Choose simulation study
Simulation 1 (cancer tissues, Zhang)
Simulation 2 (cultured cell lines, NGP nutlin)
Simulation 3 (normal tissues, GTEx)
Choose DE tool
All
DESeq
DESeq2 (default)
DESeq2 (Setting 1)
DESeq2 (Setting 2)
edgeR exact
edgeR GLM
edgeR QL
edgeR robust (pDF=10)
edgeR robust (pDF=20)
edgeR robust (pDF=5)
edgeR robust (pDF='auto')
limmaQN
limmaTrend
limmaTrend (robust)
limmaVoom
limmaVoom (robust)
limmaVoom+QW
limmaVst
NOISeq
PoissonSeq
QuasiSeq (QL)
QuasiSeq (QLShrink)
QuasiSeq (QLSpline)
SAMSeq
Choose performance metrics
TPR
FDR
FNR
TNR
FPR
Choose gene biotype
mRNA
lncRNA
Number of samples per group
Number of samples per group
Number of samples per group
Proportion of true DE genes
Nominal FDR threshold
Adjust X scales
Plot type
wrap
gride
Adjust X scales (for ROC curve)
Adjust Y scales (for ROC curve)
Show features of simulation source RNA-seq datasets
Show features of simulated gene expression data
Choose DE tool
DESeq
DESeq2 (default)
DESeq2 (Setting 1)
DESeq2 (Setting 2)
edgeR exact
edgeR GLM
edgeR QL
edgeR robust (pDF=10)
edgeR robust (pDF=20)
edgeR robust (pDF=5)
edgeR robust (pDF='auto')
limmaQN
limmaTrend
limmaTrend (robust)
limmaVoom
limmaVoom (robust)
limmaVoom+QW
limmaVst
NOISeq
PoissonSeq
QuasiSeq (QL)
QuasiSeq (QLShrink)
QuasiSeq (QLSpline)
SAMSeq
ROC curve
Indicate the nominal FDR threshold
Performance metrics at different number of replicates
Performance metrics at different proportion of true DE genes
Performance metrics at different nominal FDR
Adjust Y scales
Adjust X scales
Summary statistics
Distribution of average read counts
Estimates of biological coefficient of variation
Multidimensional scaling plot
Choose biotype
All
mRNA
lncRNA
Filtration method
minimum read counts
CPM
Minimum read counts per condition
Minimum mean CPM
Normalization method
DESeq
TMM
QN
SAMSeq
PoissonSeq
Number of bins
Free axis scales
Adjust X scales
Adjust Y scales
Choose biotype
All
mRNA
lncRNA
Adjust X scales
Adjust Y scales
Free axis scales
Adjust X scales
Adjust Y scales
DE too to analyse the full(source) dataset
DESeq2
Wilcoxon rank sum test
limma t-test
Nominal FDR
Choose biotype
All
mRNA
lncRNA
Nominal FDR
Adjust X scales
Adjust Y scales
Choose type of summary
Proportion of biotypes in the simulated counts
Proportion of biotypes in the set of DE genes within each simulated counts
Proportion of DE genes in the set of each biotype within each simulated counts
Choose X-axis
Replicate size at a fixed proportion of DE genes
Proportion of true DE genes at a fixed replicate size
Proportion of true DE genes
Number of replicates per group
Adjust Y-axis
Click here to see simulation quality assessment (Zhang)
Click here to see simulation quality assessment (NGP Nutlin)
Click here to see simulation quality assessment (GTEx)
Choose DE tool
DESeq
DESeq2 (default)
DESeq2 (Setting 1)
DESeq2 (Setting 2)
edgeR exact
edgeR GLM
edgeR QL
edgeR robust (pDF=10)
edgeR robust (pDF=20)
edgeR robust (pDF=5)
edgeR robust (pDF='auto')
limmaQN
limmaTrend
limmaTrend (robust)
limmaVoom
limmaVoom (robust)
limmaVoom+QW
limmaVst
NOISeq
PoissonSeq
QuasiSeq (QL)
QuasiSeq (QLShrink)
QuasiSeq (QLSpline)
SAMSeq
Choose attributes
Tool full name
Tool short name
Normalization method
Count distribution assumption
Description
Possible number of factors to be used
Integrated outlier adjustment method
Available in package
Package version
Tool reference
Package reference
Show all DE tools in a table
Go to the first page
Performance of all DGE tools
Performance of specific DGE tool
Features of source datasets
Features of simulated data
DE Tools
Description
Performance of selected DE tool(s)
Performance of selected DE tool across different simulation scenarios
Characteristics of source RNA-seq datasets
Note
Features of simulated gene expression data
Notes