plot2TX: Estimate And Plot Transcript Proportion Difference

Description Usage Arguments Value Examples

View source: R/plot2TX.R

Description

For any compared two replicates in each cell line, the proportion of one transcript for genes that only include two annotated transcripts can be different even flipped. This function estimates and plots the proportion difference stratefied by detrended logsignal. Means of absolute difference will be reported for three levels of detrened logsignals. Average is used when multiple two-replicate comparisons included.

Usage

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plot2TX(dat, genes, step = 0.5, thresholds = c(1, 6), plotcell = 1, ...)

Arguments

dat

A rnaseqcomp S4 class object.

genes

A vector of gene names corresponding to quantified transcripts. Note that length(genes) should equal to nrow([email protected][[1]]).

step

A number specifying the resolution on detrended logsignal for calculation and plotting the proportion difference. (default: 0.5)

thresholds

A vector of two numbers define cutoffs for three levels of detreded log signals, where one number summary will be generated. (default: c(1, 6))

plotcell

1 or 2 indicating which cell line will be plotted. If values other than 1 and 2, both cell lines will be plotted. This value won't affect estimation for both cell lines. (default: 1)

...

Parameters for base function plot.

Value

plot

2TX plots of quantification pipelines for selected cell line by plotcell.

list

A list of two matrices indicating the mean and standard error of absolute proportion differences. Valuesa are based on average of two cell lines.

Examples

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data(simdata)
condInfo <- factor(simdata$samp$condition)
repInfo <- factor(simdata$samp$replicate)
evaluationFeature <- rep(TRUE, nrow(simdata$meta))
calibrationFeature <- simdata$meta$house & simdata$meta$chr == 'chr1'
unitReference <- 1
dat <- signalCalibrate(simdata$quant, condInfo, repInfo, evaluationFeature,
calibrationFeature, unitReference, calibrationFeature2 = calibrationFeature)
plot2TX(dat,genes=simdata$meta$gene)

rnaseqcomp documentation built on May 2, 2018, 4:11 a.m.