All functions

DE.edgeR()

Differential Expression Analysis Using EdgeR

DE.statistics()

Statistics for DEA Results Based Golden Standards

DE.voom()

Differential Expression Analysis Using Voom-limma Pipeline

data.benchmark

MiRNA Sequencing Benchmark Data

data.group

Sample Labels of the test and benchmark data

data.miR.info

MiRNA Information

data.simulation

Simulation Plan

data.test

MiRNA Sequencing Test Data

fig.CAT()

Concordance At The Top Plot

fig.FDR_FNR()

Selection of normalization methods based on golden standards (FDR and FNR)

fig.FDR_FNR.boxplot()

Boxplot of FDR and FNR for Simulated data

fig.RLE()

Relative Log Expression Plot

fig.dendrogram()

Dendrogram for clustering p-values

fig.venn()

Venn diagram for p-values

fig.volcano()

Volcano Figure

norm.DESeq()

Normalization By DESeq (DESeq)

norm.PoissonSeq()

Normalization By PoissonSeq (PoissonSeq)

norm.QN()

Normalization By Quantile Normalization (QN)

norm.RUVg()

Normalization By Remove Unwanted Variation Using Control Genes (RUVg)

norm.RUVr()

Normalization By Remove Unwanted Variation Using Residuals (RUVr)

norm.RUVs()

Normalization By Remove Unwanted Variation Using Replicate Samples (RUVs)

norm.SVA()

Normalization By Surrogate Variable Analysis for Sequencing Data (SVA)

norm.TC()

Normalization By Total Count (TC)

norm.TMM()

Normalization By Trimmed Mean of M-values (TMM)

norm.UQ()

Normalization By Upper Quantile (UQ)

norm.med()

Normalization By Median (Med)

pip.norm.DE()

Pipeline of Differential Expression Analysis for RNASeq Data

pip.norm()

Normalization for RNASeq Data

pip.simulated.data()

Full normalization assessment for simulated data

precision.seq()

Full normalization assessment for given normalized test data

simulated.data()

Simulated Data

simulation.algorithm()

The Algorithm for Obtaining the Simulation Plan This algorithm is tailored for the data.benchmark included in this package.