[BioC] finding differential expression pattern between paired columns of data frame.

[Jakub Stanislaw Nowak]

Hello Bioconductor list members,

I am trying to analyse output of my smallRNA sequencing data. My starting point is a table with differentially expressed miRs between two stages of different cell types.
My goal is to identify miRs that are changing in LineA - Line D and not changing in LineB - LineC and reciprocal situation.

Below is a head of my data.frame that I work on.

> data.selected.list
                ID      		Line A	      Line B 	    Line C     Line D
1   mmu-miR-23a-5p -2.28461513 -2.75356783 -2.0132004  0.1397216
2    mmu-miR-22-3p  1.65924854  0.03717303  0.4909068  2.4100528
3     mmu-miR-5113 -0.91337275 -0.87022608 -1.3224488  1.3906263
4   mmu-miR-324-3p  1.44133874 -0.07767235 -0.2187064 -0.2201993
5 mmu-miR-365-1-5p -0.03644659  0.72755490  1.0445618  1.3071159
6  mmu-miR-3068-5p  0.49474466 -0.28506641 -0.2995997  1.3754639

As this very much looked for me as microarray experiment I originally was thinking to apply some tools for microarray analysis to get this done.

So i did as below
1. Constructed matrix
> design <- model.matrix(~-1+factor(c(1,2,3,4)))
> colnames(design) <- c("LineA","LineB","LineC","LineD")
> design
  LineA LineB LineC LineD
1     1     0     0     0
2     0     1     0     0
3     0     0     1     0
4     0     0     0     1
attr(,"assign")
[1] 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$`factor(c(1, 2, 3, 4))`
[1] “contr.treatment”

2. Constructed comparison matrix
> contrast.matrix <- makeContrasts(LineA-LineD, LineB-LineC, levels=design)
> contrast.matrix
       Contrasts
Levels  LineA - LineD LineB - LineC
  LineA             1             0
  LineB             0             1
  LineC             0            -1
  LineD            -1             0

3. Perform fitting and of course got benched for my naive thinking that this is going to work

> fit <- lmFit(data.selected[,(2:5)],design)
> fit2 <- contrasts.fit(fit, contrast.matrix)
> fit2 <- eBayes(fit2)
Error in ebayes(fit = fit, proportion = proportion, stdev.coef.lim = stdev.coef.lim,  : 
  No residual degrees of freedom in linear model fits

I thought that maybe there is something wrong with the input for my fitting and I checked that limma is using normalised log data so I followed my very naive thinking and try to normalise the table

> eset<-rma(data.selected.list[,2:5])
Error in (function (classes, fdef, mtable)  : 
  unable to find an inherited method for function ‘probeNames’ for signature ‘"data.frame”’

As it hasn’t work I have two questions:
1. Can I somehow edit my data.frame that it will become a better fit for limma package and perform comparison I want.
2. I won’t be surprised if you say that this approach is not valid therefore I will welcome any suggestion about other models/methods I can use to make this comparison for my initial table.

Thank you very much for help,

Jakub


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