OK, if updating DGEList$samples$lib.size is the way of using original
library sizes, than I know how to do it, but still I'm not sure if this is
the right way to go with this kind of IP-Input normalization
Mali

On Sun, Jan 8, 2012 at 5:43 PM, mali salmon <shalmom1@gmail.com> wrote:

> Dear List
> I have peak counts from RNA-IP samples and corresponding inputs, for two
> different conditions.
> I would like to find DE-binding between the two IP conditions after
> removing the differential expression effect.
> In a previous post (titled "differential binding question") Mark Robinson
> suggested to do GLM analysis.
> Before doing the DE analysis I have to normalize the data.
>
> Using DESeq "estimateSizeFactors" function I get the following sizeFactors
>
> > sizeFactors( cds )
>      cond1_IP    cond1_IP.1   cond1_Input cond1_Input.1         cond2_IP
>     6.3672619     6.1015548     0.3209480     0.2553967     3.2300114
>     cond2_IP.1       cond2_IP.2      cond2_Input    cond2_Input.1
>     1.7808445     1.7027369     0.2480639     0.2530747
>
> With edgeR, these are the normalize factors I get using both TMM and RLE
> methods
> > dTMM$samples
>               group lib.size norm.factors
> cond1_IP     H  8345160    0.9916792
> cond1_IP.1    H  9395446    1.2221615
> cond1_Input   H  1126656    0.4489350
> cond1_Input.1 H   219823    2.1955057
> cond2_IP            S  5707895    0.8339317
> cond2_IP.1          S  5914904    0.5014391
> cond2_IP.2          S  5602070    0.5043970
> cond2_Input         S   223442    1.9909578
> cond2_Input.1       S   226840    1.9934207
>
> >dRLE$samples
>               group lib.size norm.factors
> cond1_IP      H  8345160    1.2656111
> cond1_IP.1    H  9395446    1.0772223
> cond1_Input   H  1126656    0.4725259
> cond1_Input.1 H   219823    1.9271892
> cond2_IP            S  5707895    0.9386643
> cond2_IP.1          S  5914904    0.4994138
> cond2_IP.2          S  5602070    0.5041749
> cond2_Input         S   223442    1.8415393
> cond2_Input.1       S   226840    1.8505947
>
>
> The "real" library size (number of reads that have been successfully
> aligned in each sample) are
> cond1_IP       24055908
> cond1_IP    16654296
> cond1_lnput   12919153
> cond1_Input    33778948
> cond2_IP    17340233
> cond2_IP    29284664
> cond2_IP    27788144
> cond2_Input 33477921
> cond2_Input 33980303
>
> As you can see, DESeq and edgeR are weighting-up Input samples and
> weighting-down IP. I suppose this is due to the fact that many less Input
> reads are found in peak regions compared to IP which makes DESeq and edgeR
> to think that the Input library size is much lower than IP. In fact, the
> original library size of Input samples is in most cases  larger than the IP.
>
> What do you think, shall I use the original library sizes as normalization
> factors instead of the calculated ones? I know this is possible with DESeq,
> but I couldn't find how to do it with edgeR.
>
> Thanks
> Mali
>
>

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