rnaseqcomp (version 1.0.0)

plotCAT: CAT Plots of Differential Express Features

Description

For each pipeline, 2 biological conditions (cell lines) each with 2 quantification replicates are compared and fold changes of each replicate are calculated. Then, CAT plots between replicates (precision) or between mean of replicates and other technology (accuracy).

Usage

plotCAT(dat1, dat2, constant = NULL, otherFC = NULL, infinity = FALSE, step = 5L, type = "l", lwd = 2, col = NULL, lty = 1, xlim = c(20L, 500L), ylim = c(0, 1), xlab = "Size of List", ylab = "Proportion in Common", cex.leg = 0.6, ...)

Arguments

dat1,dat2

rnaseqcomp S4 class objects for two conditions. dat1 and dat2 should have the same size of slot quantData and the same repInfo.

constant

A numeric constant that can be added to quantifications before fold changes calculation (default: NULL).

otherFC

A numeric vector of fold changes by other independent technology such as microarray, with each elements corresponding to rows of quantData slot in dat1 or dat2. Missing data NA allowed. (default: NULL)

infinity

A logical indicator that specify if fold change of infinity should be considered. Functional only if constant is not a positive number. (default: FALSE)

step

Plot steps on x-axis (default: 5).

type

Plot types (default: 'l').

lwd

Plot line weights (default: 2).

col

Plot colors (default: NULL, colors are assigned by package RColorBrewer).

lty

Plot line styles (default: 1).

xlim

Plot limits of x-axis (default: c(20, 500)).

ylim

Plot limits of y-axis (default: c(0, 1)).

xlab

Plot label of x-axis (default: 'Size of List').

ylab

Plot label of y-axis (default: 'Proportion in Common').

cex.leg

Legend size (default: 0.6).

...

Other parameters for base function plot.

Value

CAT plot

CAT plots for all the quantification pipelines.

Precision or Accuracy

A numeric vector of pipeline precision or accuracy, depending on whether otherFC is provided.

Examples

data(encodeCells)
evaluationFeature <- encodeCells$genemeta$type == "protein_coding"
calibrationFeature <- encodeCells$genemeta$housekeeping
unitReference <- grepl("Cufflinks",encodeCells$repInfo)
dat1 <- matrixFilter(encodeCells$gm12878,encodeCells$repInfo,
    evaluationFeature,calibrationFeature,unitReference)
dat2 <- matrixFilter(encodeCells$k562,encodeCells$repInfo,
    evaluationFeature,calibrationFeature,unitReference)

plotCAT(dat1,dat2)
plotCAT(dat1,dat2,infinity=TRUE)

genes <- encodeCells$genemeta[encodeCells$genemeta$type ==
    "protein_coding", 1]
otherFC <- encodeCells$arrayFC[match(genes,names(encodeCells$arrayFC))]
plotCAT(dat2,dat1,otherFC=otherFC)
plotCAT(dat2,dat1,constant=1,otherFC=otherFC)