Hi Steve,

Don't take this the wrong way, but it sounds like you are quite new to not
just this analysis, but to R as a whole since indexing things (vectors,
lists, matrices) is something very basic that you need to master before
being conversant with the language.

Indeed, I have limited knowledge in using R and edgeR. Thanks for the
suggestion to contacting R-help for these questions. Unfortunately my
graduate program offers very little help with R statistics and even fewer
with bioinformatics especially that of small RNAs. With these limited
resources, I feel like I'm working in the dark and my analysis, to say the
least, is cryptic. I'll take a step back before I jump the gun with the
analysis. Thanks for the insight.

Best,
Eleanor




On Fri, Mar 21, 2014 at 11:19 AM, Steve Lianoglou
<lianoglou.steve@gene.com>wrote:

> Hi Eleanor,
>
> Please CC (use "reply-all") the bioconductor mailing list on all
> correspondences so that everyone can help (and benefit) from this
> discussion.
>
> Comments in line:
>
>
> On 21 Mar 2014, at 11:02, Eleanor Su wrote:
>
>  Can you explain what you mean with that a bit more. You shouldn't be
>> doing any normalization of your actual counts prior to feeding them to
>> edgeR, are you?
>>
>> I'm only working with small non-coding RNAs of a non-model organism. Since
>> this is a fairly new kind of analysis, I'm following someone else's
>> pipeline. Thus I've normalized my samples prior doing analysis in R. I've
>> normalize all my counts based on the reads generated.
>>
>
> What I mean is that you shouldn't do that :-)
>
> Have you read through the edgeR User's Guide? The `calcNormFactors` does
> the step that it sounds like you are doing before analysis -- but it also
> keeps the count data "in tact" which is what you want. I guess you are
> dividing your counts by some normalization constant prior to edgeR
> analysis, which is a big no-no.
>
> The (expression) input to edgeR should be the raw count matrix of features
> x samples -- many people choose to use only uniquely mapping reads for this
> purpose, so probably a good idea for you to ensure that is the case (at
> least for your first analysis).
>
>
>  Look at section 2.10 of the edgeR User's Guide (Clustering, heatmaps,
>>> etc.) where the authors identify this to still be a matter of
>>> research, but they suggest to use "moderated log-counts-per-million"
>>>
>>
>> I've generated a heatmap already using this script, but I only want a
>> heatmap of the significant differentially expressed sequences.
>>
>
> What script?
>
>
>  When I
>> generate the heatmap accordingly to the section 2.10, I end up with a
>> heatmap that I can't even read because it's plotting all the sequences.
>> Would you suggest just generating a new file with only significant
>> sequences and then generating a heatmap accordingly to section 2.10?
>>
>
> When you call the `heatmap` function (or whatever function you are using
> to generate these things (the aheatmap function from the NMF package is
> quite nice, btw)), you should only pass it a matrix that consists of the
> rows you want to plot.
>
> You do not have to generate an intermediary new file to do this.
>
> Don't take this the wrong way, but it sounds like you are quite new to not
> just this analysis, but to R as a whole since indexing things (vectors,
> lists, matrices) is something very basic that you need to master before
> being conversant with the language.
>
> If this is the case, I'd strongly recommend you spend some time reading up
> on introductory R stuff (R comes with "an introduction to R") for some time
> before trying to do something any more advanced.
>
> Ensuring that you do so will not only mitigate the chances of you shooting
> yourself in the foot by doing something silly, but it will also allow you
> to get better (and more considered) help here since you will be able to ask
> the type of questions that will leverage the expertise from the people
> subscribed to this list.
>
> For instance, if you have questions regarding fundamental "R programming"
> type of things (indexing a matrix, for example), you should direct those to
> R-help, which you can subscribe to here:
>
> https://stat.ethz.ch/mailman/listinfo/r-help
>
>
> HTH,
> -steve
>
> --
> Steve Lianoglou
> Computational Biologist
> Genentech
>

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