Moderator: Sean Davis
Sean Davis ♦ 23k
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- Location:
- National Institutes of Health, Bethesda, MD
- Website:
- http://watson.nci.nih....
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- seandavis12
- Scholar ID:
- Google Scholar Page
- Last seen:
- 2 months ago
- Joined:
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- Email:
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Posts by Sean Davis
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... Do you know how to convert from your custom transcript locations to genome locations? What organism are you working with? ...
written 12 weeks ago by
Sean Davis ♦ 23k
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... The original submitters apparently filtered the data. That is not uncommon. ...
written 12 weeks ago by
Sean Davis ♦ 23k
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... These numbers are functionally the same. No, I would not merge the fastq files. I would report two Q30 numbers and two GC numbers. In all cases where the experiment "worked" as expected, the numbers for read 1 and read 2 will be quite similar. If they are not, you NEED to know that. ...
written 4 months ago by
Sean Davis ♦ 23k
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... I'd report both forward and reverse Q30 and GC from FastQC, so four numbers. This is for quality control, so having all values available will avoid "hiding" bad data. ...
written 4 months ago by
Sean Davis ♦ 23k
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... Your approach of using cox regression to get an ordered list of genes is fine. From there, you can proceed to threshold based on some cutoff for significance or use a rank-based GSEA approach. ...
written 4 months ago by
Sean Davis ♦ 23k
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... Using
resGR <- results(RAW_DESeq2AF, lfcThreshold = 1, format = "GRangesList")
or
resGR <- results(RAW_DESeq2AF, lfcThreshold = 1, format = "DataFrame")
should address the issue. ...
written 4 months ago by
Sean Davis ♦ 23k
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... Take a look at ggbio.
https://bioconductor.org/packages/ggbio
https://www.google.com/search?q=ggbio&tbm=isch
...
written 4 months ago by
Sean Davis ♦ 23k
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... The Bioconductor project does not support bioconda directly, so your mileage may vary when using bioconda. The recommended way to install Bioconductor packages is from within R using:
source("https://bioconductor.org/biocLite.R")
biocLite("DESeq2")
biocLite("edgeR")
...
written 4 months ago by
Sean Davis ♦ 23k
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... It sounds like you'll need to learn some R before proceeding. Being able to select subsets of data in R is a skill that will serve you well. You can still use WGCNA after subsetting your data if it suits your needs. ...
written 4 months ago by
Sean Davis ♦ 23k
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... You may not need the WGCNA package at all if you simply want to get correlations. Take a look at the `cor` function in R.
That said, no matter what you end up doing, you will definitely want to filter your genes before doing the correlation. Only about 50% of the genes are likely expressed in eac ...
written 4 months ago by
Sean Davis ♦ 23k
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