I search for a command to compute Walsh-Hadamard Transform of an image in R, but I don't find anything. In MATLAB fwht use for this. this command implement Walsh-Hadamard Tranform to each row of matrix. Can anyone introduce a similar way to compute Walsh-Hadamard on rows or columns of Matrix in R?
I find a package here:
http://www2.uaem.mx/r-mirror/web/packages/boolfun/boolfun.pdf
But why this package is not available when I want to install it?
Packages that are not maintained get put in the Archive. They get put there when that aren't updated to match changing requirements or start making errors with changing R code base. https://cran.r-project.org/web/packages/boolfun/index.html
It's possible that you might be able to extract useful code from the archive version, despite the relatively ancient version of R that package was written under.
The R code for walshTransform calls an object code routine:
walshTransform <- function ( truthTable ) # /!\ should check truthTable values are in {0,1}
{
len <- log(length(truthTable),base=2)
if( len != round(len) )
stop("bad truth table length")
res <- .Call( "walshTransform",
as.integer(truthTable),
as.integer(len))
res
}
Installing the package succeeded on my Mac, but would require the appropriate toolchain on whatever OS you are working in.
Related
Earlier, I had posted a question and was able to load in my data successfully and create a topGO object after some help. I'm trying to visualise GO terms that are significantly associated with the list of differentially expressed genes that I have from mouse RNA-seq data.
Now, I'd want to raise a concern about ViSEAGO's tutorial. The tutorial initially specifies loading two files: 'selection.txt' and 'background.txt'. The origin of these files is not clearly stated. However, after a lot of digging into topGO's documentation, I was able to find the datatypes for each of the files. But, even after following these, I have a problem running the following code. Does anyone have any insights to share?
WORKING CODE:
mysampleGOdata <- new("topGOdata",
description = "my Simple session",
ontology = "BP",
allGenes = geneList_new,
nodeSize = 1,
annot = annFUN.org,
mapping="org.Mm.eg.db",
ID = "SYMBOL")
resultFisher <- runTest(mysampleGOdata, algorithm = "classic", statistic = "fisher")
head(GenTable(mysampleGOdata,fisher=resultFisher),20)
myNewBP<-GenTable(mysampleGOdata,fisher=resultFisher)
PROBLEMS:
> head(myNewBP,2)
GO.ID Term Annotated Significant Expected fisher
1 GO:0006006 glucose metabolic process 194 12 0.19 1.0e-19
2 GO:0019318 hexose metabolic process 223 12 0.22 5.7e-19
> ###################
> # merge results
> myBP_sResults<-ViSEAGO::merge_enrich_terms(
+ Input=list(
+ condition=c("mysampleGOdata","resultFisher")
+ )
+ )
Error in setnames(x, value) :
Can't assign 3 names to a 2 column data.table
> myNewBP<-GenTable(mysampleGOdata,fisher=resultFisher)
> ###################
> # display the merged table
> ViSEAGO::show_table(myNewBP)
Error in ViSEAGO::show_table(myNewBP) :
object must be enrich_GO_terms, GO_SS, or GO_clusters class objects
According to the tutorial, the printed table contains for each enriched GO terms, additional columns including the list of significant genes and frequency (ratio of the number of significant genes to the number of background genes) evaluated by comparison. I think I have that, but it's definitely not working.
Can someone see why? I'm not very clear on this.
Thanks!
I think you try to circumvent an error you made at the beginning. You receive the error due to the fact that you did not use the wrapper function from the ViSEAGO package. As you stated in your last question, you had initial problems formatting your data.
Here are some tips:
The "selection" file is a character vector with your DEGs names or IDs. I recommend using EntrezID's.
The "Background" file is a character vector with known genes. I recommend using EntrezID's as well. You can easily generate this character vector with:
background=keys(org.Hs.eg.db, keytype ='ENTREZID').
With these two files, you can easily proceed to the next steps of the package as described in the vignette.
# connect to EntrezGene
EntrezGene<-ViSEAGO::EntrezGene2GO()
# load GO annotations from EntrezGene
# with the add of GO annotations from orthologs genes (see above)
#id = "9606" = homo sapiens
myGENE2GO<-ViSEAGO::annotate(id="9606", EntrezGene)
BP<-ViSEAGO::create_topGOdata(
geneSel = selection, #your DEG vector
allGenes = background, #your created background vector
gene2GO=myGENE2GO,
ont="BP",
nodeSize=5
)
classic<-topGO::runTest(
BP,
algorithm ="classic",
statistic = "fisher"
)
# merge results
BP_sResults<-ViSEAGO::merge_enrich_terms(
Input=list(
condition=c("BP","classic")
)
)
You should get a merged list of your enriched GO terms with the corresponding statistical tests you prefer.
I have faced this problem recently, it was very frustrating. In my case the whole issue seemed to be related to the package version I was using.
I used conda to install ViSEAGO. Nevertheless, R's version in my conda environment was a bit old (i.e. 3.6.1 to be specific). Therefore, when installing ViSEAGO with conda, the version 1.0.0 of the package was installed. Please note that the most recent version of ViSEAGO is 1.4.0.
Therefore, I created a conda environment with R version 4.0.3, and repeated the procedure to install ViSEAGO by using conda. When doing this, ViSEAGO's 1.4.0 version was installed, and everything went fine.
I've tried to backtrack the error, and only find one thing: in the older ViSEAGO version, the function Custom2GO loaded tables with 4 columns; in the most recent version it admits 5 columns (the new one being 'gene_symbol'). I think this disagreement might be part of the issue, as the source code of the function merge_enrich_terms seems to deal with the columns 'gene_id' and 'gene_symbol' at some point, but I'm not sure.
Hope you find my comment helpful!
Cheers,
Mauricio
I like using function reshape from the matlab package, but I need then to specify base::sum(m) each time I want to sum the elements of my matrix or else matlab::sum is called, which only sums by columns..
I need loading package gtools to use the rdirichlet function, but then the function gtools::logit masks the function pracma::logit that I like better..
I gess there are no such things like:
library(loadOnly = "rdirichlet", from = "gtools")
or
library(loadEverythingFrom = "matlab", except = "sum")
.. because functions from the package matlab may internaly work on the matlab::sum function. So the latter must be loaded. But is there no way to get this behavior from the point of view of the user? Something that would feel like:
library(pracma)
library(matlab)
library(gtools)
sum <- base::sum
logit <- pracma::logit
.. but that would not spoil your ls() with all these small utilitary functions?
Maybe I need defining my own default namespace?
To avoid spoiling your ls, you can do something like this:
.ns <- new.env()
.ns$sum <- base::sum
.ns$logit <- pracma::logit
attach(.ns)
To my knowledge there is no easy answer to what you want to achieve. The only dirty hack I can think of is to download the source of the packages "matlab", "gtools", "pracma" and delete the offending functions from their NAMESPACE file prior to installation from source (with R CMD INSTALL package).
However, I would recommend using the explicit notation pracma::logit, because it improves readability of your code for other people and yourself in the future.
This site gives a good overview about package namespaces:
http://r-pkgs.had.co.nz/namespace.html
I am running a machine that only has R 2.10 and cannot be updated (this is a fact: it cannot be changed).
I need to use the "expm" package's functionality for taking the kth power of a matrix (it is the function %^%) but I cannot figure out how to copy the source code, since the earliest version of this package was made for versions later than R 2.10. On R, after installing the expm package, running:
library(expm)
getAnywhere('%^%')
gives me the source code, but then when I copy this source code in the form %^% <- *source code*, I get the error
Error in stopifnot(is.numeric(x) || is(x, "dMatrix"), length(d <- dim(x)) == :
object 'x' not found
Can anyone help me figure out how to use the original implementation of %^% via source code?
Use backticks ` to denote a non-syntactic name.
`%^%` <- ....
I was trying to acquaint myself with R's nChooseK function but I can't get it to work. I thought it was part of the standard setup (i.e. no additional package needed).
Please help. Here is what I tried:
> nChooseK(10,2)
Error: could not find function "nChooseK"
> n<-4;k<-2
> print(nChooseK(n,k))
Error in print(nChooseK(n, k)) : could not find function "nChooseK"
the last one was an example I saw here: R basic nChooseK
The function is in the R.basic package which is not part of the default R installation. You probably meant to use just choose().
As joran mentions the function nChooseK is a part of R.basic. You can tell this from the example you posted by looking at the top of the page:
You'll notice the "R.basic" in the curley braces which tells you that that function is a part of the "R.basic" package. So to use nChooseK you'll first need to load that package
library(R.basic)
If you don't have R.basic installed yet then you'll need to install it
install.packages("R.basic", contriburl="http://www.braju.com/R/repos/")
library(R.basic)
But as noted the choose function in base R does the same thing
choose(37, 12)
#[1] 1852482996
nChooseK(37, 12)
#[1] 1852482996
I'm busy exploring the package vegan for R, using it to calculate nestedness of community matrices and null models. I'm particularly interested in using the permat functions as well as Oecosimu.
However, when running my program I obtained the following errors:
Error in nullmodel(comm, method) : could not find function "list2env"
Error in nullmodel(m, ALGO) : could not find function "list2env"
I then even ran an example (given below) of how to use these functions given by the R help function, and even these examples gave the same error. Am I suppose to import something else in order to use these functions or how do I go about fixing this?
Examples:
m <- matrix(c(
1,3,2,0,3,1,
0,2,1,0,2,1,
0,0,1,2,0,3,
0,0,0,1,4,3
), 4, 6, byrow=TRUE)
x1 <- permatswap(m, "quasiswap")
summary(x1)
x2 <- permatfull(m)
summary(x2)
x3 <- permatfull(m, "none", mtype="prab")
x3$orig
summary(x3)
x4 <- permatfull(m, strata=c(1,1,2,2))
summary(x4)
Technically, this is a bug in the development version of Vegan on R-Forge. We were failing to declare a dependency on R versions >= 2.12 in DESCRIPTION. I have checked in the relevant change to the source tree to fix this but it will take a day or so before the tarball and binaries are rebuilt by R-Forge.
That said, you should probably update your R to something more recent. Or use the versions of those functions provided in Vegan 2.0-x on CRAN.
list2env is part of R base, which means it comes with the distribution, not in an add-on package. So if you don't have it you're probably either running an old version of R or have a broken installation. The example worked fine for me, with R 2.12.1 and vegan 2.1-0.
Your code works for me without an error message
The most probable cause of your error is your using old versions of R, vegan or permute
The R news for changes says
CHANGES IN R VERSION 2.12.0: NEW FEATURES:
o New list2env() utility function as an inverse of
as.list(<environment>) and for fast multi-assign() to existing
environment. as.environment() is now generic and uses list2env()
as list method.
CHANGES IN R VERSION 2.12.1: BUG FIXES:
o When list2env() created an environment it was missing a PROTECT
call and so was vulnerable to garbage collection.
CHANGES IN R VERSION 2.13.0: NEW FEATURES:
o list2env(envir = NULL) defaults to hashing (with a suitably sized
environment) for lists of more than 100 elements.
So update your version of R and the packages and try again.