User: flippy23

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flippy230
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Posts by flippy23

<prev • 12 results • page 1 of 2 • next >
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Comment: C: paired sample - SVA model matrix
... I have a follow-up question based on this. In downstream analysis, subject ID (two samples - pre/post) is the random effect group. Because I'm controlling for patient specific effects in downstream analysis, I guess the within-individual technical variability is now of interest - as that may be cor ...
written 5 months ago by flippy230
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random effects + interaction term + limma
... Hi, I realize that I made a similar post, but didn't realize I was editing that, and didn't know how to go back, so this is a similar post, but different question about my interpretation, rather than the code I am following section 9.5.2 - 9.5.4 of the limma user guide. I understand that each of t ...
limma rna-seq random effects interaction written 5 months ago by flippy230
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Comment: C: limma 9.5.2 and 9.5.3
... Thanks. I'm having a bit of trouble finding information on constructing the contrast vector. I was wondering if you could point me in the correct direction as to where this information would be available? Thanks ...
written 5 months ago by flippy230
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Comment: C: normalization for multi-level experiment
... Thank you for the feedback. I guess what I'm worried about is that the slope for each random effect / subject may actually be attributed to differences in the time to which the RNA-seq was collected (before/after treatment). Would introducing TPM normalization to control for gene length/sequencing d ...
written 5 months ago by flippy230
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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 6 months 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 6 months 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 6 months 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 6 months 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 6 months 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 6 months ago by flippy230 • updated 6 months ago by Michael Love25k

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