User: agustin.gonvi

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Cleveland, OH
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@AgustinGonVi
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Posts by agustin.gonvi

<prev • 13 results • page 1 of 2 • next >
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Comment: C: Limma residuals matrix
... The values -0.07 and 0.06 are the residuals for Test and Reference respectively. As I work with the residuals the average expression of any gene across all samples is zero. I al puzzled on how to calculate logFC in that case. ...
written 19 days ago by agustin.gonvi10
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Comment: C: Limma residuals matrix
... I have another question about subtracting the intercept. How does limma calculate the logFC for an observation were the mean value of Test is -0.072431315 and Reference is 0.06725765?. I cannot do log2(T/R) in this case. Thanks ...
written 23 days ago by agustin.gonvi10
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Comment: C: Limma residuals matrix
... Thanks! I did find the sex-DEG. And also need the residuals. I wasn't sure whether using removeBatchEffect to regress sex out, would use the same linear model as lmFit. I was wondering if there is an equivalent of resid() applied on the lm() output. ...
written 4 months ago by agustin.gonvi10
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Limma residuals matrix
... I am looking for sexual dimorphism in gene expression so I fit a model design <- model.matrix(~Sex, data = pData(eset)) fit <- lmFit(eset, design) I need to obtain a matrix with the residuals of the linear model. I look at the object *fit* and I cannot interpret where the residuals ...
limma written 4 months ago by agustin.gonvi10 • updated 4 months ago by Gordon Smyth39k
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tximport from Galaxy output
... I am using Galaxy, and the output of both Salmon and Kallisto is an *.tabular file per sample with the following format: > Name Length EffectiveLength TPM NumReads > ENSMUST00000193812.1|ENSMUSG00000102693.1|OTTMUSG00000049935.1|OTTMUST00000127109.1|4933401J01Rik-201|4933401J01Rik|1070|TEC| 1 ...
normalization tximport written 5 months ago by agustin.gonvi10 • updated 5 months ago by Michael Love26k
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Comment: C: How should I compare two different RNA-seq datasets using DESeq2 or EgdeR?
... Would it work if you calculate the adjustment coefficients using the controls only and then apply the adjustment to all samples? In that way controls from both databases will be centered and the large biological effect will not affect the adjustment. ...
written 6 months ago by agustin.gonvi10
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Comment: C: WGCNA with paired samples
... I’ve read in other posts that a strong driver of gene expression could reduce the number of modules. Trying to put that together with what you just said, I am thinking that in the case of low numbers of modules due to a strong biological relevant driver the modules will still be there, but the algo ...
written 6 months ago by agustin.gonvi10
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Comment: C: WGCNA with paired samples
... > I don't see how approach 1 would be useful in answering your question except if you had a hypothesis that the network organization is different pre- and post-training. Is this a crazy idea? I would think that certain genes with low basal expression values and low module membership overall, can ...
written 6 months ago by agustin.gonvi10
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Comment: C: Steep dendrograms and few modules. WGCNA on RNA seq data
... Thanks! I've noticed that 3 RNA seq databases I am working with reach a fitting index of 0.8 between 4 and 6 stp. With micro-arrays, I was using numbers stp 9 and 11. That could be the problem. Is there a mean connectivity I should be shooting for? I was working under the assumption that the largest ...
written 6 months ago by agustin.gonvi10
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Steep dendrograms and few modules. WGCNA on RNA seq data
... Hi, I am trying to run WGCNA on RNA seq data and I end up with few modules. I've seen a [similar post][1] but unlike those data, mine present a better scale free topology index. I run 3 different databases and always have similar results, I am wondering if I am missing something. [Fitting Index][ ...
wgcna rna seq written 6 months ago by agustin.gonvi10 • updated 6 months ago by Peter Langfelder2.3k

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Supporter 8 months ago, voted at least 25 times.

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