## User: Onyi Ukay

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Website:
https://oukaire.github...
Last seen:
17 hours ago
Joined:
2 months ago
Email:
o********@alaalumni.org

#### Posts by Onyi Ukay

<prev • 7 results • page 1 of 1 • next >
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... Thanks for affirming Mikhael's answer Aaron. TMM is indeed a DGEList, defined as: dge <- DGEList(counts=counts) keep <- filterByExpr(dge, design, min.count=5)  dge <- dge[keep,,keep.lib.sizes=FALSE] TMM <- calcNormFactors(dge) Thank you for also pointing out a different problem th ...
written 5 days ago by Onyi Ukay0
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written 7 days ago by Onyi Ukay0
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... Here is a snapshot of a data.frame, df:  Subject | Cell_type | Condition | Viral_load --------|-----------|-----------|------------ 1 | A | normal | 10 1 | B | normal | 180 2 | A | diseased | 500 2 | B | diseased | 90 3 | A ...
written 8 days ago by Onyi Ukay0 • updated 8 days ago by mikhael.manurung10
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... For the part of my analysis that does not directly handle confounded Subjects and Condition, what if I took this approach: Take two subsets, each of Conditionnormal and Conditiondiseased, then run DESeq with the design = ~ Subject + Cell_type on each subset. Would you recommend such an approach th ...
written 8 weeks ago by Onyi Ukay0
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... I appreciate the help! ...
written 8 weeks ago by Onyi Ukay0
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... Michael, thank you for your insightful response. I have some additional questions. Suppose my references are Subject1, Cell_typeA, and Conditionnormal. I tried the method and extracted the term for one of four comparisons of interest: contrast=list("Conditionnormal.Cell_typeB","Conditiondiseased.C ...
written 8 weeks ago by Onyi Ukay0
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... My actual dataset is larger but here's a snapshot of what it looks like:  Subject | Cell_type | Condition ---------|------------|------------ 1           | A              | normal 1           | B              | normal 2           | A              | diseased 2           | B              | diseased 3 ...
written 9 weeks ago by Onyi Ukay0

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