FW: Tigre Package question 3
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@solanki-anisha-6383
Last seen 7.0 years ago
>Dear Antti, > >Thanks for your reply. The information you have given me has been very >useful. I had another quick question regarding the mmgmos command. I >understand that the command accepts data as an AffyObject. However, I have >data from RNA-Seq and not from affymetrix microarrays. Hence I cannot >create an Affyobject from my data as the object requires CEL files to >convert the data into an AffyObject. Is there any other alternative other >than using an Affyobject. I have tried to run a matrix with the expression >values of every sample from my data. However the command mmgmos doesn't >seem to accept this as a valid object. > >Please advise. > >Thanks > >Anisha > > >On 10/02/2014 09:38, "Antti Honkela" <antti.honkela at="" hiit.fi=""> wrote: > >>On 2014-02-09 18:49 , Solanki, Anisha wrote: >> >>Dear Anisha, >> >>> I have now solved the previous error by adding variances independently >>>to >>> the expression Dataset. >> >>The error variances are critical to the accuracy of the method, so you >>should never just impute any values there without careful consideration. >>More about how you could fix this better below. >> >>> I just had another quick question. The targets are >>> ranked by the log-likelihood. Does this mean that the higher the >>> log-likelihood the greater the probability of the gene being a target >>>or >>> vice versa? Also what does null log likelihood stand for? >> >>Our method is based on comparing log-likelihoods over different data >>sets (time series for different genes), which is slightly trickier than >>usual comparison of log-likelihoods over the same data. >> >>The log-likelihood measures how well the data fit a model assuming >>regulation, therefore higher log-likelihood should be counted as >>evidence for being a target. >> >>That said, some time series are easy to fit, and get a high likelihood >>over practically any model. To catch these, we fit the baseline or null >>model (which is just a time-independent Gaussian). We can then filter >>out genes that fit the null model equally well or better than the true >>model. >> >>Finally, even though one might consider the likelihood ratio of real vs. >>null a useful statistic, it is actually not good for ranking. This is >>because the range of null model likelihoods is much larger, and >>therefore the ranking will be determined by how badly the null model >>fits instead of how well the real model fits, and tell nothing about the >>regulation. >> >>In summary, you should: >>1. *Filter* by likelihood ratio real/null: only keep genes where >> log-likelihood > null-log-likelihood >>2. *Rank* remaining genes by log-likelihood >> >>>> I think this means that my Data lacks calculated variances. As I >>>> understand from your User guide you process affymetrix Datasets using >>>>the >>>> mmgmos command from the PUMA package which automatically calculates >>>>the >>>> variances for you. However, when I try to run my expression value >>>>matrix >>>> through this mmgmos command it doesn't work and gives me this error >>>> "unable to find an inherited method for function ?probeNames? for >>>> signature ?"ExpressionTimeSeries"? >> >>You should run mmgmos on the original AffyBatch object, not on an >>ExpressionTimeSeries object. >> >> >>Hope this helps, >> >>Antti >> >>-- >>Antti Honkela >>antti.honkela at hiit.fi - http://www.hiit.fi/u/ahonkela/ >> >
impute PROcess mmgmos puma impute PROcess mmgmos puma • 598 views
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