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. |