timeSeq.heatmap: Heatmap of the Most Significant NDPE Genes

In timeSeq: Detecting Differentially Expressed Genes in Time Course RNA-Seq Data

Description

Heatmap for the most significant NDPE genes.

Usage

timeSeq.heatmap(timeSeq.obj,n)

Arguments

timeSeq.obj	an object returned by timeSeq function
n	the number of the most significant NPDE genes. It must be a positive integer.

Author(s)

Fan Gao and Xiaoxiao Sun

References

Sun, Xiaoxiao, David Dalpiaz, Di Wu, Jun S. Liu, Wenxuan Zhong, and Ping Ma. "Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model." BMC Bioinformatics, 17(1):324, 2016.

Chong Gu. Model diagnostics for smoothing spline ANOVA models. Canadian Journal of Statistics, 32(4):347-358, 2004.

Chong Gu. Smoothing spline ANOVA models. Springer, second edition, 2013.

Chong Gu and Ping Ma. Optimal smoothing in nonparametric mixed-effect models. Annals of Statistics, pages 1357-1379, 2005a.

Wood (2001) mgcv:GAMs and Generalized Ridge Regression for R. R News 1(2):20-25

Examples

data(simulate.dt)
attach(simulate.dt)
model.fit <- timeSeq(data.count,group.label,gene.names,exon.level=FALSE,pvalue=TRUE)
timeSeq.heatmap(model.fit,n=10)

timeSeq documentation built on May 2, 2019, 3:07 a.m.