User: Paul Harrison

gravatar for Paul Harrison
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Australia/Melbourne/Monash University Bioinformatics Platform
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http://logarithmic.net...
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@paulfharrison
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6 years, 8 months ago
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Posts by Paul Harrison

<prev • 13 results • page 1 of 2 • next >
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Comment: C: How to best represent weights in SummarizedExperiment or SingleCellExperiment?
... Ok, it's important not to guess wrong. I'll require the user to do something explicitly marking the assays appropriate to use. I think my solution will be to add some metadata fields to a SummarizedExperiment defining the appropriate pair of assays to use. I'll then write my code to produce an erro ...
written 7 months ago by Paul Harrison70
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Comment: C: How to best represent weights in SummarizedExperiment or SingleCellExperiment?
... Thanks Davide and Aaron. I had not thought about the distinction between frequencies and precision weights. So if I'm understanding correctly, `zinbwave` weights can be thought of as (fractional) frequencies, and contribute to the degrees of freedom as such. Also importantly, they are for `"counts"` ...
written 9 months ago by Paul Harrison70
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Comment: C: How to best represent weights in SummarizedExperiment or SingleCellExperiment?
... It looks like `SingleCellExperiment` has a `weights()` method, which is hopeful but I'm not clear on which assay the weights are meant to be associated with with. ...
written 9 months ago by Paul Harrison70
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How to best represent weights in SummarizedExperiment or SingleCellExperiment?
... I am trying to work out the best way to represent weights in the newer `SummarizedExperiment` and `SingleCellExperiment` classes. By weights I mean inverse variance (up to some scaling factor) for example as accepted by the `lm()` function. Weights are a somewhat simplistic but general way of acco ...
summarizedexperiment singlecellexperiment written 9 months ago by Paul Harrison70 • updated 9 months ago by davide risso830
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Answer: A: Bootstrap confidence intervals on RNA-seq expression from Kallisto
... Since RNA-seq is count based, in an RNA-seq analysis at the gene level the noise in individual counts for genes can be assumed to be Poisson (variance equal to the mean). This is an estimate of "technical variation", but does not include "biological varation". This would be the equivalent of the Kal ...
written 2.1 years ago by Paul Harrison70
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Comment: C: Limma-voom vs limma-trend
... If you examine the documentation to `contrasts.fit`, there is a warning that using weights with a non-orthogonal design matrix uses an approximation. Since voom uses weights on inidividual observations, and limma-trend does not, this could be an advantage to using limma-trend. ...
written 2.3 years ago by Paul Harrison70
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In limma's barcode plot, why are the cutoffs for the pale red and blue bands +/-sqrt(2)?
... limma's barcodeplot function by default draws a pale red rectangle for statistics greater than sqrt(2) and a pale blue rectangle for statistics less than -sqrt(2). What is the reasoning is behind this choice? Is the expectation that statistics follows a standard normal distribution for genes that a ...
limma barcodeplot written 3.1 years ago by Paul Harrison70 • updated 3.1 years ago by Gordon Smyth38k
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Comment: C: Behaviour of weights in limma
... On Thu, Jun 5, 2014 at 10:04 AM, Gordon K Smyth wrote: > Dear Paul, > >> Date: Wed, 4 Jun 2014 17:30:59 +1000 >> From: Paul Harrison >> To: Bioconductor mailing list >> Subject: [BioC] Behaviour of weights in limma >> >> Hello, >> >> I have so ...
written 5.4 years ago by Paul Harrison70
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Behaviour of weights in limma
... Hello, I have some data from a variant of RNA-seq which I am hoping do some moderated t-test differential testing on with limma. In this data, many of the reads have sequenced through into the poly(A) tail, and we believe this gives us information about changes in poly(A) tail length. For each gen ...
limma written 5.4 years ago by Paul Harrison70 • updated 5.4 years ago by Gordon Smyth38k
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Comment: C: Log transformation and left censoring
... On Mon, Feb 4, 2013 at 7:56 PM, Wolfgang Huber wrote: > > Hi Paul > > the model (in the "parametric" case) is > > var(X) = a * E(X) + b * E(X)^2 > > with some a,b>0. And you are right that var(X)=0 <=> E(X)=0 > but since the support of X is positive, the onl ...
written 6.7 years ago by Paul Harrison70

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