Error while mapping SYMBOLS to ENTREZID - r

I am getting a strange error converting Gene Symbols to Entrez ID. Here is my code:
testData = read.delim("IL_CellVar.txt",head=T,row.names = 2)
testData[1:5,1:3]
# ClustID Genes.Symbol ChrLoc
# NM_001034168.1 4 Ank2 chrNA:-1--1
# NM_013795.4 4 Atp5l chrNA:-1--1
# NM_018770 4 Igsf4a chrNA:-1--1
# NM_146150.2 4 Nrd1 chrNA:-1--1
# NM_134065.3 4 Epdr1 chrNA:-1--1
clustNum = 5
filteredClust = testData[testData$ClustID == clustNum,]
any(is.na(filteredClust$Genes.Symbol))
# [1] FALSE
selectedEntrezIds <- unlist(mget(filteredClust$Genes.Symbol,org.Mm.egSYMBOL2EG))
# Error in unlist(mget(filteredClust$Genes.Symbol, org.Mm.egSYMBOL2EG)) :
# error in evaluating the argument 'x' in selecting a method for function
# 'unlist': Error in #.checkKeysAreWellFormed(keys) :
# keys must be supplied in a character vector with no NAs
Another approach fails too:
selectedEntrezIds = select(org.Mm.eg.db,filteredClust$Genes.Symbol, "ENTREZID")
# Error in .select(x, keys, columns, keytype = extraArgs[["kt"]], jointype = jointype) :
# 'keys' must be a character vector
Just for the sake or error, removing 'NA', doesn't help:
a <- filteredClust$Genes.Symbol[!is.na(filteredClust$Genes.Symbol)]
selectedEntrezIds <- unlist(mget(a,org.Mm.egSYMBOL2EG))
# Error in unlist(mget(a, org.Mm.egSYMBOL2EG)) :
# error in evaluating the argument 'x' in selecting a method for function
# 'unlist': Error in # .checkKeysAreWellFormed(keys) :
# keys must be supplied in a character vector with no NAs
I am not sure why I am getting this error as the master file from which gene symbols were extracted for testData gives no problem while converting to EntrezID. Would apprecite help on this.

Since you didn't provide a minimal reproducible example for us to replicate the error you've experienced, I'm making a speculation here based on the error message. This is most likely caused by the default behavior of read.delim and functions alike (read.csv, read.table etc.) that converts strings in your data file to factor's.
You need to add an extra parameter to read.delim, specifically, stringsAsFactors=F (by default, it is TRUE).
That is,
testData = read.delim("IL_CellVar.txt", head=T, row.names = 2, stringsAsFactors=F)
If you read the documentation:
stringsAsFactors
logical: should character vectors be converted to factors? Note that this is overridden by as.is and colClasses, both of which allow finer control.
You can check the class of your Gene.symbol column by:
class(testData$Gene.Symbol)
and I guess it woul be "factor".
This leads to the error you had:
# Error in .select(x, keys, columns, keytype = extraArgs[["kt"]], jointype = jointype) :
# 'keys' must be a character vector
You can also manually convert the factors to strings/characters by:
testData$Gene.Symbol <- as.character(testData$Gene.Symbol)
You can read more about this peculiar behavior in this chapter of Hadley's book "Advanced R". And I'm quoting the relevant paragraph here:
... Unfortunately, most data loading functions in R automatically convert character vectors to factors. This is suboptimal, because there’s no way for those functions to know the set of all possible levels or their optimal order. Instead, use the argument stringsAsFactors = FALSE to suppress this behaviour, and then manually convert character vectors to factors using your knowledge of the data. A global option, options(stringsAsFactors = FALSE), is available to control this behaviour, but I don’t recommend using it. Changing a global option may have unexpected consequences when combined with other code (either from packages, or code that you’re source()ing), and global options make code harder to understand because they increase the number of lines you need to read to understand how a single line of code will behave. ...

Related

Uncommon error message converting Matrix to Sparse in R

I'm trying to run a LASSO on our dataset, and to do so, I need to convert non-numeric variables to numeric, ideally via a sparse matrix. However, when I try to use the Matrix command, I get the same error:
Error in asMethod(object) : invalid class 'NA' to dup_mMatrix_as_geMatrix
I thought this was due to NA's in my data, so I did an na.omit and got the same error. I tried again with a mini subset of my code and got the same error again:
> sparsecombined <- Matrix(combined1[1:10,],sparse=TRUE)
Error in asMethod(object) : invalid class 'NA' to dup_mMatrix_as_geMatrix
This is the data set I tried to convert with that last line of code:
Is there anything that jumps out that might prevent sparse conversion?
The easiest way to incorporate categorical variables into a LASSO is to use my glmnetUtils package, which provides a formula/data frame interface to glmnet.
glmnet(ArrDelay ~ ArrTime + uniqueCarrier + TailNum + Origin + Dest,
data=combined1, sparse=TRUE)
This automatically handles categorical vars via one-hot encoding (also known as dummy variables). It can also use sparse matrices if so desired.
I think the error is due to the fact that you have non-numeric data types in your matrix.
Perhaps first convert your nun-numeric columns like UniqueCarrier to binary vectors using one-hot encoding. And only then convert the matrix to sparse.
Here is my code that I used for that conversion:
# Convert Genre into binary variables
# Convert genreVector into a corpus in order to parse each text string into a binary vector with 1s representing the presence of a genre and 0s the absence
library(tm)
library(slam)
convertToBinary <- function(category) {
genreVector = category
genreVector = strsplit(genreVector, "(\\s)?,(\\s)?") # separate out commas
genreVector = gsub(" ", "_", genreVector) # combine DirectorNames with whitespaces
genreCorpus = Corpus(VectorSource(genreVector))
#dtm = DocumentTermMatrix(genreCorpus, list(dictionary=genreNames))
dtm = DocumentTermMatrix(genreCorpus)
binaryGenreVector = inspect(dtm)
return(binaryGenreVector)
#return(data.frame(binaryGenreVector)) # convert binaryGenreVector to dataframe
}
directorBinary = convertToBinary(x$Director)
directorBinaryDF = as.data.frame(directorBinary)
See nograpes answer in
recommenderlab, Error in asMethod(object) : invalid class 'NA' to dup_mMatrix_as_geMatrix
I got this error due to passing a data frame where a matrix was expected, and it looks like that's the same reason you are getting it. The solution in simple -- convert your data to a matrix before passing it to the Matrix function:
sparsecombined <- Matrix(as.matrix(combined1[1:10,]),sparse=TRUE)
In your case, this code will probably complain because you have some non-numeric data stored in there (e.g. the TailNum column). So you would need to downselect to just the numeric columns.

R: passing by parameter to function and using apply instead of nested loop and recursive indexing failed

I have two lists of lists. humanSplit and ratSplit. humanSplit has element of the form::
> humanSplit[1]
$Fetal_Brain_408_AGTCAA_L001_R1_report.txt
humanGene humanReplicate alignment RNAtype
66 DGKI Fetal_Brain_408_AGTCAA_L001_R1_report.txt 6 reg
68 ARFGEF2 Fetal_Brain_408_AGTCAA_L001_R1_report.txt 5 reg
If you type humanSplit[[1]], it gives the data without name $Fetal_Brain_408_AGTCAA_L001_R1_report.txt
RatSplit is also essentially similar to humanSplit with difference in column order. I want to apply fisher's test to every possible pairing of replicates from humanSplit and ratSplit. Now I defined the following empty vector which I will use to store the informations of my fisher's test
humanReplicate <- vector(mode = 'character', length = 0)
ratReplicate <- vector(mode = 'character', length = 0)
pvalue <- vector(mode = 'numeric', length = 0)
For fisher's test between two replicates of humanSplit and ratSplit, I define the following function. In the function I use `geneList' which is a data.frame made by reading a file and has form:
> head(geneList)
human rat
1 5S_rRNA 5S_rRNA
2 5S_rRNA 5S_rRNA
Now here is the main function, where I use a function getGenetype which I already defined in other part of the code. Also x and y are integers :
fishertest <-function(x,y) {
ratReplicateName <- names(ratSplit[x])
humanReplicateName <- names(humanSplit[y])
## merging above two based on the one-to-one gene mapping as in geneList
## defined above.
mergedHumanData <-merge(geneList,humanSplit[[y]], by.x = "human", by.y = "humanGene")
mergedRatData <- merge(geneList, ratSplit[[x]], by.x = "rat", by.y = "ratGene")
## [here i do other manipulation with using already defined function
## getGenetype that is defined outside of this function and make things
## necessary to define following contingency table]
contingencyTable <- matrix(c(HnRn,HnRy,HyRn,HyRy), nrow = 2)
fisherTest <- fisher.test(contingencyTable)
humanReplicate <- c(humanReplicate,humanReplicateName )
ratReplicate <- c(ratReplicate,ratReplicateName )
pvalue <- c(pvalue , fisherTest$p)
}
After doing all this I do the make matrix eg to use in apply. Here I am basically trying to do something similar to double for loop and then using fisher
eg <- expand.grid(i = 1:length(ratSplit),j = 1:length(humanSplit))
junk = apply(eg, 1, fishertest(eg$i,eg$j))
Now the problem is, when I try to run, it gives the following error when it tries to use function fishertest in apply
Error in humanSplit[[y]] : recursive indexing failed at level 3
Rstudio points out problem in following line:
mergedHumanData <-merge(geneList,humanSplit[[y]], by.x = "human", by.y = "humanGene")
Ultimately, I want to do the following:
result <- data.frame(humanReplicate,ratReplicate, pvalue ,alternative, Conf.int1, Conf.int2, oddratio)
I am struggling with these questions:
In defining fishertest function, how should I pass ratSplit and humanSplit and already defined function getGenetype?
And how I should use apply here?
Any help would be much appreciated.
Up front: read ?apply. Additionally, the first three hits on google when searching for "R apply tutorial" are helpful snippets: one, two, and three.
Errors in fishertest()
The error message itself has nothing to do with apply. The reason it got as far as it did is because the arguments you provided actually resolved. Try to do eg$i by itself, and you'll see that it is returning a vector: the corresponding column in the eg data.frame. You are passing this vector as an index in the i argument. The primary reason your function erred out is because double-bracket indexing ([[) only works with singles, not vectors of length greater than 1. This is a great example of where production/deployed functions would need type-checking to ensure that each argument is a numeric of length 1; often not required for quick code but would have caught this mistake. Had it not been for the [[ limit, your function may have returned incorrect results. (I've been bitten by that many times!)
BTW: your code is also incorrect in its scoped access to pvalue, et al. If you make your function return just the numbers you need and the aggregate it outside of the function, your life will simplify. (pvalue <- c(pvalue, ...) will find pvalue assigned outside the function but will not update it as you want. You are defeating one purpose of writing this into a function. When thinking about writing this function, try to answer only this question: "how do I compare a single rat record with a single human record?" Only after that works correctly and simply without having to overwrite variables in the parent environment should you try to answer the question "how do I apply this function to all pairs and aggregate it?" Try very hard to have your function not change anything outside of its own environment.
Errors in apply()
Had your function worked properly despite these errors, you would have received the following error from apply:
apply(eg, 1, fishertest(eg$i, eg$j))
## Error in match.fun(FUN) :
## 'fishertest(eg$i, eg$j)' is not a function, character or symbol
When you call apply in this sense, it it parsing the third argument and, in this example, evaluates it. Since it is simply a call to fishertest(eg$i, eg$j) which is intended to return a data.frame row (inferred from your previous question), it resolves to such, and apply then sees something akin to:
apply(eg, 1, data.frame(...))
Now that you see that apply is being handed a data.frame and not a function.
The third argument (FUN) needs to be a function itself that takes as its first argument a vector containing the elements of the row (1) or column (2) of the matrix/data.frame. As an example, consider the following contrived example:
eg <- data.frame(aa = 1:5, bb = 11:15)
apply(eg, 1, mean)
## [1] 6 7 8 9 10
# similar to your use, will not work; this error comes from mean not getting
# any arguments, your error above is because
apply(eg, 1, mean())
## Error in mean.default() : argument "x" is missing, with no default
Realize that mean is a function itself, not the return value from a function (there is more to it, but this definition works). Because we're iterating over the rows of eg (because of the 1), the first iteration takes the first row and calls mean(c(1, 11)), which returns 6. The equivalent of your code here is mean()(c(1, 11)) will fail for a couple of reasons: (1) because mean requires an argument and is not getting, and (2) regardless, it does not return a function itself (in a "functional programming" paradigm, easy in R but uncommon for most programmers).
In the example here, mean will accept a single argument which is typically a vector of numerics. In your case, your function fishertest requires two arguments (templated by my previous answer to your question), which does not work. You have two options here:
Change your fishertest function to accept a single vector as an argument and parse the index numbers from it. Bothing of the following options do this:
fishertest <- function(v) {
x <- v[1]
y <- v[2]
ratReplicateName <- names(ratSplit[x])
## ...
}
or
fishertest <- function(x, y) {
if (missing(y)) {
y <- x[2]
x <- x[1]
}
ratReplicateName <- names(ratSplit[x])
## ...
}
The second version allows you to continue using the manual form of fishertest(1, 57) while also allowing you to do apply(eg, 1, fishertest) verbatim. Very readable, IMHO. (Better error checking and reporting can be used here, I'm just providing a MWE.)
Write an anonymous function to take the vector and split it up appropriately. This anonymous function could look something like function(ii) fishertest(ii[1], ii[2]). This is typically how it is done for functions that either do not transform as easily as in #1 above, or for functions you cannot or do not want to modify. You can either assign this intermediary function to a variable (which makes it no longer anonymous, figure that) and pass that intermediary to apply, or just pass it directly to apply, ala:
.func <- function(ii) fishertest(ii[1], ii[2])
apply(eg, 1, .func)
## equivalently
apply(eg, 1, function(ii) fishertest(ii[1], ii[2]))
There are two reasons why many people opt to name the function: (1) if the function is used multiple times, better to define once and reuse; (2) it makes the apply line easier to read than if it contained a complex multi-line function definition.
As a side note, there are some gotchas with using apply and family that, if you don't understand, will be confusing. Not the least of which is that when your function returns vectors, the matrix returned from apply will need to be transposed (with t()), after which you'll still need to rbind or otherwise aggregrate.
This is one area where using ddply may provide a more readable solution. There are several tutorials showing it off. For a quick intro, read this; for a more in depth discussion on the bigger picture in which ddply plays a part, read Hadley's Split, Apply, Combine Strategy for Data Analysis paper from JSS.

Error with knn function

I try to run this line :
knn(mydades.training[,-7],mydades.test[,-7],mydades.training[,7],k=5)
but i always get this error :
Error in knn(mydades.training[, -7], mydades.test[, -7], mydades.training[, :
NA/NaN/Inf in foreign function call (arg 6)
In addition: Warning messages:
1: In knn(mydades.training[, -7], mydades.test[, -7], mydades.training[, :
NAs introduced by coercion
2: In knn(mydades.training[, -7], mydades.test[, -7], mydades.training[, :
NAs introduced by coercion
Any idea please ?
PS : mydades.training and mydades.test are defined as follow :
N <- nrow(mydades)
permut <- sample(c(1:N),N,replace=FALSE)
ord <- order(permut)
mydades.shuffled <- mydades[ord,]
prop.train <- 1/3
NOMBRE <- round(prop.train*N)
mydades.training <- mydades.shuffled[1:NOMBRE,]
mydades.test <- mydades.shuffled[(NOMBRE+1):N,]
I suspect that your issue lies in having non-numeric data fields in 'mydades'. The error line:
NA/NaN/Inf in foreign function call (arg 6)
makes me suspect that the knn-function call to the C language implementation fails. Many functions in R actually call underlying, more efficient C implementations, instead of having an algorithm implemented in just R. If you type just 'knn' in your R console, you can inspect the R implementation of 'knn'. There exists the following line:
Z <- .C(VR_knn, as.integer(k), as.integer(l), as.integer(ntr),
as.integer(nte), as.integer(p), as.double(train), as.integer(unclass(clf)),
as.double(test), res = integer(nte), pr = double(nte),
integer(nc + 1), as.integer(nc), as.integer(FALSE), as.integer(use.all))
where .C means that we're calling a C function named 'VR_knn' with the provided function arguments. Since you have two of the errors
NAs introduced by coercion
I think two of the as.double/as.integer calls fail, and introduce NA values. If we start counting the parameters, the 6th argument is:
as.double(train)
that may fail in cases such as:
# as.double can not translate text fields to doubles, they are coerced to NA-values:
> as.double("sometext")
[1] NA
Warning message:
NAs introduced by coercion
# while the following text is cast to double without an error:
> as.double("1.23")
[1] 1.23
You get two of the coercion errors, which are probably given by 'as.double(train)' and 'as.double(test)'. Since you did not provide us with exact details of how 'mydades' is, here are some of my best guesses (and an artificial multivariate normal distribution data):
library(MASS)
mydades <- mvrnorm(100, mu=c(1:6), Sigma=matrix(1:36, ncol=6))
mydades <- cbind(mydades, sample(LETTERS[1:5], 100, replace=TRUE))
# This breaks knn
mydades[3,4] <- Inf
# This breaks knn
mydades[4,3] <- -Inf
# These, however, do not introduce the coercion for NA-values error message
# This breaks knn and gives the same error; just some raw text
mydades[1,2] <- mydades[50,1] <- "foo"
mydades[100,3] <- "bar"
# ... or perhaps wrongly formatted exponential numbers?
mydades[1,1] <- "2.34EXP-05"
# ... or wrong decimal symbol?
mydades[3,3] <- "1,23"
# should be 1.23, as R uses '.' as decimal symbol and not ','
# ... or most likely a whole column is non-numeric, since the error is given twice (as.double problem both in training AND test set)
mydades[,1] <- sample(letters[1:5],100,replace=TRUE)
I would not keep both the numeric data and class labels in a single matrix, perhaps you could split the data as:
mydadesnumeric <- mydades[,1:6] # 6 first columns
mydadesclasses <- mydades[,7]
Using calls
str(mydades); summary(mydades)
may also help you/us in locating the problematic data entries and correct them to numeric entries or omitting non-numeric fields.
The rest of the run code (after breaking the data), as provided by you:
N <- nrow(mydades)
permut <- sample(c(1:N),N,replace=FALSE)
ord <- order(permut)
mydades.shuffled <- mydades[ord,]
prop.train <- 1/3
NOMBRE <- round(prop.train*N)
mydades.training <- mydades.shuffled[1:NOMBRE,]
mydades.test <- mydades.shuffled[(NOMBRE+1):N,]
# 7th column seems to be the class labels
knn(train=mydades.training[,-7],test=mydades.test[,-7],mydades.training[,7],k=5)
Great answer by#Teemu.
As this is a well-read question, I will give the same answer from an analytics perspective.
The KNN function classifies data points by calculating the Euclidean distance between the points. That's a mathematical calculation requiring numbers. All variables in KNN must therefore be coerce-able to numerics.
The data preparation for KNN often involves three tasks:
(1) Fix all NA or "" values
(2) Convert all factors into a set of booleans, one for each level in the factor
(3) Normalize the values of each variable to the range 0:1 so that no variable's range has an unduly large impact on the distance measurement.
I would also point out that the function seems to fail when using integers. I needed to convert everything into "num" type prior to calling the knn function. This includes the target feature, which most methods in R use the factor type. Thus, as.numeric(my_frame$target_feature) is required.

Change stringsAsFactors settings for data.frame

I have a function in which I define a data.frame that I use loops to fill with data. At some point I get the Warning message:
Warning messages:
1: In [<-.factor(*tmp*, iseq, value = "CHANGE") :
invalid factor level, NAs generated
Therefore, when I define my data.frame, I'd like to set the option stringsAsFactors to FALSE but I don't understand how to do it.
I have tried:
DataFrame = data.frame(stringsAsFactors=FALSE)
And also:
options(stringsAsFactors=FALSE)
What is the correct way to set the stringsAsFactors option?
It depends on how you fill your data frame, for which you haven't given any code. When you construct a new data frame, you can do it like this:
x <- data.frame(aName = aVector, bName = bVector, stringsAsFactors = FALSE)
In this case, if e.g. aVector is a character vector, then the dataframe column x$aName will be a character vector as well, and not a factor vector. Combining that with an existing data frame (using rbind, cbind or similar) should preserve that mode.
When you execute
options(stringsAsFactors = FALSE)
you change the global default setting. So every data frame you create after executing that line will not auto-convert to factors unless explicitly told to do so. If you only need to avoid conversion in a single place, then I'd rather not change the default. However if this affects many places in your code, changing the default seems like a good idea.
One more thing: if your vector already contains factors, then neither of the above will change it back into a character vector. To do so, you should explicitly convert it back using as.character or similar.

input 'data' is not double type?

While programming in R, I'm continuosly facing the following error::
Error in data.validity(data, "data") : Bad usage: input 'data' is
not double type.
Can anyone please explain why this error is happening, i.e. the reasons in the dataset which cause the error to arise?
Here is the code I'm running. The packages I have loaded are cluster, psych and clv.
data1 <- read.table(file='dataset.csv', sep=',', header=T, row.names=1)
data1.p <- as.matrix(data1)
hello.data <- data1.p[,1:15]
agnes.mod <- agnes(hello.data)
v.pred <- as.integer(cutree(agnes.mod,3)) # "cut" the tree
scatt <- clv.Scatt(hello.data, v.pred)
Error in data.validity(data, "data") :
Bad usage: input 'data' is not double type.
The key part of data.validity() raising the error is:
data = as.matrix(data)
if( !is.double(data) )
stop(paste("Bad usage: input '", name, "' is not double type.", sep=""))
data is converted to a matrix and then checked if it is a numeric matrix via is.double(). If it isn't numeric the clause is true and the error raised. So why isn't your data (hello.data) numeric when converted to a matrix? Either you have character variables in your data or there are factors. Do you have factors? Try
str(hello.data)
Are there any non-numeric variables in there? If you have character data then get rid of it. If you have factors, then data.validity() could coerce via data.matrix() but as it doesn't, try
hello.data <- data.matrix(hello.data)
after the line creating hello.data then run the rest of your code.
Whether this makes sense (treating a nominal or ordinal variable as a simple numeric) is unclear as you haven't provided a reproducible example or explained what your data are etc.

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