  colour = "black",
  alpha = 0.2,
  size = 1.5,
  loessColour = "red",
  loessAlpha = 1,
  loessWidth = 0.5,
  fontSize = 12,
  ...,
  colourGroups = NULL,
  legend = FALSE,
  showAllData = TRUE,
  density = FALSE,
  densityColour = "blue",
  densityWidth = 0.5
)

# S3 method for class 'GEandAScorrelation'
print(x, ...)

# S3 method for class 'GEandAScorrelation'
as.table(x, pvalueAdjust = "BH", ...)

Arguments

x

GEandAScorrelation object obtained after running correlateGEandAS()

genes

Character: genes

ASevents

Character: AS events

autoZoom

Boolean: automatically set the range of PSI values based on available data? If FALSE, the axis relative to PSI values will range from 0 to 1

loessSmooth

Boolean: plot a smooth curve computed by stats::loess.smooth?

loessFamily

Character: if gaussian, loess fitting is by least-squares, and if symmetric, a re-descending M estimator is used

colour

Character: points' colour

alpha

Numeric: points' alpha

size

Numeric: points' size

loessColour

Character: loess line's colour

loessAlpha

Numeric: loess line's opacity

loessWidth

Numeric: loess line's width

fontSize

Numeric: plot font size

...

Arguments passed on to stats::loess.smooth

span

smoothness parameter for loess.

degree

degree of local polynomial used.

evaluation

number of points at which to evaluate the smooth curve.

colourGroups

List of characters: sample colouring by group

legend

Boolean: show legend for sample colouring?

showAllData

Boolean: show data outside selected groups as a single group (coloured based on the colour argument)

density

Boolean: contour plot of a density estimate

densityColour

Character: line colour of contours

densityWidth

Numeric: line width of contours

pvalueAdjust

Character: method used to adjust p-values (see Details)

Value

Plots, summary tables or results of correlation analyses

Details

The following methods for p-value adjustment are supported by using the respective string in the pvalueAdjust argument:

  • none: do not adjust p-values

  • BH: Benjamini-Hochberg's method (false discovery rate)

  • BY: Benjamini-Yekutieli's method (false discovery rate)

  • bonferroni: Bonferroni correction (family-wise error rate)

  • holm: Holm's method (family-wise error rate)

  • hochberg: Hochberg's method (family-wise error rate)

  • hommel: Hommel's method (family-wise error rate)

See also

Other functions to correlate gene expression and alternative splicing: correlateGEandAS()

Other functions to correlate gene expression and alternative splicing: correlateGEandAS()

Examples

annot <- readFile("ex_splicing_annotation.RDS")
junctionQuant <- readFile("ex_junctionQuant.RDS")
psi <- quantifySplicing(annot, junctionQuant, eventType=c("SE", "MXE"))
#> Using 3 of 3 events (100%) whose junctions are present in junction quantification data...
#>   |                                        |   0% 
  |========                                |  20% 
  |================                        |  40% 
  |========================                |  60% 
  |================================        |  80% 
  |========================================| 100% 

#> Using 3 of 3 events (100%) whose junctions are present in junction quantification data...
#>   |                                        |   0% 
  |========                                |  20% 
  |================                        |  40% 
  |========================                |  60% 
  |================================        |  80% 
  |========================================| 100% 


geneExpr <- readFile("ex_gene_expression.RDS")
corr <- correlateGEandAS(geneExpr, psi, "ALDOA")

# Quick display of the correlation results per splicing event and gene
print(corr)
#> ================================================================================
#> SE_2_+_32_35_37_38_ALDOA splicing event
#> ALDOA|226 gene expression
#> 
#> 	Pearson's product-moment correlation
#> 
#> data:  exprNum and eventPSInum
#> t = -0.7542, df = 4, p-value = 0.4927
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#>  -0.9051981  0.6427793
#> sample estimates:
#>        cor 
#> -0.3528456 
#> 
#> ================================================================================
#> MXE_2_+_32_35_37_38_40_42_ALDOA splicing event
#> ALDOA|226 gene expression
#> 
#> 	Pearson's product-moment correlation
#> 
#> data:  exprNum and eventPSInum
#> t = -0.26642, df = 4, p-value = 0.8031
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#>  -0.8522745  0.7610748
#> sample estimates:
#>        cor 
#> -0.1320457 
#> 

# Table summarising the correlation analysis results
as.table(corr)
#>        Alternative splicing event      Gene
#> 1        SE 2 + 32 35 37 38 ALDOA ALDOA|226
#> 2 MXE 2 + 32 35 37 38 40 42 ALDOA ALDOA|226
#>   Pearson's product-moment correlation   p-value p-value (BH adjusted)
#> 1                           -0.3528456 0.4926962             0.8030827
#> 2                           -0.1320457 0.8030827             0.8030827

# Correlation analysis plots
colourGroups <- list(Normal=paste("Normal", 1:3),
                     Tumour=paste("Cancer", 1:3))
attr(colourGroups, "Colour") <- c(Normal="#00C65A", Tumour="#EEE273")
plot(corr, colourGroups=colourGroups, alpha=1)
#> $`SE_2_+_32_35_37_38_ALDOA`
#> $`SE_2_+_32_35_37_38_ALDOA`$`ALDOA|226`

#> 
#> 
#> $`MXE_2_+_32_35_37_38_40_42_ALDOA`
#> $`MXE_2_+_32_35_37_38_40_42_ALDOA`$`ALDOA|226`

#> 
#>