5 Differential Binding Region - DiffBind

Based on 71231 consensus peaks (see here)

Software package used: DiffBind

Background normalisation method using DiffBind3

li.diff<-list()
li.diff[['bg']][["female"]]<-dba(dbo.cnt,dbo.cnt$masks$F) # isa DBA
li.diff[['bg']][["male"]]<- dba(dbo.cnt,dbo.cnt$masks$M) # isa DBA

li.diff[['bg']]<-lapply(li.diff[['bg']], dba.normalize, method=DBA_ALL_METHODS,normalize=DBA_NORM_NATIVE,library=DBA_LIBSIZE_FULL,background=TRUE)
li.diff[['bg']]<-lapply(li.diff[['bg']], dba.contrast, design="~Replicate+Factor")
li.diff[['bg']]<-lapply(li.diff[['bg']], dba.analyze, method=DBA_ALL_METHODS)

lapply(li.diff[['bg']], dba.show, bContrasts=TRUE)
lapply(li.diff[['bg']][c('male')], dba.report, method=DBA_ALL_METHODS,bDB=T,bGain=T,bLoss=T)
lapply(li.diff[['bg']], dba.normalize, bRetrieve=T,method=DBA_ALL_METHODS)[["female"]]

5.1 No. of DBR

5.1.1 adjutedd P-val<0.05

Table 5.1: Number of differential binding regions
Factor Group Samples Group2 Samples2 DB.edgeR DB.DESeq2
Factor FD 5 FV 5 0 0
Factor MD 6 MV 6 213 147 
    lapply(li.diff[['bg']][c('male')], dba.report, method=DBA_ALL_METHODS,bDB=T,bGain=T,bLoss=T)
## $male
## 6 Samples, 246 sites in matrix:
##    Contrast Direction DB Method Intervals
## 1 MD vs. MV       All DB  edgeR       213
## 2 MD vs. MV      Gain DB  edgeR        21
## 3 MD vs. MV      Loss DB  edgeR       192
## 4 MD vs. MV       All DB DESeq2       147
## 5 MD vs. MV      Gain DB DESeq2        10
## 6 MD vs. MV      Loss DB DESeq2       137
## $male
## 2 Samples, 246 sites in matrix:
##    Contrast Direction DB Method Intervals
## 1 MD vs. MV       All DB  edgeR       213
## 2 MD vs. MV       All DB DESeq2       147
DBR between edgeR & DESeq2 (MD vs MV)

Figure 5.1: DBR between edgeR & DESeq2 (MD vs MV)

PCA based on DBR (MD vs MV)

Figure 5.2: PCA based on DBR (MD vs MV)

PCA based on DBR (FD vs FV)

Figure 5.3: PCA based on DBR (FD vs FV)

5.1.2 adjutedd P-val<0.01:

Table 5.2: Number of differential binding regions
Factor Group Samples Group2 Samples2 DB.edgeR DB.DESeq2
Factor FD 5 FV 5 0 0
Factor MD 6 MV 6 51 28

5.2 Genomic feature of DBRs

Table 5.3: % of genomic features
Feature DESeq2 edgeR
Promoter (<=1kb) 24.489796 30.516432
Promoter (1-2kb) 6.802721 9.859155
3’ UTR 5.442177 4.225352
1st Exon 2.040816 1.877934
Other Exon 2.721088 3.755869
1st Intron 10.204082 7.981221
Other Intron 17.687075 15.962441
Downstream (<=300) 2.040816 1.408451
Distal Intergenic 28.571429 24.413145
Table 5.4: No of peaks by genomic features
Feature DESeq2 edgeR
Promoter (<=1kb) 36 65
Promoter (1-2kb) 10 21
3’ UTR 8 9
1st Exon 3 4
Other Exon 4 8
1st Intron 15 17
Other Intron 26 34
Downstream (<=300) 3 3
Distal Intergenic 42 52
Genomic feautres of peaks

Figure 5.4: Genomic feautres of peaks

5.3 DBR from DESeq2

5.3.1 Normalisation factors

Normalisation factors by group

Figure 5.5: Normalisation factors by group

5.3.2 Male (MD vs MV)

MA plot (DESeq2)

Figure 5.6: MA plot (DESeq2)

Volcano plot (DESeq2)

Figure 5.7: Volcano plot (DESeq2)

5.4 DBR from edgeR

5.4.1 Normalisation factors

Normalisation factors by group

Figure 5.8: Normalisation factors by group

5.4.2 Male (MD vs MV)

MA plot (edgeR)

Figure 5.9: MA plot (edgeR)

Volcano plot (edgeR)

Figure 5.10: Volcano plot (edgeR)

5.5 Normalisation parameters

lapply(li.diff[['bg']], dba.normalize, bRetrieve=T,method=DBA_ALL_METHODS)
## $female
## $female$edgeR
## $female$edgeR$background
## [1] TRUE
## 
## $female$edgeR$norm.method
## [1] "TMM"
## 
## $female$edgeR$norm.factors
##  [1] 1.0037253 1.0178863 0.9796488 1.0187121 1.0037716 1.0066757 1.0061241 1.0289548 0.9727954 0.9637627
## 
## $female$edgeR$lib.method
## [1] "background"
## 
## $female$edgeR$lib.sizes
##  [1] 14406659 14313568  9052547  8273839 10782259 10634010  7783031  8816437 10163025 15059456
## 
## $female$edgeR$control.subtract
## [1] TRUE
## 
## $female$edgeR$filter.value
## [1] 1
## 
## 
## $female$DESeq2
## $female$DESeq2$background
## [1] TRUE
## 
## $female$DESeq2$norm.method
## [1] "RLE"
## 
## $female$DESeq2$norm.factors
##   F1V-K27   F1D-K27   F2V-K27   F2D-K27   F3V-K27   F3D-K27   F4V-K27   F4D-K27   F5V-K27   F5D-K27 
## 1.3711165 1.3802589 0.8437394 0.7962241 1.0251811 1.0144064 0.7400174 0.8575304 0.9419272 1.3851355 
## 
## $female$DESeq2$lib.method
## [1] "background"
## 
## $female$DESeq2$lib.sizes
##  [1] 14406659 14313568  9052547  8273839 10782259 10634010  7783031  8816437 10163025 15059456
## 
## $female$DESeq2$control.subtract
## [1] TRUE
## 
## $female$DESeq2$filter.value
## [1] 1
## 
## 
## $female$background
## $female$background$binned
## class: RangedSummarizedExperiment 
## dim: 188762 10 
## metadata(6): spacing width ... param final.ext
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames(10): F1V-K27 F1D-K27 ... F5V-K27 F5D-K27
## colData names(4): bam.files totals ext rlen
## 
## $female$background$bin.size
## [1] 15000
## 
## $female$background$back.calc
## [1] "Background bins"
## 
## 
## 
## $male
## $male$edgeR
## $male$edgeR$background
## [1] TRUE
## 
## $male$edgeR$norm.method
## [1] "TMM"
## 
## $male$edgeR$norm.factors
##  [1] 1.0475833 1.0580352 0.9403566 0.9527507 1.0195866 1.0227243 0.9939970 1.0574761 0.9178885 0.9608961 1.0178277
## [12] 1.0234355
## 
## $male$edgeR$lib.method
## [1] "background"
## 
## $male$edgeR$lib.sizes
##  [1]  7842803  6256923  7620403 45662054 16275364 12003441 13043263 14752067 14301167 13951545 10101914  5878181
## 
## $male$edgeR$control.subtract
## [1] TRUE
## 
## $male$edgeR$filter.value
## [1] 1
## 
## 
## $male$DESeq2
## $male$DESeq2$background
## [1] TRUE
## 
## $male$DESeq2$norm.method
## [1] "RLE"
## 
## $male$DESeq2$norm.factors
##   M1V-K27   M1D-K27   M2V-K27   M2D-K27   M3V-K27   M3D-K27   M4V-K27   M4D-K27   M5V-K27   M5D-K27   M6V-K27 
## 0.6945813 0.5609957 0.6119628 3.7079627 1.4081663 1.0398274 1.1014219 1.3339729 1.1196840 1.1447977 0.8711595 
##   M6D-K27 
## 0.5085670 
## 
## $male$DESeq2$lib.method
## [1] "background"
## 
## $male$DESeq2$lib.sizes
##  [1]  7842803  6256923  7620403 45662054 16275364 12003441 13043263 14752067 14301167 13951545 10101914  5878181
## 
## $male$DESeq2$control.subtract
## [1] TRUE
## 
## $male$DESeq2$filter.value
## [1] 1
## 
## 
## $male$background
## $male$background$binned
## class: RangedSummarizedExperiment 
## dim: 189298 12 
## metadata(6): spacing width ... param final.ext
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames(12): M1V-K27 M1D-K27 ... M6V-K27 M6D-K27
## colData names(4): bam.files totals ext rlen
## 
## $male$background$bin.size
## [1] 15000
## 
## $male$background$back.calc
## [1] "Background bins"