Correction Performance Visualization

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Description

Pairwise comparison matrix of the original and corrected measurements

Usage

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posplot(test.corrected, pos.test, n.lim = 1000, fit.cut.lr = 50)

Arguments

test.corrected

a data matrix from correct.guided function

pos.test

a data matrix of test transcripts' position information

n.lim

the maximum number of log ratios points will be plotted

fit.cut.lr

the minimum number of sequencing reads points will be used for log ratio plotting

Details

Distributions of sequencing reads of samples measured before and after correction will be plotted with the positions on the x-axis and the number of sequencing reads as bars. Also patterns of the fluctuation factor, log ratio before the correction (black line) and log ratio after the correction (blue line) will be plotted.

Value

a plot produced on the current graphic device

Author(s)

Guoshuai Cai

References

Cai G, RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION, Ph.D. Thesis, 2013

See Also

correct.guided, counts.preprocess

Examples

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word<-81

data(obj.index)
data(train.dat.seq)
data(train.dat.counts)
data(test.dat.seq)
data(test.dat.counts)

#train

train.index<-index.preprocess(train.dat.seq,word)
obj.train<-counts.preprocess(train.dat.counts)
obj.train[["index"]]<-train.index

coe.lm<-lm.estimate(obj.train,fit.cut.train=5)

#test

test.index<-index.preprocess(test.dat.seq,word)
obj.test<-counts.preprocess(test.dat.counts)
obj.test[["index"]]<-test.index

test.corrected<-correct.guided(coe.lm,obj.test)

posplot(test.corrected,obj.test$pos)