Moderator: b.nota
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C: RNA Expression Pathway Database
... I am afraid that what you want: "expression of X causes an increase/decrease in the expression of Y" is too simplistic for real biology. What I mean is that biology is more complex than finding one to one gene expression relationships.
Databases containing data with "protein X binds protein Y" or " ...
written 2 days ago by
b.nota ♦ 5.4k
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... Can you show with head how your fastq file looks like? Is it really in color space? ...
written 3 days ago by
b.nota ♦ 5.4k
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... Please read more about Solid data conversion to fastq [here][1]. You probably lose information with the conversion to fastq.
[1]: https://www.biostars.org/p/45631/ ...
written 3 days ago by
b.nota ♦ 5.4k
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... Too short means too short alignment. Are you sure you use the right reference? ...
written 3 days ago by
b.nota ♦ 5.4k
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... Let's show an example with random data, because I don't have your RMA values.
# Let's make a random matrix first
set.seed(11)
emptyMatrix <- matrix(nrow = 19000, ncol = 18)
randomMatrix <-apply(emptyMatrix, c(1,2), function(x) sample(c(1:16), 1))
colnames(randomMatrix) &l ...
written 3 days ago by
b.nota ♦ 5.4k
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... Did you look in the link I sent? I don't see anything about CDF file being necessary.
> exprsFile <- file.path(dataDirectory, "exprsData.txt")
> exprs <- as.matrix(read.table(exprsFile, header=TRUE, sep="\t",
+ row.names=1,
+ ...
written 3 days ago by
b.nota ♦ 5.4k
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... You can import normalized values into `limma`, using `ExpressionSet()` https://www.bioconductor.org/packages/3.7/bioc/vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf.
A PCA can be made in R with the `prcomp()` function https://stat.ethz.ch/R-manual/R-devel/library/stats/html/prcomp.html. ...
written 3 days ago by
b.nota ♦ 5.4k
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... The unique read group header (`string` after `-R`) makes it a bit difficult for a `for` loop, unless you can extract that `string` easily? ...
written 4 days ago by
b.nota ♦ 5.4k
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... Yes you are testing "exp" vs "ctrl" with this design, but with the "day" effect taken into account (think of it as a similar approach to a paired t-test). ...
written 4 days ago by
b.nota ♦ 5.4k
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... If you want to include `"paired"` in your design, you should not make the `group` factor, but include both `experiment` and `day` in the design.
experiment <- factor(targets$Experiment)
day <- factor(targets$Day)
design <- model.matrix(~0+experiment+day)
# Depending on ...
written 4 days ago by
b.nota ♦ 5.4k
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