[BioC] MLSeq Mathematical Concepts
mtmorgan at fhcrc.org
Wed Apr 23 18:26:14 CEST 2014
I think it would be great to capture these ideas and brief implementation as a
'Sequence Analysis Machine Learning' workflow
that re-uses some pre-existing but real data like
or other new / existing Experiment Data packages
The basic requirement for a work flow is an R markdown (Rmd or Rnw) document;
Sonali (cc'd) has agreed to help with the technical aspects needed to 'make this
Any takers (on or off-list)?
On 04/23/2014 03:27 AM, Bernd Klaus wrote:
> Dear Dario,
> I think you are right about being careful to simply use the voom weights
> to pre-transform the data. As Dr. Smyth pointed out a while ago, an algorithm
> should always use these weights explicitly in some way rather than using them to
> pretransform the data.
> You could possibly incorporate them easily in a DDA classifier for example.
> Apart from Wolfgangs links, I might point you to two interesting papers
> Zwiener et. al. - Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures
>  http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0085150
> They investigate a couple of pretransformations for RNA-Seq data classification and find
> that rank based transformation perform well in general. (They do not consider voom weights)
>  Gallopin et.al. - A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data
> They use a GLMM combined with a lasso penalty to incorporate unequal sample variances and then
> estimate a graphical model using a type of partial correlation.
> This is somewhat similar to the voom approach, however the variances and
> the model parameters are estimated in "one-go". However, they note that the
> algorithm used is very slow.
> Best wishes,
> On Apr 23, 2014, at 9:43 AM, Wolfgang Huber <whuber at embl.de> wrote:
>> Dear Dario
>> good points, and as usual in machine learning, I don’t expect there to be a simple answer or universally best solution.
>> For classification, the (pre)selection of features (genes) used is probably more important than most other choices, esp. if the classification task is simple and can be driven by a few genes. For clustering, similar, plus the choice of distance metric or embedding.
>> That said, it is plausible that both, using the untransformed counts (or RPKMs etc.), or the log-transformed values, have problems with high variance (either at the upper or lower end of the dynamic range) that can be avoided with a different transformation, log-like for high values, linear-like for low (e.g. DESeq2’s vst, rlog). Paul McMurdie and Susan Holmes have some on this in their waste-not-want-not paper , and Mike in a Supplement to the DESeq2 paper (draft). It would be interesting to collect more examples, and someone should probably study this more systematically (if they aren’t already.)
>> Kind regards
>>  http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003531
>> [2 http://www-huber.embl.de/DESeq2paper —> Regularized logarithm for sample clustering (As of today, there is a version of 19 February which I think will soon be updated with a more extensive survey).
>> Il giorno 23 Apr 2014, alle ore 07:00, Dario Strbenac <dstr7320 at uni.sydney.edu.au> ha scritto:
>>>> From reading the vignette, MLSeq seems to be a set of wrapper functions that allows the user easy access to normalisation strategies in edgeR or DEseq and passes the data onto algorithms such as Support Vector Machine or Random Forest. Are there any results that demonstrate that normalisation improves classification performance ? I am also not convinced about the description of using voom weights to transform the data. The author of voom stated that specialised clustering and classification algorithms are needed to handle the CPM and weights separately. Why does MLSeq use standard classification algorithms and how were the weights and expression values combined ?
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