Moderator: andrew.j.skelton73

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Posts by andrew.j.skelton73

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Comment: C: ATYPICAL VOLCANO PLOT RNA SEQ
... It would be helpful if you included code for your analysis, did you use `lfcShrink`? Are your samples all from the same experiment? What did you PCA show? ...
written 8 days ago by andrew.j.skelton735.4k
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Answer: A: eQTL analysis considering P-values
... For generic QTL analyses, I'd recommend the MatrixEQTL package and associated methodology. It's blazing fast providing you have the right LINPACK libraries. [Website][1] / [Paper][2] / [Package (CRAN)][3] Check out the website for some useful examples and configurations [1]: http://www.bios. ...
written 10 days ago by andrew.j.skelton735.4k
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Comment: C: WGCNA Co-expression network analysis on cuffdiff output
... What have you tried to solve your WGCNA question? ...
written 8 weeks ago by andrew.j.skelton735.4k
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Comment: C: WGCNA Co-expression network analysis on cuffdiff output
... What have you tried? - Also beware that CuffDiff / Tophat2/ Cufflinks have been superseded by HISAT2 / Stringtie / Ballgown. ...
written 8 weeks ago by andrew.j.skelton735.4k
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Comment: C: transcription expression matrix for DESEQ2
... In this case, it's probably worth contacting the author, or looking at potentially more detailed methods in the supplementary. Keep in mind, "*just because you can, doesn't mean you should*" - It's worth critically assessing what DESeq2 requires as inputs, against what they've provided from bitSeq. ...
written 8 weeks ago by andrew.j.skelton735.4k
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Answer: A: transcription expression matrix for DESEQ2
... bitSeq is more an all in one for differential transcript expression. You wouldn't be able to input those estimates into DESeq2, I'd recommend following one of these methodologies if you want to do differential gene expression or differential transcript expression from the same quantifications: **S ...
written 9 weeks ago by andrew.j.skelton735.4k
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Comment: C: transcription expression matrix for DESEQ2
... Where did this expression matrix come from? - You can provide matrix input for DESeq2, but this must be integer counts of genes. For transcript level count estimates, you need to follow the [tximport][1] protocol, from either Salmon or Kallisto. [1]: https://bioconductor.org/packages/release/bio ...
written 9 weeks ago by andrew.j.skelton735.4k
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Comment: C: WGCNA-consensus network analysis using RNA-seq data
... If these are from different experiments, then that's a whole different box of questions. Normalising cross experiment is far from trivial, and can only be done in some cases. The big caveat for WGCNA is that you need a decent number (>20) of samples to get interpretable output. If these are cro ...
written 9 weeks ago by andrew.j.skelton735.4k
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Answer: A: Is it possible to make a PCA plot for samples using TPM exprssion values in R?
... You should transform your data to a log-like scale. If you're analysing in DESeq2, look at `vst` or `rlog` methods, alternatively if you're using `Limma Voom`, then your data should be good to go. Have a look at the [tximport][1] package if you're confused about these different input metrics. When ...
written 9 weeks ago by andrew.j.skelton735.4k
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Answer: A: WGCNA-consensus network analysis using RNA-seq data
... For use in WGCNA, your data should be log2-like, so [vst][1] or [rlog][2] transformations are appropriate. Alternatively, you could use [Limma Voom][3] too for RNAseq data. [1]: https://rdrr.io/bioc/DESeq2/man/varianceStabilizingTransformation.html [2]: https://rdrr.io/bioc/DESeq2/man/rlog.ht ...
written 9 weeks ago by andrew.j.skelton735.4k

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Scholar 5 months ago, created an answer that has been accepted. For A: Surrogate Variable Analysis for Complex Experimental Design
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