User: flippy23

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flippy230
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Posts by flippy23

<prev • 9 results • page 1 of 1 • next >
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Comment: C: Computationally Singular Matrices - DESeq
... thank you - it does seem that the first couple of SVs were highly correlated with cell type proportions. ...
written 27 days ago by flippy230
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svaseq - over adjustment
... Hi, I had a question about over adjustment with SVA. I used the svaseq() on my dataset and ended up with 11 SV's. I had problems with DE analysis because my matrix was "computationally singular". It seems that the first couple of SV's in our dataset are highly correlated > 0.7 with cell type p ...
sva svaseq written 27 days ago by flippy230
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Comment: C: Computationally Singular Matrices - DESeq
... So there is no concern with the output of this correlation? Were the 11 SV's just extremely correlated with the variation across the samples? Was it overcorrecting? ...
written 27 days ago by flippy230
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Comment: C: Computationally Singular Matrices - DESeq
... (Intercept) study_id121 study_id123 study_id124 study_id125 study_id126 study_id127 study_id128 study_id130 study_id131 (Intercept) 1 NA NA NA NA NA NA NA NA NA study_id121 NA 1. ...
written 27 days ago by flippy230
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Comment: C: Computationally Singular Matrices - DESeq
... I'm not getting a single output. it's a matrix of the pairwise study id's and their respective correlations. because i'm looking at before/after treatment within the same sample, these pairwise correlations are 1. Not all of them, just the ones within the same study id ...
written 27 days ago by flippy230
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Computationally Singular Matrices - DESeq
... Hello, I had a question about computationally singular matrices in DESeq and surrogate variables. After including all the SVs into my formula where I am analyzing paired samples (before/after treatment within a sample), I use the DESeq function and am returned with the error that my matrix is compu ...
rnaseq sva deseq2 written 29 days ago by flippy230 • updated 28 days ago by Michael Love23k
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Comment: C: paired sample - SVA model matrix
... Got it. I just want to ensure that I'm accounting for blocking correctly in the full model. I wouldn't include study_id in the null model because differences between the patients isn't something I want to account. Likewise, wouldn't include treatment effect in the null model because that's what I'm ...
written 4 months ago by flippy230
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Comment: C: paired sample - SVA model matrix
... So...just to make sure I understand. null model (only adjustment covariates) would consist of: covariate 1 + covariate 2 + covariate 3  full model: ~Study_ID+Treatment + covariate 1 + covariate 2 + covariate 3    how will it differentiate the treatment effect from the other covariates?    ...
written 4 months ago by flippy230
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paired sample - SVA model matrix
... Hello,   I am running SVA and had a question regarding the null and full model matrices. I am comparing a before/after effect of a treatment within each patient. How do I account for this in building the model matrices? How do I specify to SVA that the before/after variable is within the same pati ...
limma sva paired sample written 4 months ago by flippy230 • updated 4 months ago by James W. MacDonald49k

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