User: Andrew_McDavid
Andrew_McDavid • 150
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- EquivMeasures
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- 2 years ago
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Posts by Andrew_McDavid
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Comment:
C: Warning in bayes fit in MAST GSEA
... Can you provide the output from `sessionInfo()`?
...
written 16 days ago by
Andrew_McDavid • 150
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... Restart R, verify that you are running bioconductor 3.6 with BiocInstaller::biocValid() and make sure you aren't reloading from disk any "stale" objects that were created when you had mismatched package versions. Checking that you can run the equivalent code in the MAITAnalysis vignette should serv ...
written 4 months ago by
Andrew_McDavid • 150
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... Yup. Downgrade MAST to version 1.4.1 (here: http://bioconductor.org/packages/3.6/bioc/html/MAST.html) or upgrade to R 3.5 and Bioconductor 3.7.
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written 4 months ago by
Andrew_McDavid • 150
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... It looks like you have a mismash of Bioconductor 3.7 and 3.6. What does `BiocInstaller::biocValid()` report? Did you install MAST off of github, or via Bioconductor?
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written 4 months ago by
Andrew_McDavid • 150
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... Well, if all you care about is the top 10 genes, sure. But you probably will want to know p values and effect sizes, too, which are all in the `datatable.` You can get the top 10 genes by contrast by `order`ing it by contrast and then p value.
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written 5 months ago by
Andrew_McDavid • 150
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If I understood correctly, type1 cells are differentially upregulated in Gene{1,2,7,8}.
The * should represent significance (p < 0.01).
For both the type and cngeneson covaraites as the message at the top of the output states,
the top 4 genes by Z score are showh. The * indicates which contr ...
written 5 months ago by
Andrew_McDavid • 150
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... The random effects model
lmer.output <- zlm(~ Treatment +(1|Treatment:Plate), data,method='glmer', ebayes=FALSE)
seems like the safer bet. In the fixed effects model, you would probably end up with
an awkward situation where the interactions are significant in some genes and not others, and I do ...
written 5 months ago by
Andrew_McDavid • 150
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Comment:
C: Using MAST with DropSeq data
... Technically the issue is the quality of the normality assumption in the continuous portion of the model. In my experience the non-zero component of the log2(1+CPM) has appeared pretty symmetric for droplet technologies, but you could evaluate this yourself informally graphically or formally with t ...
written 6 months ago by
Andrew_McDavid • 150
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... The "test" happens in `calcZ`,
which is using the means and variances collected in `gsea@tests`.
The test is nothing fancy, just a normal theory Z-test.
The fancy thing is that the variance collected in `calcZ` consists of a quadratic form on the gene-gene covariance matrix, and that the covari ...
written 6 months ago by
Andrew_McDavid • 150
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... In the MAITAnalysis vignette, section 5, a competitive gene set enrichment is performed
zlmCond <- zlm(~condition + cngeneson, sca)
# bootstrap, resampling cells
# R should be set to >50 if you were doing this for real.
boots <- bootVcov1(zlmCond, R=4)
gsea <- gseaAfterBoot(zlmCond, bo ...
written 6 months ago by
Andrew_McDavid • 150
Latest awards to Andrew_McDavid
Scholar
17 months ago,
created an answer that has been accepted.
For A: MAST reported warning "Coefficients ... are never estimible and will be dropped.
Scholar
17 months ago,
created an answer that has been accepted.
For A: Estimating cell size factors for single cell RNA-seq data
Scholar
17 months ago,
created an answer that has been accepted.
For A: How do you test a contrast in MAST?
Teacher
17 months ago,
created an answer with at least 3 up-votes.
For A: How do you test a contrast in MAST?
Scholar
17 months ago,
created an answer that has been accepted.
For A: MAST reported warning "Coefficients ... are never estimible and will be dropped.
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