regarde ici E. coli promoter sequences: RegulonDB
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Human TF motifs JASPAR database
Human TF motifs
JASPAR database -
Prodoric database E. coli TF motifs ht …
Prodoric database
E. coli TF motifs
http://prodoric.tu-bs.de/ -
GOMO: predicting GO terms from PWM http …
GOMO: predicting GO terms from PWM
http://meme.nbcr.net -
Great explanation about many apply funct …
Great explanation about many apply functions in R
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Use sapply(), lapply(), vapply() when th …
Use sapply(), lapply(), vapply() when the data is data frame or list and use apply() when the data is matrix. Because matrix does not have the “direction”, e.g., row-wise or column-wise, to apply the function. So it has to be explicitly given.
apply(data, 2, FUN) # for example, column-wise application of the FUN -
Difference between “[” and “[[” in R …
Difference between “[” and “[[” in R
Those are operators for elements in R.
The difference between the two is that “[” slices the data while “[[” extracts the data.
The reason is “[[” iterates to get the data.
The manual says it in this way; “[” keep the name while “[[” drops the name, which is hard for me to understand what it means.
Example,nx <- c(Abc = 123, pi = pi) nx[1] nx[[1]]
The difference between "[" and "[[" is more prominent when they are used with c()
z <- list(a = list(b=9, c= c("helo", "world")), d=1:5) z[[c(1,2)]] returns the first and the second elements of z which are all a and d. On the other hand, z[[c(1,2)]] returns the second element of the first element which is the list c.
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Extracting a fixed position data from th …
Extracting a fixed position data from the list of vectors
I don’t exactly understand how this works but it works.v <- list(c(1,2), c(3,4), 5, c(-3:3)) sapply(v, "[", i = 2) or sapply(v, function(x) x[2]) https://stat.ethz.ch/pipermail/r-help/2007-November/145936.html
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Hash in R? Use names() ex. nucleotide …
Hash in R?
Use names()
ex.
nucleotide <- 1:4 names(nucleotide) <- c("A", "T", "G", "C")