Anton Enright
r format(Sys.Date())
All data were pre-processed using minion to identify and check adapters, reaper to trim adapter sequences followed by tally to deduplicate reads while maintaining depth information. Subsequent to this all reads passed through the mirmod pipeline against all miRBase (Release 21) precursor sequences for Mouse and Zebrafish. Reads were summed across paired end sequences for the same read pair. Finally reads are loaded into R for final analysis.
This is the sample description file used for the analyses below.
| Name | File | Barcodes | 3p_ad |
|---|---|---|---|
| wt1 | A638S1.R1.fastq.gz | no_barcode | AGATCGGAAGAGCACA |
| wt2 | A638S2.R1.fastq.gz | no_barcode | AGATCGGAAGAGCACA |
| wt3 | A638S3.R1.fastq.gz | no_barcode | AGATCGGAAGAGCACA |
| mt1 | A638S4.R1.fastq.gz | no_barcode | AGATCGGAAGAGCACA |
| mt2 | A638S5.R1.fastq.gz | no_barcode | AGATCGGAAGAGCACA |
| mt3 | A638S6.R1.fastq.gz | no_barcode | AGATCGGAAGAGCACA |
We first load the R/BioConductor libraries that we need.
library(RColorBrewer)
library(gplots)
library(DESeq2)
library(reshape2)
library(ggplot2)
hmcol = colorRampPalette(brewer.pal(9, "GnBu"))(100)
spectral <- colorRampPalette(rev(brewer.pal(11, "Spectral")), space="Lab")(100)We can now load all the count data
setwd("/Users/aje/anton_r_notebook/alena_mirna_oct_2016/")
mircounts <- read.table("mouse_counts_mar_2017.txt",header=TRUE,row.names=1)
mircounts=mircounts[-nrow(mircounts),]As well as the pdata, which contains information on each sample.
pdata <- read.table("pdata_mar_2017.txt",header=TRUE,row.names=1)
#pdata=pdata[c(1,2,5,6),]
#mircounts=mircounts[,c(1,2,5,6)]
colnames(mircounts)=rownames(pdata)
conds=as.factor(as.character(pdata$Genotype))We are now ready to create a DESeq object from the counts table.
#Lets Load the Counts First
coldata = as.data.frame(t(t(conds)))
rownames(coldata)=colnames(mircounts)
colnames(coldata)='treatment'
dds <- DESeqDataSetFromMatrix(countData = mircounts, colData = coldata, design = ~ treatment)We are ready to normalise the data, but first we should look at the number of sequenced reads per sample.
cond_colours = c("#E41A1C","#377EB8")[as.factor(conds)]
names(cond_colours)=conds
group_colours = brewer.pal(length(rownames(pdata)),"Accent")[as.factor(rownames(pdata))]
names(group_colours)=rownames(pdata)
quartz()
barplot(apply(mircounts,2,sum), las=2,col=cond_colours,main="Pre Normalised Counts",cex.names=0.4)
legend("topright",levels((conds)),cex=0.6,fill=cond_colours[levels(conds)])
We will also estimate the negative binomial dispersion of the data.
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
quartz()
plotDispEsts(dds)
Now we can normalise and plot the counts again.
normcounts <- counts(dds, normalized=TRUE)
rawcounts=counts(dds,normalized=FALSE)
log2counts=log2(normcounts+1)
quartz()
barplot(apply(normcounts,2,sum), las=2,col=cond_colours,main="Post-Normalised Counts",cex.names=0.4)
legend("topright",levels((conds)),cex=0.6,fill=cond_colours[levels(conds)])
We will apply the Variance Stabilising Transformation (VST) it's better than log2 for counts.
vsd <- varianceStabilizingTransformation(dds)
vstcounts <- assay(vsd)
vstcounts <- vstcounts[order(apply(vstcounts,1,sum),decreasing =TRUE),]As an additional QC step we can calculate the sample-to-sample Pearson correlations and plot them in a heatmap.
quartz()
heatmap.2(cor(rawcounts),trace="none",col=hmcol,main="Sample to Sample Correlation (Raw Counts)",cexRow=0.5,cexCol=0.5,RowSideColors=cond_colours, margins=c(9,7))
quartz()
heatmap.2(cor(vstcounts),trace="none",col=hmcol,main="Sample to Sample Correlation (VST)",cexRow=0.5,cexCol=0.5,RowSideColors=cond_colours,margins=c(9,7))
We can also perform PCA.
pca <- princomp(vstcounts)
quartz()
plot(pca$loadings, col=cond_colours, pch=19, cex=2, main="Sample to Sample PCA (VST)")
text(pca$loadings, as.vector(colnames(mircounts)), pos=3, cex=0.4)
legend("topright",levels(conds),fill=cond_colours[levels(conds)],cex=0.4)
PCA of the top 3 Principal Components.
pca2=prcomp(t(vstcounts),center=TRUE)
quartz()
par(mfrow=c(1,3))
plot(pca2$x, col=cond_colours, pch=19, cex=2, main="Sample to Sample PCA (VST)")
text(pca2$x, as.vector(colnames(mircounts)), pos=3, cex=0.4)
plot(pca2$x[,1],pca2$x[,3], col=cond_colours, pch=19, cex=2, main="Sample to Sample PCA (VST)",ylab="PC3",xlab="PC1")
text(pca2$x[,1],pca2$x[,3], as.vector(colnames(mircounts)), pos=3, cex=0.4)
plot(pca2$x[,2],pca2$x[,3], col=cond_colours, pch=19, cex=2, main="Sample to Sample PCA (VST)",ylab="PC3",xlab="PC2")
text(pca2$x[,2],pca2$x[,3], as.vector(colnames(mircounts)), pos=3, cex=0.4)
Here are the top10 microRNAs.
top10=apply(mircounts,1,sum)[1:10]
top10[11]=sum(apply(mircounts,1,sum)[11:nrow(mircounts)])
names(top10)[11]="other"
pie(top10,col=brewer.pal(11,"Set3"),main="Top10 microRNAs")
This is the expression of the top10 microRNAs sample to sample.
heatmap.2(vstcounts[names(top10)[1:10],],col=hmcol,trace="none",cexCol=0.4,cexRow=0.6,ColSideColors=cond_colours)
This is the expression of the miR-29 families of microRNAs sample to sample.
heatmap.2(vstcounts[rownames(mircounts)[grep("mir-29[a-z]",rownames(mircounts))],],col=hmcol,trace="none",cexCol=0.4,cexRow=0.6,ColSideColors=cond_colours)
quartz()
barplot(t(vstcounts[rownames(mircounts)[grep("mir-29[a-z]",rownames(mircounts))],]),beside=T,las=2,cex.names=0.5,col=cond_colours,main="miR-29 levels (VST)")
legend("topright",rownames(pdata),fill=cond_colours,cex=0.4)
Run the statistical contrast on the count data
p_threshold=0.05
lfc_threshold=0.75
cds <- nbinomWaldTest(dds)
res=results(cds,contrast=c("treatment","wt","mut"))
res <- res[order(res$padj),]
res## log2 fold change (MAP): treatment wt vs mut
## Wald test p-value: treatment wt vs mut
## DataFrame with 1471 rows and 6 columns
## baseMean log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric> <numeric>
## mmu-mir-708-5p 370.21877 -3.880212 0.5208669 -7.449528
## mmu-mir-219-2-3p 2508.84257 -3.586149 0.6161118 -5.820615
## mmu-mir-204-5p 595.61606 3.605966 0.6224714 5.792982
## mmu-mir-219-2-5p 89.54399 -2.535594 0.4502091 -5.632036
## mmu-mir-10b-5p 632527.44731 -2.852560 0.5180408 -5.506439
## ... ... ... ... ...
## mmu-mir-875-3p 0.12715984 -0.42370466 1.021558 -0.41476320
## mmu-mir-882-5p 0.22104968 -0.42370466 1.021558 -0.41476320
## mmu-mir-489-5p 0.08632002 0.05517826 1.021749 0.05400371
## mmu-mir-804-3p 0.21019972 0.26690316 1.024176 0.26060278
## mmu-mir-142-5p 0.12715984 -0.42370466 1.021558 -0.41476320
## pvalue padj
## <numeric> <numeric>
## mmu-mir-708-5p 9.367487e-14 8.814805e-11
## mmu-mir-219-2-3p 5.863146e-09 2.168921e-06
## mmu-mir-204-5p 6.914734e-09 2.168921e-06
## mmu-mir-219-2-5p 1.780944e-08 4.189672e-06
## mmu-mir-10b-5p 3.661657e-08 6.891239e-06
## ... ... ...
## mmu-mir-875-3p 0.6783153 NA
## mmu-mir-882-5p 0.6783153 NA
## mmu-mir-489-5p 0.9569322 NA
## mmu-mir-804-3p 0.7943988 NA
## mmu-mir-142-5p 0.6783153 NA
sig = rownames(res[(abs(res$log2FoldChange) > lfc_threshold) & (res$padj < p_threshold) & !is.na(res$padj),])Volcanoplots of Significant Hits
plot(res$log2FoldChange,-log(res$padj,10),ylab="-log10(Adjusted P)",xlab="Log2 FoldChange",main=paste("Volcano Plot","WT v Scr\nmir-29 in green\nsig. in red"),pch=19,cex=0.4)
points(res[sig,"log2FoldChange"],-log(res[sig,"padj"],10),pch=19,cex=0.4,col="red")
text(res[sig[1:10],"log2FoldChange"],-log(res[sig[1:10],"padj"],10),pch=19,cex=0.4,pos=2,labels = rownames(res[sig[1:10],]))
points(res[rownames(mircounts)[grep("mir-29[a-z]",rownames(mircounts))],"log2FoldChange"],-log(res[rownames(mircounts)[grep("mir-29[a-z]",rownames(mircounts))],"padj"],10),pch=19,cex=0.6,col="green")
text(res[rownames(mircounts)[grep("mir-29[a-z]",rownames(mircounts))],"log2FoldChange"],-log(res[rownames(mircounts)[grep("mir-29[a-z]",rownames(mircounts))],"padj"],10),pch=19,cex=0.4,pos=2,labels =rownames(mircounts)[grep("mir-29[a-z]",rownames(mircounts))])
abline(h=-log10(p_threshold),lty=3)
abline(v=-lfc_threshold,lty=3)
abline(v=lfc_threshold,lty=3) 
wt_median = apply(vstcounts[,pdata$Genotype == "wt"],1,median)
mt_median = apply(vstcounts[,pdata$Genotype == "mut"],1,median)
plot(wt_median,mt_median,cex=0.4,pch=19,col="darkblue")
points(wt_median[grep("mir-29[a-z]",rownames(vstcounts))],mt_median[grep("mir-29[a-z]",rownames(vstcounts))],cex=0.4,pch=19,col="green")
points(wt_median[sig],mt_median[sig],cex=1,col="red")
text(wt_median[grep("mir-29[a-z]",rownames(vstcounts))],mt_median[grep("mir-29[a-z]",rownames(vstcounts))],cex=0.4,pos=3,labels=rownames(vstcounts)[grep("mir-29[a-z]",rownames(vstcounts))])
abline(a=0,b=1,lty=2,col="red")
Heatmap of significant hits.
heatmap.2(vstcounts[sig,],trace="none",ColSideColors = cond_colours,col=hmcol,margins=c(5,5),cexRow=0.5,cexCol=0.6,labCol=paste(rownames(pdata),pdata$SampleName,sep="\n"),main="Significant Hits Heatmap (VST)")
Let's output the final results table with normalised expression values and stats listed
write.table(cbind(as.matrix(counts(dds,normalized=T)[rownames(res),]),as.matrix(res)),"mouse_results.txt",quote=F,sep="\t")Now we load in the length data from the mapping analysis separately to analyse. We will analyse the top 5 expressed, top 5 differential miRs and the miR-29 family, (Excluding miRs with norm counts sum < 50)
length_mouse=read.table("length_tables_mouse_mar_2017.txt",sep="\t",header=FALSE)
length_mouse$genotype=gsub("\\d+.lane.clean.uniquified.fa.gz","",gsub("mouse_","",length_mouse$V2))
mirlist=unique(c(rownames(counts(dds,normalized=T)[1:5,]),rownames(res[1:5,]),rownames(mircounts)[grep("mir-29[a-z]",rownames(mircounts))]))
for (i in 1:length(mirlist)){
mir=mirlist[i]
if (median(normcounts[mirlist[i],]) >= 50){
length_table=as.matrix(length_mouse[length_mouse$V1==mir,4:34])/apply(as.matrix(length_mouse[length_mouse$V1==mir,4:34]),1,max)
rownames(length_table)=length_mouse[length_mouse$V1==mir,"genotype"]
length_table=length_table[order(rownames(length_table)),]
colours = c("#E41A1C","#377EB8")[as.factor(rownames(length_table))]
names(colours)=as.factor(rownames(length_table))
heatmap.2(length_table,col=spectral,trace="none",Rowv=F,Colv=F,dendrogram="none",labCol=paste(c(0:30),"nt"),main=paste("Length Table\n",mir),RowSideColors=colours)
barplot(length_table,beside=T,col=colours,names=paste(c(0:30),"nt"),las=2)
matplot(t(length_table),type="b",col=colours,pch=19,cex=0.4,lty=1,lwd=0.4,main=paste("Length Analysis:",mir),xlab=paste(c(0:30),"nt"),las=2,xaxt="n")
axis(1, at = 1:31, labels = paste(c(0:30),"nt"), cex.axis = 0.7,las=2)
legend("topright",levels(as.factor(rownames(length_table))),fill=colours[levels(as.factor(rownames(length_table)))])
for (i in 16:18){
j=i+5
pvalue=NA
pvalue_full=NA
pvalue_full=t.test(length_table[1:3,i:j],length_table[4:6,i:j])
pvalue=pvalue_full$p.value
if (pvalue <= 0.05){
sig="*"
} else {
sig="ns"
}
print(paste(mir," trimming-test ",i,"-",j," P-value:",pvalue," >>",sig, " DF:",pvalue_full$parameter, " T:",pvalue_full$statistic, " Conf Int 95:", paste(pvalue_full$conf.int[1],pvalue_full$conf.int[2],sep=" to "),sep=""))
}
for (i in 21:23){
j=i+5
pvalue=NA
pvalue_full=NA
pvalue_full=t.test(length_table[1:3,i:j],length_table[4:6,i:j])
pvalue=pvalue_full$p.value
if (pvalue <= 0.05){
sig="*"
} else {
sig="ns"
}
print(paste(mir," tailing-test ",i,"-",j," P-value:",pvalue," >>",sig, " DF:",pvalue_full$parameter, " T:",pvalue_full$statistic, " Conf Int 95:", paste(pvalue_full$conf.int[1],pvalue_full$conf.int[2],sep=" to "),sep=""))
}
#Trimming and Tailing Combined
pvalue=NA
pvalue_full=NA
pvalue_full=t.test(as.numeric(rbind(apply(length_table[c(1:3),16:28],2,mean))),as.numeric(rbind(apply(length_table[c(4:6),16:28],2,mean))),paired=TRUE)
pvalue=pvalue_full$p.value
if (pvalue <= 0.05){
sig="*"
} else {
sig="ns"
}
print(paste(mir," combined-test (16-28nt) P-value:",pvalue," >>",sig, " DF:",pvalue_full$parameter, " T:",pvalue_full$statistic, " Conf Int 95:", paste(pvalue_full$conf.int[1],pvalue_full$conf.int[2],sep=" to "),sep=""))
}
}
## [1] "mmu-mir-9-2-5p trimming-test 16-21 P-value:0.979147074753133 >>ns DF:33.9863112682457 T:-0.026331327322368 Conf Int 95:-0.001103504236752 to 0.00107527471498382"
## [1] "mmu-mir-9-2-5p trimming-test 17-22 P-value:0.997543317648514 >>ns DF:33.9975400557372 T:-0.00310172131199731 Conf Int 95:-0.00576215229455686 to 0.00574459012970759"
## [1] "mmu-mir-9-2-5p trimming-test 18-23 P-value:0.868021928599759 >>ns DF:33.3321144249497 T:0.167457519251251 Conf Int 95:-0.0214987735156065 to 0.0253568397471551"
## [1] "mmu-mir-9-2-5p tailing-test 21-26 P-value:0.969498108549098 >>ns DF:33.999812871166 T:0.0385203440628656 Conf Int 95:-0.289183558914827 to 0.300358071496801"
## [1] "mmu-mir-9-2-5p tailing-test 22-27 P-value:0.969632325471596 >>ns DF:33.9978075392212 T:0.0383507745822705 Conf Int 95:-0.28452829355675 to 0.29547356495224"
## [1] "mmu-mir-9-2-5p tailing-test 23-28 P-value:0.963322451065909 >>ns DF:33.987105833169 T:0.046324870345685 Conf Int 95:-0.233229795015377 to 0.244110583239036"
## [1] "mmu-mir-9-2-5p combined-test (16-28nt) P-value:0.238135870741399 >>ns DF:12 T:1.24151409363319 Conf Int 95:-0.00189261856948422 to 0.00690641521201209"
## [1] "mmu-mir-148a-3p trimming-test 16-21 P-value:0.355061167786251 >>ns DF:30.8263337491432 T:0.938939406094083 Conf Int 95:-6.03328717112049e-05 to 0.00016323361798593"
## [1] "mmu-mir-148a-3p trimming-test 17-22 P-value:0.634011657381611 >>ns DF:33.9992429598472 T:0.480411445922994 Conf Int 95:-0.000163308078772884 to 0.00026442074074543"
## [1] "mmu-mir-148a-3p trimming-test 18-23 P-value:0.98387447784671 >>ns DF:33.5600429059776 T:0.0203628873193764 Conf Int 95:-0.000658449964027967 to 0.000671772221667914"
## [1] "mmu-mir-148a-3p tailing-test 21-26 P-value:0.9996680157295 >>ns DF:33.9999974911181 T:0.000419150840056292 Conf Int 95:-0.345187869385356 to 0.345330288894428"
## [1] "mmu-mir-148a-3p tailing-test 22-27 P-value:0.99971287174773 >>ns DF:33.9999972388832 T:0.000362517317625601 Conf Int 95:-0.325950072608663 to 0.326066381081413"
## [1] "mmu-mir-148a-3p tailing-test 23-28 P-value:0.99964078082836 >>ns DF:33.999995153129 T:0.000453536603351561 Conf Int 95:-0.257762694248638 to 0.257877769881689"
## [1] "mmu-mir-148a-3p combined-test (16-28nt) P-value:0.239968636507565 >>ns DF:12 T:1.23638543418157 Conf Int 95:-3.80280539200181e-05 to 0.000137807267007839"
## [1] "mmu-let-7f-2-5p trimming-test 16-21 P-value:0.943810792007168 >>ns DF:33.9974420911757 T:-0.0710037811618578 Conf Int 95:-0.012234162564613 to 0.0114081360043994"
## [1] "mmu-let-7f-2-5p trimming-test 17-22 P-value:0.913389013473437 >>ns DF:33.9996036074929 T:-0.109577068855395 Conf Int 95:-0.0138690535099352 to 0.0124499534059278"
## [1] "mmu-let-7f-2-5p trimming-test 18-23 P-value:0.927226996467204 >>ns DF:33.9837793900387 T:-0.09201425154328 Conf Int 95:-0.0309868477819178 to 0.0283024444479046"
## [1] "mmu-let-7f-2-5p tailing-test 21-26 P-value:0.995530815031397 >>ns DF:33.9997122943032 T:-0.00564265566914789 Conf Int 95:-0.321783447028004 to 0.320001491031192"
## [1] "mmu-let-7f-2-5p tailing-test 22-27 P-value:0.996302361169858 >>ns DF:33.9996056548419 T:-0.00466851727525919 Conf Int 95:-0.299037621925798 to 0.297666859708043"
## [1] "mmu-let-7f-2-5p tailing-test 23-28 P-value:0.997226453803235 >>ns DF:33.99924646274 T:-0.00350178282864773 Conf Int 95:-0.225138218749477 to 0.224363677728302"
## [1] "mmu-let-7f-2-5p combined-test (16-28nt) P-value:0.183212085778741 >>ns DF:12 T:-1.41248912129883 Conf Int 95:-0.00128709407665642 to 0.000274644326572715"
## [1] "mmu-let-7i-5p trimming-test 16-21 P-value:0.848743000061334 >>ns DF:33.7823651058229 T:-0.192191319045608 Conf Int 95:-0.000557712238783943 to 0.000461360425194253"
## [1] "mmu-let-7i-5p trimming-test 17-22 P-value:0.837499463693066 >>ns DF:33.8702563215943 T:-0.206677150605091 Conf Int 95:-0.00112614253757127 to 0.000918258424176481"
## [1] "mmu-let-7i-5p trimming-test 18-23 P-value:0.907473362525591 >>ns DF:33.8698272202658 T:-0.117098843104553 Conf Int 95:-0.0110775279980336 to 0.00987065513412996"
## [1] "mmu-let-7i-5p tailing-test 21-26 P-value:0.999631198394098 >>ns DF:33.999840334052 T:-0.000465635040593305 Conf Int 95:-0.333165212702236 to 0.333012575720575"
## [1] "mmu-let-7i-5p tailing-test 22-27 P-value:0.999811793155087 >>ns DF:33.9998092263479 T:-0.000237622880830643 Conf Int 95:-0.314373510878005 to 0.314300002410018"
## [1] "mmu-let-7i-5p tailing-test 23-28 P-value:0.999861400997893 >>ns DF:33.999565102234 T:0.000174989893530334 Conf Int 95:-0.245123861807625 to 0.245166079042673"
## [1] "mmu-let-7i-5p combined-test (16-28nt) P-value:0.913354484650761 >>ns DF:12 T:-0.111124698574519 Conf Int 95:-0.00078781932531294 to 0.000711357689153109"
## [1] "mmu-mir-21a-5p trimming-test 16-21 P-value:0.602235033654314 >>ns DF:32.6007835244115 T:0.526310597010793 Conf Int 95:-0.000400062723017628 to 0.000679103282610342"
## [1] "mmu-mir-21a-5p trimming-test 17-22 P-value:0.870092010270946 >>ns DF:33.9999911688782 T:0.164781187554127 Conf Int 95:-0.000926798016952538 to 0.00109035558136243"
## [1] "mmu-mir-21a-5p trimming-test 18-23 P-value:0.914599969177603 >>ns DF:33.9222619913425 T:-0.108040649847303 Conf Int 95:-0.00601029035451322 to 0.00540354538213444"
## [1] "mmu-mir-21a-5p tailing-test 21-26 P-value:0.978822137736616 >>ns DF:33.9924372712352 T:0.0267416950327245 Conf Int 95:-0.282372454857223 to 0.289902791203375"
## [1] "mmu-mir-21a-5p tailing-test 22-27 P-value:0.980685341727589 >>ns DF:33.996846253943 T:0.0243884647494285 Conf Int 95:-0.306491803394869 to 0.313937395634301"
## [1] "mmu-mir-21a-5p tailing-test 23-28 P-value:0.979022948296825 >>ns DF:33.9982197254784 T:0.0264880331764493 Conf Int 95:-0.285980949754223 to 0.293534267588676"
## [1] "mmu-mir-21a-5p combined-test (16-28nt) P-value:0.424414833426922 >>ns DF:12 T:0.826930670104607 Conf Int 95:-0.00291131452633063 to 0.00647294932580569"
## [1] "mmu-mir-708-5p trimming-test 16-21 P-value:0.112790349868752 >>ns DF:21.2148395297818 T:1.65428777489835 Conf Int 95:-0.00317284752405484 to 0.0279287545986177"
## [1] "mmu-mir-708-5p trimming-test 17-22 P-value:0.142530288939413 >>ns DF:22.9742896169251 T:1.51850190023461 Conf Int 95:-0.00590294613318734 to 0.0384811708485778"
## [1] "mmu-mir-708-5p trimming-test 18-23 P-value:0.18895193472291 >>ns DF:23.3549881431042 T:1.35318110424691 Conf Int 95:-0.0104022893837014 to 0.0498458408438004"
## [1] "mmu-mir-708-5p tailing-test 21-26 P-value:0.73621757694301 >>ns DF:33.9446482486479 T:0.339643561698369 Conf Int 95:-0.211421983915208 to 0.296265298350502"
## [1] "mmu-mir-708-5p tailing-test 22-27 P-value:0.779706751718029 >>ns DF:33.8477659621155 T:0.281939336930118 Conf Int 95:-0.233952882792304 to 0.309308694528717"
## [1] "mmu-mir-708-5p tailing-test 23-28 P-value:0.779285957154257 >>ns DF:33.6290644194064 T:0.28250749902864 Conf Int 95:-0.216075405878785 to 0.285816308958805"
## [1] "mmu-mir-708-5p combined-test (16-28nt) P-value:0.111724531847424 >>ns DF:12 T:1.71662019892478 Conf Int 95:-0.0063574584452888 to 0.0535816712739401"
## [1] "mmu-mir-219-2-3p trimming-test 16-21 P-value:0.590951833562155 >>ns DF:25.1604460803497 T:0.544410446903276 Conf Int 95:-0.00391118138395952 to 0.00672312052606395"
## [1] "mmu-mir-219-2-3p trimming-test 17-22 P-value:0.589921955379468 >>ns DF:30.3966234147123 T:0.544717904917365 Conf Int 95:-0.00777506410625704 to 0.013435462370549"
## [1] "mmu-mir-219-2-3p trimming-test 18-23 P-value:0.73213738638379 >>ns DF:33.1497198670127 T:0.345183215814676 Conf Int 95:-0.0171829309364391 to 0.0242063990664659"
## [1] "mmu-mir-219-2-3p tailing-test 21-26 P-value:0.992979193491957 >>ns DF:33.9964855915922 T:0.00886433191348056 Conf Int 95:-0.307341830501197 to 0.310034719961808"
## [1] "mmu-mir-219-2-3p tailing-test 22-27 P-value:0.996319142976593 >>ns DF:33.9976314472357 T:0.00464733094369069 Conf Int 95:-0.291702910231647 to 0.293040095436656"
## [1] "mmu-mir-219-2-3p tailing-test 23-28 P-value:0.994923980723995 >>ns DF:33.9993134363931 T:-0.00640883673202112 Conf Int 95:-0.230536995141774 to 0.229087535507142"
## [1] "mmu-mir-219-2-3p combined-test (16-28nt) P-value:0.405079027713917 >>ns DF:12 T:0.862955661616701 Conf Int 95:-0.00148175732450219 to 0.00342526746126815"
## [1] "mmu-mir-204-5p trimming-test 16-21 P-value:0.114840601885707 >>ns DF:17 T:1.66199720290651 Conf Int 95:-4.27307854635458e-05 to 0.000359906006020399"
## [1] "mmu-mir-204-5p trimming-test 17-22 P-value:0.680122738317857 >>ns DF:31.3185367301735 T:0.416171518058843 Conf Int 95:-0.00175192019812791 to 0.00265065500688693"
## [1] "mmu-mir-204-5p trimming-test 18-23 P-value:0.973133543206085 >>ns DF:33.9652077786137 T:0.0339275303533797 Conf Int 95:-0.0112962293925779 to 0.0116797906230057"
## [1] "mmu-mir-204-5p tailing-test 21-26 P-value:0.906876675718502 >>ns DF:33.933347959726 T:-0.117855938281525 Conf Int 95:-0.291537961019094 to 0.259579332906688"
## [1] "mmu-mir-204-5p tailing-test 22-27 P-value:0.887581842889889 >>ns DF:33.9576611473082 T:-0.142430339322638 Conf Int 95:-0.300356515745744 to 0.261014501483345"
## [1] "mmu-mir-204-5p tailing-test 23-28 P-value:0.863371773480411 >>ns DF:33.9861814882506 T:-0.173392192576792 Conf Int 95:-0.258119384570933 to 0.217536755157895"
## [1] "mmu-mir-204-5p combined-test (16-28nt) P-value:0.0423757630717704 >>* DF:12 T:-2.27081267688628 Conf Int 95:-0.0179446230954341 to -0.00037102056807968"
## [1] "mmu-mir-219-2-5p trimming-test 16-21 P-value:0.00552098381687736 >>* DF:17.7377210106036 T:3.15767108887249 Conf Int 95:0.0555487068139388 to 0.277123088454055"
## [1] "mmu-mir-219-2-5p trimming-test 17-22 P-value:0.000810430195119382 >>* DF:17.4455457227696 T:4.04083191015943 Conf Int 95:0.101137599643207 to 0.321245938598057"
## [1] "mmu-mir-219-2-5p trimming-test 18-23 P-value:0.000203140595087985 >>* DF:17.9056335168279 T:4.64678233881055 Conf Int 95:0.14149719552329 to 0.375193983484817"
## [1] "mmu-mir-219-2-5p tailing-test 21-26 P-value:0.111143947806822 >>ns DF:32.4769353415231 T:1.63767501323928 Conf Int 95:-0.0497610923255782 to 0.459182849839864"
## [1] "mmu-mir-219-2-5p tailing-test 22-27 P-value:0.200282143550049 >>ns DF:31.0333859188655 T:1.30858977528408 Conf Int 95:-0.0920850710264358 to 0.421849311764322"
## [1] "mmu-mir-219-2-5p tailing-test 23-28 P-value:0.291565749783691 >>ns DF:29.1914238182909 T:1.07412457670821 Conf Int 95:-0.107502822350207 to 0.345460013601414"
## [1] "mmu-mir-219-2-5p combined-test (16-28nt) P-value:0.00271035268070555 >>* DF:12 T:3.76213490344346 Conf Int 95:0.0641329016259005 to 0.24063974275524"
## [1] "mmu-mir-10b-5p trimming-test 16-21 P-value:0.708901030942516 >>ns DF:32.1117338083658 T:-0.376663784956955 Conf Int 95:-0.00225023392060858 to 0.00154781409450249"
## [1] "mmu-mir-10b-5p trimming-test 17-22 P-value:0.618280126358844 >>ns DF:32.6484246424169 T:-0.503087369650537 Conf Int 95:-0.00437273902734717 to 0.00263949037087023"
## [1] "mmu-mir-10b-5p trimming-test 18-23 P-value:0.819463761597534 >>ns DF:33.8602122110015 T:-0.230015397163365 Conf Int 95:-0.0158981044399343 to 0.012665664826793"
## [1] "mmu-mir-10b-5p tailing-test 21-26 P-value:0.994571765055719 >>ns DF:33.998914419628 T:-0.00685354168537097 Conf Int 95:-0.297625125589059 to 0.295624452975113"
## [1] "mmu-mir-10b-5p tailing-test 22-27 P-value:0.996278669940583 >>ns DF:33.9989576502025 T:-0.00469842992685134 Conf Int 95:-0.29422724357655 to 0.292869910975451"
## [1] "mmu-mir-10b-5p tailing-test 23-28 P-value:0.99887364306616 >>ns DF:33.9988323811619 T:-0.00142209666380634 Conf Int 95:-0.242046108473334 to 0.24170759425747"
## [1] "mmu-mir-10b-5p combined-test (16-28nt) P-value:0.459879174945897 >>ns DF:12 T:-0.763559570532024 Conf Int 95:-0.0018423522177498 to 0.000886153968977146"
## [1] "mmu-mir-29a-3p trimming-test 16-21 P-value:0.650310237769317 >>ns DF:32.7306381835289 T:0.457533713734189 Conf Int 95:-0.0050841071855242 to 0.00803304752167843"
## [1] "mmu-mir-29a-3p trimming-test 17-22 P-value:0.670701562715975 >>ns DF:33.4329996429245 T:0.428963168692497 Conf Int 95:-0.00773238642451645 to 0.0118667613788523"
## [1] "mmu-mir-29a-3p trimming-test 18-23 P-value:0.992853898798633 >>ns DF:33.936505901642 T:-0.00902264812940034 Conf Int 95:-0.100841635304586 to 0.0999502319790266"
## [1] "mmu-mir-29a-3p tailing-test 21-26 P-value:0.990598526082584 >>ns DF:33.9997739823665 T:-0.0118702341491527 Conf Int 95:-0.299495556028962 to 0.296017197954771"
## [1] "mmu-mir-29a-3p tailing-test 22-27 P-value:0.985439715142127 >>ns DF:33.9998897323767 T:-0.0183843333126032 Conf Int 95:-0.262118756443745 to 0.257418853745345"
## [1] "mmu-mir-29a-3p tailing-test 23-28 P-value:0.969210112766676 >>ns DF:33.9808976975408 T:-0.0388843964595903 Conf Int 95:-0.154789253079909 to 0.148977191475009"
## [1] "mmu-mir-29a-3p combined-test (16-28nt) P-value:0.766990285239726 >>ns DF:12 T:-0.303118406368457 Conf Int 95:-0.00317005054650869 to 0.0023957336308636"
## [1] "mmu-mir-29b-1-3p trimming-test 16-21 P-value:0.0471457605204787 >>* DF:19.9789129201383 T:2.11549256432463 Conf Int 95:0.000439892513159605 to 0.0628914290931691"
## [1] "mmu-mir-29b-1-3p trimming-test 17-22 P-value:0.0156237676775384 >>* DF:21.2082526547041 T:2.62853464988826 Conf Int 95:0.0100127656631076 to 0.0856638868702449"
## [1] "mmu-mir-29b-1-3p trimming-test 18-23 P-value:0.00921310616314494 >>* DF:23.8132367166199 T:2.83409507181799 Conf Int 95:0.0194446594577204 to 0.123815910228331"
## [1] "mmu-mir-29b-1-3p tailing-test 21-26 P-value:0.581612254402018 >>ns DF:33.5217471049371 T:0.556430728134715 Conf Int 95:-0.199872756556187 to 0.350481027390253"
## [1] "mmu-mir-29b-1-3p tailing-test 22-27 P-value:0.639487059671462 >>ns DF:33.1745132537695 T:0.472750134908387 Conf Int 95:-0.211773554243833 to 0.340015679223932"
## [1] "mmu-mir-29b-1-3p tailing-test 23-28 P-value:0.690307198013953 >>ns DF:32.7166103244717 T:0.40198778403265 Conf Int 95:-0.194804894540366 to 0.290701688593442"
## [1] "mmu-mir-29b-1-3p combined-test (16-28nt) P-value:0.017744621104104 >>* DF:12 T:2.74565203368548 Conf Int 95:0.0091269904877137 to 0.0792915233214068"
## [1] "mmu-mir-29c-5p trimming-test 16-21 P-value:0.28840716266688 >>ns DF:33.5995716017716 T:1.07869463620812 Conf Int 95:-0.00103740672126122 to 0.00338232584516633"
## [1] "mmu-mir-29c-5p trimming-test 17-22 P-value:0.591150726092031 >>ns DF:26.2484199405879 T:-0.543826289857812 Conf Int 95:-0.0202823532884344 to 0.0117924846338411"
## [1] "mmu-mir-29c-5p trimming-test 18-23 P-value:0.612251250780343 >>ns DF:31.817263845374 T:-0.511906763143119 Conf Int 95:-0.0530622048350088 to 0.0317521103016977"
## [1] "mmu-mir-29c-5p tailing-test 21-26 P-value:0.80054161096952 >>ns DF:33.9916655874462 T:-0.254631944773153 Conf Int 95:-0.276679912739776 to 0.215066633449607"
## [1] "mmu-mir-29c-5p tailing-test 22-27 P-value:0.798583315455231 >>ns DF:33.9869670722594 T:-0.257189379732094 Conf Int 95:-0.28975743396186 to 0.224656973356327"
## [1] "mmu-mir-29c-5p tailing-test 23-28 P-value:0.810037538082939 >>ns DF:33.9515995026603 T:-0.242258216177191 Conf Int 95:-0.259089156929587 to 0.203900362059837"
## [1] "mmu-mir-29c-5p combined-test (16-28nt) P-value:0.0214207672514714 >>* DF:12 T:-2.64385446177496 Conf Int 95:-0.0268053572349086 to -0.00258479516094193"
## [1] "mmu-mir-29c-3p trimming-test 16-21 P-value:0.587403722745256 >>ns DF:32.6382894077793 T:0.548024898987411 Conf Int 95:-0.0146911873461574 to 0.0255173561600422"
## [1] "mmu-mir-29c-3p trimming-test 17-22 P-value:0.76789158483482 >>ns DF:33.9932498423994 T:0.297505033706475 Conf Int 95:-0.0238535229155881 to 0.0320351345420817"
## [1] "mmu-mir-29c-3p trimming-test 18-23 P-value:0.879449930673378 >>ns DF:33.1432894921226 T:-0.152841588839458 Conf Int 95:-0.14874825935032 to 0.127957510947464"
## [1] "mmu-mir-29c-3p tailing-test 21-26 P-value:0.938130338956058 >>ns DF:33.9999703347325 T:-0.0781962551604969 Conf Int 95:-0.29122398418298 to 0.269643066438616"
## [1] "mmu-mir-29c-3p tailing-test 22-27 P-value:0.901939008763118 >>ns DF:33.9643812337362 T:-0.124136449674233 Conf Int 95:-0.246333078783958 to 0.217972783263099"
## [1] "mmu-mir-29c-3p tailing-test 23-28 P-value:0.808126889757535 >>ns DF:32.8450105842172 T:-0.244809617223079 Conf Int 95:-0.122511800836618 to 0.0961993889536541"
## [1] "mmu-mir-29c-3p combined-test (16-28nt) P-value:0.543666026527142 >>ns DF:12 T:-0.625011070708 Conf Int 95:-0.0187697245479682 to 0.010401662891136"
## [1] "mmu-mir-29b-2-5p trimming-test 16-21 P-value:0.739770903847523 >>ns DF:33.6088423229711 T:0.334914579067431 Conf Int 95:-0.0069217047304003 to 0.00965186346055899"
## [1] "mmu-mir-29b-2-5p trimming-test 17-22 P-value:0.815340029582888 >>ns DF:26.7262189259627 T:-0.23585920823868 Conf Int 95:-0.0166074911546283 to 0.0131845219669532"
## [1] "mmu-mir-29b-2-5p trimming-test 18-23 P-value:0.569940438324162 >>ns DF:25.7471855473381 T:-0.575513248514891 Conf Int 95:-0.0245184241779927 to 0.0137961282420745"
## [1] "mmu-mir-29b-2-5p tailing-test 21-26 P-value:0.904059325822727 >>ns DF:33.4208696686984 T:0.121453292406892 Conf Int 95:-0.193267005024468 to 0.21781911773121"
## [1] "mmu-mir-29b-2-5p tailing-test 22-27 P-value:0.874951010107516 >>ns DF:33.6620984902155 T:0.158575324035112 Conf Int 95:-0.242674414020189 to 0.283734748665069"
## [1] "mmu-mir-29b-2-5p tailing-test 23-28 P-value:0.862548008566446 >>ns DF:33.8701455511156 T:0.174453091911684 Conf Int 95:-0.250357624738798 to 0.297369269299617"
## [1] "mmu-mir-29b-2-5p combined-test (16-28nt) P-value:0.327837575732214 >>ns DF:12 T:1.02003308462982 Conf Int 95:-0.0114271574346663 to 0.0315450076068869"