3.Differential Expression
Differential Expression for bulk RNAs
Pipeline

Data Structure
Inputs
File format
Information contained in file
File description
Notes
txt
miRNA.homer.ct.mx
Raw counts matrix
Used for edgeR and DESeq2 analysis
txt
miRNA.homer.rpm.mx
RPM matrix
Used for wilcoxon test
txt
miRNA.NCvsHCC.DESeq2.ids.rpkm.mx
RPKM matrix
Used for clustering plot
txt
design.txt
Sample info.
Classification information
Outputs
File format
Information contained in file
File description
Notes
tsv
miRNA.NCvsHCC.edgeR.tsv
EdgeR result
LogFC, PValue and FDR
txt
miRNA.homer.DESeq2.rlog.mx
DESeq2 result
Rlog normalized counts
tsv
miRNA.NCvsHCC.DESeq2.tsv
DESeq2 result
LogFC, PValue and padj
tsv
miRNA.NCvsHCC.wilcox.tsv
Wilcox result
LogF, PVaule and FDR
miRNA.NCvsHCC.DESeq2.norm.pdf
Heatmap result
Rlog normalized count
miRNA.NCvsHCC.DESeq2.pca.pdf
Scatter plot
PCA analysis
miRNA.NCvsHCC.DESeq2.MAplot.pdf
Scatter plot
MAplot analysis
miRNA.NCvsHCC.DESeq2.Dist.pdf
Heatmap result
Sample distance
miRNA.NCvsHCC.DESeq2.ids.DE.pdf
Heatmap result
DE genes
Running Scripts
Software/Tools
Assumption for most normalization and differential expression analysis tools: The expression levels of most genes are similar, i.e., not differentially expressed.
a) DEseq: defines scaling factor (also known as size factor) estimates based on a pseudoreferencesample, which is built with the geometric mean of gene counts across all cells (samples).
b) EdgeR (TMM): trimmed mean of M values

c) Wilcox Test using RPM: Read counts Per Million of total mapped reads; alternatives: RPKM, TPM
Performance:

Example of single case
# experimential design
design <- read.table("design.txt",sep="\t",header=T)
# expression matrix
mx <- read.table("miRNA.homer.ct.mx",sep="\t",header=T)
# filter genes
filter_genes <- apply(
mx[,2:ncol(mx)],
1,
function(x) length(x[x > 2]) >= 2
)
mx_filterGenes <- mx[filter_genes,]
Draw Plots
1. Heatmap for DESeq2 normalized count matrix

2. PCA analysis

3. MA plot

4. Distance between samples

5. Hierarchical clustering for differential expressed genes

Tips/Utilities
Homework and more
Identify differential expressed genes for other RNA types. between differential conditions, i.e. Normal Control (NC) V.S. HCC using three methods: edgeR, DESeq2 and Wilcox/Mann-Whitney-U Test.
Draw Venn plot to show the difference among the above three methods.
Additional Tutorial : 3. Differential Expression Analysis
Video
1. Differential expression
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