User: swbarnes2

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swbarnes2340
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Posts by swbarnes2

<prev • 104 results • page 1 of 11 • next >
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Answer: A: Understanding interactions in DESeq2
... > I can't for the life of me understand how I'm getting such radically > different results simply from the addition of a single interaction > term. From the tutorial: http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#interactions > The key point to rem ...
written 3 days ago by swbarnes2340
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Comment: C: featureCount error - cannot identify gene_id
... Those two genes overlap a teeny tiny bit, and run in the same direction, which I guess is why the flattening software smooshed them together. ...
written 11 days ago by swbarnes2340
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Answer: A: Question on Design formula for DESeq2
... You have exactly one control? This is awful experimental design. You can't use DESeq without replicates. This data will not go through DESeq with sample 15 in it. I don't know what exactly you are trying to compare to what. I don't think you are going to be able to tease out subtle interactions ...
written 11 days ago by swbarnes2340
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Comment: C: Question on Design formula for DESeq2
... Are your samples really named 1-15? ...
written 11 days ago by swbarnes2340
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Answer: A: How to perform DEseq2 on datasets without replicates or merged replicates
... With just one sample of each kind, there is no use for fancy statistics. You can use the library normalization techniques described in DESeq, and then just compare the values directly. You won't be able to generate a p-value. ...
written 12 days ago by swbarnes2340
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Answer: A: DESeq2 comparison of treatment within tissue type, ignoring other tissue type
... > I would like to see the ATRAvsDMSO comparison for Graves tissue alone, > and for normal tissue alone. The way I currently have it set up, my > only options are for tissueGvsN, txATRAvsDMSO, or tissueG.txATRA. The > way I understand it, this is comparing all the tissue types, > regar ...
written 25 days ago by swbarnes2340
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Answer: A: DESeq2 analysis. Understanding results() and the design formula.
... WT_1h_KO_1h <- results(rnaDDS,contrast=c("condition", "WT_1h","KO_1h")) Most people would flip-flop the order, so that you are dividing the KO by the WT. > Additionally, I am interested to understand how to properly write the > design formula by adding the factors genotype and time an ...
written 25 days ago by swbarnes2340
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Answer: A: interpreting FC values from glmTreat, especially unshrunk log-fold-changes
... > These large unshrunk logFC values look like an error! Any ideas on how > to deal with it? I doubt those are errors. More likely, they are genes where one group has nearly zero expression. So obviously anything divided by zero can get really huge really easily. The shrunken fold change is ...
written 4 weeks ago by swbarnes2340
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Answer: A: BiomaRt Error: "character string of length 1."
... The biomart-based lines of the code work for me ensembl = useMart('ensembl', dataset='hsapiens_gene_ensembl', host = "www.ensembl.org", ensemblRedirect = FALSE) genemap <- getBM(attributes = c('ensembl_gene_id', 'hgnc_symbol'), filters='ensembl_gene_id', values='ENSG00000000003', mart=en ...
written 6 weeks ago by swbarnes2340
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Comment: C: DESeq2 analysis with multiple factors
... I can't make heads or tails of your coldata the way its formatted, but I'm not surprised that your three term design doesn't work. Won't each mouse belong to one batch and/or one condition? ...
written 8 weeks ago by swbarnes2340

Latest awards to swbarnes2

Scholar 4 months ago, created an answer that has been accepted. For C: Principal components in DESeq2
Scholar 5 months ago, created an answer that has been accepted. For C: Principal components in DESeq2
Supporter 5 months ago, voted at least 25 times.
Scholar 12 months ago, created an answer that has been accepted. For C: Principal components in DESeq2
Teacher 12 months ago, created an answer with at least 3 up-votes. For A: Appropriate experimental design for differential expresion

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